Add content to data analyst roadmap (#6402)
* complete data-analyst content * Apply suggestions from code review Clean uppull/6403/head
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# APIs and Data Collection |
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Application Programming Interfaces, better known as APIs, play a fundamental role in the work of data analysts, particularly in the process of data collection. APIs are sets of protocols, routines, and tools that enable different software applications to communicate with each other. In data analysis, APIs are used extensively to collect, exchange, and manipulate data from different sources in a secure and efficient manner. This data collection process is paramount in shaping the insights derived by the analysts. |
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Application Programming Interfaces, better known as APIs, play a fundamental role in the work of data analysts, particularly in the process of data collection. APIs are sets of protocols, routines, and tools that enable different software applications to communicate with each other. In data analysis, APIs are used extensively to collect, exchange, and manipulate data from different sources in a secure and efficient manner. This data collection process is paramount in shaping the insights derived by the analysts. |
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Learn more from the following resources: |
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- [@article@What is an API?](https://aws.amazon.com/what-is/api/) |
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- [@article@A beginners guide to APIs](https://www.postman.com/what-is-an-api/) |
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# Average |
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The average, also often referred to as the mean, is one of the most commonly used mathematical calculations in data analysis. It provides a simple, useful measure of a set of data. For a data analyst, understanding how to calculate and interpret averages is fundamental. Basic functions, including the average, are integral components in data analysis that are used to summarize and understand complex data sets. Though conceptually simple, the power of average lies in its utility in a range of analyses - from forecasting models to understanding trends and patterns in the dataset. |
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The average, also often referred to as the mean, is one of the most commonly used mathematical calculations in data analysis. It provides a simple, useful measure of a set of data. For a data analyst, understanding how to calculate and interpret averages is fundamental. Basic functions, including the average, are integral components in data analysis that are used to summarize and understand complex data sets. Though conceptually simple, the power of average lies in its utility in a range of analyses - from forecasting models to understanding trends and patterns in the dataset. |
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Learn more from the following resources: |
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- [@article@AVERAGE Function](https://support.microsoft.com/en-gb/office/average-function-047bac88-d466-426c-a32b-8f33eb960cf6) |
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- [@article@Excel AVERAGE function](https://www.w3schools.com/excel/excel_average.php) |
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# Bar Charts in Data Visualization |
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As a vital tool in the data analyst's arsenal, bar charts are essential for analyzing and interpreting complex data. Bar charts, otherwise known as bar graphs, are frequently used graphical displays for dealing with categorical data groups or discrete variables. With their stark visual contrast and definitive measurements, they provide a simple yet effective means of identifying trends, understanding data distribution, and making data-driven decisions. By analyzing the lengths or heights of different bars, data analysts can effectively compare categories or variables against each other and derive meaningful insights effectively. Simplicity, readability, and easy interpretation are key features that make bar charts a favorite in the world of data analytics. |
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As a vital tool in the data analyst's arsenal, bar charts are essential for analyzing and interpreting complex data. Bar charts, otherwise known as bar graphs, are frequently used graphical displays for dealing with categorical data groups or discrete variables. With their stark visual contrast and definitive measurements, they provide a simple yet effective means of identifying trends, understanding data distribution, and making data-driven decisions. By analyzing the lengths or heights of different bars, data analysts can effectively compare categories or variables against each other and derive meaningful insights effectively. Simplicity, readability, and easy interpretation are key features that make bar charts a favorite in the world of data analytics. |
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Learn more from the following resources: |
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- [@article@A complete guide to bar charts](https://www.atlassian.com/data/charts/bar-chart-complete-guide) |
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- [@video@What is a bar chart?](https://www.youtube.com/watch?v=WTVdncVCvKo) |
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# Big Data Concepts |
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Big data refers to extremely large and complex data sets that traditional data processing systems are unable to manage effectively. For data analysts, understanding the big data concepts is crucial as it helps them gain insights, make decisions, and create meaningful presentations using these data sets. The key concepts include volume, velocity, and variety - collectively known as the 3Vs. Volume refers to the amount of data, velocity is the speed at which data is processed, and variety indicates the different types of data being dealt with. Other advanced concepts include variability and veracity. These concepts provide a framework for understanding and working with big data for data analysts. With the growing importance of big data in various industries and sectors, a comprehensive grasp of these concepts equips a data analyst to more effectively and efficiently analyze and interpret complex data sets. |
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Big data refers to extremely large and complex data sets that traditional data processing systems are unable to manage effectively. For data analysts, understanding the big data concepts is crucial as it helps them gain insights, make decisions, and create meaningful presentations using these data sets. The key concepts include volume, velocity, and variety - collectively known as the 3Vs. Volume refers to the amount of data, velocity is the speed at which data is processed, and variety indicates the different types of data being dealt with. Other advanced concepts include variability and veracity. These concepts provide a framework for understanding and working with big data for data analysts. With the growing importance of big data in various industries and sectors, a comprehensive grasp of these concepts equips a data analyst to more effectively and efficiently analyze and interpret complex data sets. |
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Learn more from the following resources: |
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- [@article@An Introduction to Big Data Concepts and Terminology](https://www.digitalocean.com/community/tutorials/an-introduction-to-big-data-concepts-and-terminology) |
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- [@article@An Introduction to Big Data Concepts](https://www.suse.com/c/rancher_blog/an-introduction-to-big-data-concepts/) |
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# Central Tendency |
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Descriptive analysis is a significant branch in the field of data analytics, and under this, the concept of Central Tendency plays a vital role. As data analysts, understanding central tendency is of paramount importance as it offers a quick summary of the data. It provides information about the center point around which the numerical data is distributed. The three major types of the central tendency include the Mean, Median, and Mode. These measures are used by data analysts to identify trends, make comparisons, or draw conclusions. Therefore, an understanding of central tendency equips data analysts with essential tools for interpreting and making sense of statistical data. |
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Descriptive analysis is a significant branch in the field of data analytics, and under this, the concept of Central Tendency plays a vital role. As data analysts, understanding central tendency is of paramount importance as it offers a quick summary of the data. It provides information about the center point around which the numerical data is distributed. The three major types of the central tendency include the Mean, Median, and Mode. These measures are used by data analysts to identify trends, make comparisons, or draw conclusions. Therefore, an understanding of central tendency equips data analysts with essential tools for interpreting and making sense of statistical data. |
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Learn more from the following resources: |
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- [@article@Measures of central tendency](https://www.abs.gov.au/statistics/understanding-statistics/statistical-terms-and-concepts/measures-central-tendency) |
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- [@video@Understanding Central Tendency](https://www.youtube.com/watch?v=n_sSVhHBdj4) |
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# Charting |
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Excel serves as a powerful tool for data analysts when it comes to data organization, manipulation, recovery, and visualization. One of the incredible features it offers is 'Charting'. Charting essentially means creating visual representations of data, which aids data analysts to easily understand complex data and showcase compelling stories of data trends, correlations, and statistical analysis. These charts vary from simple bar graphs to more complex 3D surface and stock charts. As a data analyst, mastering charting under Excel substantially enhances data interpretation, making it easier to extract meaningful insights from substantial data sets. |
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Excel serves as a powerful tool for data analysts when it comes to data organization, manipulation, recovery, and visualization. One of the incredible features it offers is 'Charting'. Charting essentially means creating visual representations of data, which aids data analysts to easily understand complex data and showcase compelling stories of data trends, correlations, and statistical analysis. These charts vary from simple bar graphs to more complex 3D surface and stock charts. As a data analyst, mastering charting under Excel substantially enhances data interpretation, making it easier to extract meaningful insights from substantial data sets. |
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Learn more from the following resources: |
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- [@video@Excel Charts and Graphs Tutorial](https://www.youtube.com/watch?v=eHtZrIb0oWY) |
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- [@article@Create a chart from start to finish](https://support.microsoft.com/en-gb/office/create-a-chart-from-start-to-finish-0baf399e-dd61-4e18-8a73-b3fd5d5680c2) |
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# Cleanup |
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The Cleanup of Data is a critical component of a Data Analyst's role. It involves the process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision making. This process is crucial for Data Analysts to generate accurate and significant insights from data, ultimately resulting in better and more informed business decisions. A solid understanding of data cleanup procedures and techniques is a fundamental skill for any Data Analyst. Hence, it is necessary to hold a high emphasis on maintaining data quality by managing data integrity, accuracy, and consistency during the data cleanup process. |
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The Cleanup of Data is a critical component of a Data Analyst's role. It involves the process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision making. This process is crucial for Data Analysts to generate accurate and significant insights from data, ultimately resulting in better and more informed business decisions. A solid understanding of data cleanup procedures and techniques is a fundamental skill for any Data Analyst. Hence, it is necessary to hold a high emphasis on maintaining data quality by managing data integrity, accuracy, and consistency during the data cleanup process. |
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Learn more from the following resources: |
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- [@article@Top 10 ways to clean your data](https://support.microsoft.com/en-gb/office/top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19) |
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- [@video@Master Data Cleaning Essentials on Excel in Just 10 Minutes](https://www.youtube.com/watch?v=jxq4-KSB_OA) |
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# CNNs |
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Convolutional Neural Networks (CNNs) form an integral part of deep learning frameworks, particularly within the realm of image processing. Data analysts with a focus on deep learning applications often turn to CNNs for their capacity to efficiently process high-dimensional data, such as images, and extract critical features relevant to the problem at hand. As a powerful tool for modeling patterns in data, CNNs are frequently employed in applications ranging from image recognition to natural language processing (NLP). Understanding CNNs, therefore, provides a robust foundation for data analysts aspiring to harness the potential of deep learning techniques. |
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Convolutional Neural Networks (CNNs) form an integral part of deep learning frameworks, particularly within the realm of image processing. Data analysts with a focus on deep learning applications often turn to CNNs for their capacity to efficiently process high-dimensional data, such as images, and extract critical features relevant to the problem at hand. As a powerful tool for modeling patterns in data, CNNs are frequently employed in applications ranging from image recognition to natural language processing (NLP). Understanding CNNs, therefore, provides a robust foundation for data analysts aspiring to harness the potential of deep learning techniques. |
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Learn more from the following resources: |
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- [@article@What are convolutional neural networks?](https://www.ibm.com/topics/convolutional-neural-networks) |
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- [@video@What are Convolutional Neural Networks (CNNs)?](https://www.youtube.com/watch?v=QzY57FaENXg) |
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# Data Collection |
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In the realm of data analysis, the concept of collection holds immense importance. As the term suggests, collection refers to the process of gathering and measuring information on targeted variables in an established systematic fashion that enables a data analyst to answer relevant questions and evaluate outcomes. This step is foundational to any data analysis scheme, as it is the first line of interaction with the raw data that later transforms into viable insights. The effectiveness of data analysis is heavily reliant on the quality and quantity of data collected. Different methodologies and tools are employed for data collection depending on the nature of the data needed, such as surveys, observations, experiments, or scraping online data stores. This process should be carried out with clear objectives and careful consideration to ensure accuracy and relevance in the later stages of analysis and decision-making. |
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In the realm of data analysis, the concept of collection holds immense importance. As the term suggests, collection refers to the process of gathering and measuring information on targeted variables in an established systematic fashion that enables a data analyst to answer relevant questions and evaluate outcomes. This step is foundational to any data analysis scheme, as it is the first line of interaction with the raw data that later transforms into viable insights. The effectiveness of data analysis is heavily reliant on the quality and quantity of data collected. Different methodologies and tools are employed for data collection depending on the nature of the data needed, such as surveys, observations, experiments, or scraping online data stores. This process should be carried out with clear objectives and careful consideration to ensure accuracy and relevance in the later stages of analysis and decision-making. |
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Learn more from the following resources: |
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- [@article@Data Collection Methods](https://www.questionpro.com/blog/data-collection-methods/) |
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- [@article@What is data collection?](https://www.simplilearn.com/what-is-data-collection-article) |
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# Concatenation |
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The term 'Concat' or ‘Concatenation’ refers to the operation of combining two or more data structures, be it strings, arrays, or datasets, end-to-end in a sequence. In the context of data analysis, a Data Analyst uses concatenation as a basic function to merge or bind data sets along an axis - either vertically or horizontally. This function is commonly used in data wrangling or preprocessing to combine data from multiple sources, handle missing values, and shape data into a form that fits better with analysis tools. An understanding of 'Concat' plays a crucial role in managing the complex, large data sets that data analysts often work with. |
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The term 'Concat' or ‘Concatenation’ refers to the operation of combining two or more data structures, be it strings, arrays, or datasets, end-to-end in a sequence. In the context of data analysis, a Data Analyst uses concatenation as a basic function to merge or bind data sets along an axis - either vertically or horizontally. This function is commonly used in data wrangling or preprocessing to combine data from multiple sources, handle missing values, and shape data into a form that fits better with analysis tools. An understanding of 'Concat' plays a crucial role in managing the complex, large data sets that data analysts often work with. |
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Learn more from the following resources: |
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- [@article@CONCAT Function](https://support.microsoft.com/en-gb/office/concat-function-9b1a9a3f-94ff-41af-9736-694cbd6b4ca2) |
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- [@article@Excel CONCAT Function](https://www.w3schools.com/excel/excel_concat.php) |
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# Count |
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The Count function in data analysis is one of the most fundamental tasks that a Data Analyst gets to handle. This function is a simple yet powerful tool that aids in understanding the underlying data by providing the count or frequency of occurrences of unique elements in data sets. The relevance of count comes into play in various scenarios – from understanding the popularity of a certain category to analyzing customer activity, and much more. This basic function offers crucial insights into data, making it an essential skill in the toolkit of any data analyst. |
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The Count function in data analysis is one of the most fundamental tasks that a Data Analyst gets to handle. This function is a simple yet powerful tool that aids in understanding the underlying data by providing the count or frequency of occurrences of unique elements in data sets. The relevance of count comes into play in various scenarios – from understanding the popularity of a certain category to analyzing customer activity, and much more. This basic function offers crucial insights into data, making it an essential skill in the toolkit of any data analyst. |
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Learn more from the following resources: |
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- [@article@COUNT Function](https://support.microsoft.com/en-gb/office/count-function-a59cd7fc-b623-4d93-87a4-d23bf411294c) |
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- [@video@How to Count Cells in Microsoft Excel (COUNT, COUNTA, COUNTIF, COUNTIFS Functions)](https://www.youtube.com/watch?v=5RFLncJuMng) |
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# CSV Files in Data Collection for Data Analysts |
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CSV or Comma Separated Values files play an integral role in data collection for data analysts. These file types allow the efficient storage of data and are commonly generated by spreadsheet software like Microsoft Excel or Google Sheets, but their simplicity makes them compatible with a variety of applications that deal with data. In the context of data analysis, CSV files are extensively used to import and export large datasets, making them essential for any data analyst's toolkit. They allow analysts to organize vast amounts of information into a structured format, which is fundamental in extracting useful insights from raw data. |
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CSV or Comma Separated Values files play an integral role in data collection for data analysts. These file types allow the efficient storage of data and are commonly generated by spreadsheet software like Microsoft Excel or Google Sheets, but their simplicity makes them compatible with a variety of applications that deal with data. In the context of data analysis, CSV files are extensively used to import and export large datasets, making them essential for any data analyst's toolkit. They allow analysts to organize vast amounts of information into a structured format, which is fundamental in extracting useful insights from raw data. |
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Learn more from the following resources: |
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- [@video@Understanding CSV Files](https://www.youtube.com/watch?v=UofTplCVkYI) |
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- [@article@What is a CSV file: A comprehensive guide](https://flatfile.com/blog/what-is-a-csv-file-guide-to-uses-and-benefits/) |
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# Data Manipulation Libraries |
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# Data Manipulation Libraries |
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Data manipulation libraries are essential tools in data science and analytics, enabling efficient handling, transformation, and analysis of large datasets. Python, a popular language for data science, offers several powerful libraries for this purpose. Pandas is a highly versatile library that provides data structures like DataFrames, which allow for easy manipulation and analysis of tabular data. NumPy, another fundamental library, offers support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. Together, Pandas and NumPy form the backbone of data manipulation in Python, facilitating tasks such as data cleaning, merging, reshaping, and statistical analysis, thus streamlining the data preparation process for machine learning and other data-driven applications. |
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# Data Visualisation Libraries |
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# Data Visualisation Libraries |
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Data visualization libraries are crucial in data science for transforming complex datasets into clear and interpretable visual representations, facilitating better understanding and communication of data insights. In Python, several libraries are widely used for this purpose. Matplotlib is a foundational library that offers comprehensive tools for creating static, animated, and interactive plots. Seaborn, built on top of Matplotlib, provides a high-level interface for drawing attractive and informative statistical graphics with minimal code. Plotly is another powerful library that allows for the creation of interactive and dynamic visualizations, which can be easily embedded in web applications. Additionally, libraries like Bokeh and Altair offer capabilities for creating interactive plots and dashboards, enhancing exploratory data analysis and the presentation of data findings. Together, these libraries enable data scientists to effectively visualize trends, patterns, and outliers in their data, making the analysis more accessible and actionable. |
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# Databases |
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Behind every strong data analyst, there's not just a rich assortment of data, but a set of robust databases that enable effective data collection. Databases are a fundamental aspect of data collection in a world where the capability to manage, organize, and evaluate large volumes of data is critical. As a data analyst, the understanding and use of databases is instrumental in capturing the necessary data for conducting qualitative and quantitative analysis, forecasting trends and making data-driven decisions. Thorough knowledge of databases, therefore, can be considered a key component of a data analyst's arsenal. These databases can vary from relational databases like SQL to NoSQL databases like MongoDB, each serving a unique role in the data collection process. |
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Behind every strong data analyst, there's not just a rich assortment of data, but a set of robust databases that enable effective data collection. Databases are a fundamental aspect of data collection in a world where the capability to manage, organize, and evaluate large volumes of data is critical. As a data analyst, the understanding and use of databases is instrumental in capturing the necessary data for conducting qualitative and quantitative analysis, forecasting trends and making data-driven decisions. Thorough knowledge of databases, therefore, can be considered a key component of a data analyst's arsenal. These databases can vary from relational databases like SQL to NoSQL databases like MongoDB, each serving a unique role in the data collection process. |
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Learn more from the following resources: |
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- [@official@PostgreSQL Roadmap](https://roadmap.sh/postgresql-dba) |
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- [@official@MongoDB Roadmap](https://roadmap.sh/mongodb) |
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# Decision Trees |
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As a data analyst, understanding machine learning topics like decision trees is crucial. Decision trees are a fundamental aspect in the field of machine learning and artificial intelligence. They present a simple yet effective method of data analysis. They have applications in several areas including customer relationship management, fraud detection, financial analysis, healthcare and more. In simpler terms, a decision tree can be considered as a method of breaking down complex decisions and estimating likely outcomes. This introduction would help data analysts understand the logic behind decision trees and how they are constructed for the purpose of predictive modeling. |
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As a data analyst, understanding machine learning topics like decision trees is crucial. Decision trees are a fundamental aspect in the field of machine learning and artificial intelligence. They present a simple yet effective method of data analysis. They have applications in several areas including customer relationship management, fraud detection, financial analysis, healthcare and more. In simpler terms, a decision tree can be considered as a method of breaking down complex decisions and estimating likely outcomes. This introduction would help data analysts understand the logic behind decision trees and how they are constructed for the purpose of predictive modeling. |
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Learn more from the following resources: |
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- [@article@What is machine learning for analytics?](https://www.oracle.com/business-analytics/what-is-machine-learning-for-analytics/) |
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- [@article@The Role of Machine Learning in Data Analysis](https://www.ironhack.com/gb/blog/the-role-of-machine-learning-in-data-analysis) |
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# Descriptive Analysis |
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In the realm of data analytics, descriptive analysis plays an imperative role as a fundamental step in data interpretation. Essentially, descriptive analysis encompasses the process of summarizing, organizing, and simplifying complex data into understandable and interpretable forms. This method entails the use of various statistical tools to depict patterns, correlations, and trends in a data set. For data analysts, it serves as the cornerstone for in-depth data exploration, providing the groundwork upon which further analysis techniques such as predictive and prescriptive analysis are built. |
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In the realm of data analytics, descriptive analysis plays an imperative role as a fundamental step in data interpretation. Essentially, descriptive analysis encompasses the process of summarizing, organizing, and simplifying complex data into understandable and interpretable forms. This method entails the use of various statistical tools to depict patterns, correlations, and trends in a data set. For data analysts, it serves as the cornerstone for in-depth data exploration, providing the groundwork upon which further analysis techniques such as predictive and prescriptive analysis are built. |
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Learn more from the following resources: |
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- [@article@Descriptive Analytics: What They Are and Related Terms](https://www.investopedia.com/terms/d/descriptive-analytics.asp) |
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- [@video@What are Descriptive Analytics?](https://www.youtube.com/watch?v=DlFqQy10aCs) |
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# Descriptive Analytics |
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Descriptive Analytics is one of the fundamental types of Data Analytics that provides insight into the past. As a Data Analyst, utilizing Descriptive Analytics involves the technique of using historical data to understand changes that have occurred in a business over time. Primarily concerned with the “what has happened” aspect, it analyzes raw data from the past to draw inferences and identify patterns and trends. This helps companies understand their strengths, weaknesses and pinpoint operational problems, setting the stage for accurate Business Intelligence and decision-making processes. |
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Descriptive Analytics is one of the fundamental types of Data Analytics that provides insight into the past. As a Data Analyst, utilizing Descriptive Analytics involves the technique of using historical data to understand changes that have occurred in a business over time. Primarily concerned with the “what has happened” aspect, it analyzes raw data from the past to draw inferences and identify patterns and trends. This helps companies understand their strengths, weaknesses and pinpoint operational problems, setting the stage for accurate Business Intelligence and decision-making processes. |
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Learn more from the following resources: |
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- [@article@Descriptive Analytics: What They Are and Related Terms](https://www.investopedia.com/terms/d/descriptive-analytics.asp) |
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- [@video@What are Descriptive Analytics?](https://www.youtube.com/watch?v=DlFqQy10aCs) |
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# Diagnostic Analytics |
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Diagnostic analytics, as a crucial type of data analytics, is focused on studying past performance to understand why something happened. This is an integral part of the work done by data analysts. Through techniques such as drill-down, data discovery, correlations, and cause-effect analysis, data analysts utilizing diagnostic analytics can look beyond general trends and identify the root cause of changes observed in the data. Consequently, this enables businesses to address operational and strategic issues effectively, by allowing them to grasp the reasons behind such issues. For every data analyst, the skill of performing diagnostic data analytics is a must-have asset that enhances their analysis capability. |
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Diagnostic analytics, as a crucial type of data analytics, is focused on studying past performance to understand why something happened. This is an integral part of the work done by data analysts. Through techniques such as drill-down, data discovery, correlations, and cause-effect analysis, data analysts utilizing diagnostic analytics can look beyond general trends and identify the root cause of changes observed in the data. Consequently, this enables businesses to address operational and strategic issues effectively, by allowing them to grasp the reasons behind such issues. For every data analyst, the skill of performing diagnostic data analytics is a must-have asset that enhances their analysis capability. |
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Learn more from the following resources: |
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- [@video@What is Diagnostic Analytics? | Understanding Data-Driven Decision Making](https://www.youtube.com/watch?v=ikZjeAC1yJ0) |
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- [@article@What is Diagnostic Analytics?](https://amplitude.com/explore/analytics/what-diagnostic-analytics) |
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# Distribution Shape |
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In the realm of Data Analysis, the distribution shape is considered as an essential component under descriptive analysis. A data analyst uses the shape of the distribution to understand the spread and trend of the data set. It aids in identifying the skewness (asymmetry) and kurtosis (the 'tailedness') of the data and helps to reveal meaningful patterns that standard statistical measures like mean or median might not capture. The distribution shape can provide insights into data’s normality and variability, informing decisions about which statistical methods are appropriate for further analysis. |
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In the realm of Data Analysis, the distribution shape is considered as an essential component under descriptive analysis. A data analyst uses the shape of the distribution to understand the spread and trend of the data set. It aids in identifying the skewness (asymmetry) and kurtosis (the 'tailedness') of the data and helps to reveal meaningful patterns that standard statistical measures like mean or median might not capture. The distribution shape can provide insights into data’s normality and variability, informing decisions about which statistical methods are appropriate for further analysis. |
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Learn more from the following resources: |
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- [@article@Shapes of Distributions: Definitions, Examples](https://www.statisticshowto.com/shapes-of-distributions/) |
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- [@course@Shapes of distributions](https://online.stat.psu.edu/stat414/lesson/13/13.5) |
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# Data Cleaning with dplyr |
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Data cleaning plays a crucial role in the data analysis pipeline, where it rectifies and enhances the quality of data to increase the efficiency and authenticity of the analytical process. The `dplyr` package, an integral part of the `tidyverse` suite in R, has become a staple in the toolkit of data analysts dealing with data cleaning. `dplyr` offers a coherent set of verbs that significantly simplifies the process of manipulating data structures, such as dataframes and databases. This involves selecting, sorting, filtering, creating or modifying variables, and aggregating records, among other operations. Incorporating `dplyr` into the data cleaning phase enables data analysts to perform operations more effectively, improve code readability, and handle large and complex data with ease. |
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Data cleaning plays a crucial role in the data analysis pipeline, where it rectifies and enhances the quality of data to increase the efficiency and authenticity of the analytical process. The `dplyr` package, an integral part of the `tidyverse` suite in R, has become a staple in the toolkit of data analysts dealing with data cleaning. `dplyr` offers a coherent set of verbs that significantly simplifies the process of manipulating data structures, such as dataframes and databases. This involves selecting, sorting, filtering, creating or modifying variables, and aggregating records, among other operations. Incorporating `dplyr` into the data cleaning phase enables data analysts to perform operations more effectively, improve code readability, and handle large and complex data with ease. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@dplyr website](https://dplyr.tidyverse.org/) |
||||
- [@video@Dplyr Essentials](https://www.youtube.com/watch?v=Gvhkp-Yw65U) |
||||
@ -1,3 +1,8 @@ |
||||
# Dplyr |
||||
|
||||
Dplyr is a powerful and popular toolkit for data manipulation in R. As a data analyst, this library provides integral functions to manipulate, clean, and process data efficiently. It has been designed to be easy and intuitive, ensuring a robust and consistent syntax. Dplyr ensures data reliability and fast processing, essential for analysts dealing with large datasets. With a strong focus on efficiency, dplyr functions like select, filter, arrange, mutate, summarise, and group_by optimise data analysis operations, making data manipulation a smoother and hassle-free procedure for data analysts. |
||||
Dplyr is a powerful and popular toolkit for data manipulation in R. As a data analyst, this library provides integral functions to manipulate, clean, and process data efficiently. It has been designed to be easy and intuitive, ensuring a robust and consistent syntax. Dplyr ensures data reliability and fast processing, essential for analysts dealing with large datasets. With a strong focus on efficiency, dplyr functions like select, filter, arrange, mutate, summarise, and group_by optimise data analysis operations, making data manipulation a smoother and hassle-free procedure for data analysts. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@dplyr website](https://dplyr.tidyverse.org/) |
||||
- [@video@Dplyr Essentials](https://www.youtube.com/watch?v=Gvhkp-Yw65U) |
||||
@ -1,3 +1,8 @@ |
||||
# Exploration |
||||
|
||||
In the realm of data analytics, exploration of data is a key concept that data analysts leverage to understand and interpret data effectively. Typically, this exploration process involves discerning patterns, identifying anomalies, examining underlying structures, and testing hypothesis, which often gets accomplished via descriptive statistics, visual methods, or sophisticated algorithms. It's a fundamental stepping-stone for any data analyst, ultimately guiding them in shaping the direction of further analysis or modeling. This concept serves as a foundation for dealing with complexities and uncertainties in data, hence improving decision-making in various fields ranging from business and finance to healthcare and social sciences. |
||||
In the realm of data analytics, exploration of data is a key concept that data analysts leverage to understand and interpret data effectively. Typically, this exploration process involves discerning patterns, identifying anomalies, examining underlying structures, and testing hypothesis, which often gets accomplished via descriptive statistics, visual methods, or sophisticated algorithms. It's a fundamental stepping-stone for any data analyst, ultimately guiding them in shaping the direction of further analysis or modeling. This concept serves as a foundation for dealing with complexities and uncertainties in data, hence improving decision-making in various fields ranging from business and finance to healthcare and social sciences. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@How to do Data Exploration](https://www.youtube.com/watch?v=OY4eQrekQvs) |
||||
- [@article@What is data exploration](https://www.heavy.ai/learn/data-exploration) |
||||
@ -1,3 +1,8 @@ |
||||
# Funnel Chart in Data Visualization |
||||
|
||||
A funnel chart is an important tool for Data Analysts. It is a part of data visualization, the creation and study of the visual representation of data. A funnel chart displays values as progressively diminishing amounts, allowing data analysts to understand the stages that contribute to the output of a process or system. It is often used in sales, marketing or any field that involves a multi-step process, to evaluate efficiency or identify potential problem areas. The 'funnel' shape is symbolic of a typical customer conversion process, going from initial engagement to close of sale. As Data Analysts, understanding and interpreting funnel charts can provide significant insights to drive optimal decision making. |
||||
A funnel chart is an important tool for Data Analysts. It is a part of data visualization, the creation and study of the visual representation of data. A funnel chart displays values as progressively diminishing amounts, allowing data analysts to understand the stages that contribute to the output of a process or system. It is often used in sales, marketing or any field that involves a multi-step process, to evaluate efficiency or identify potential problem areas. The 'funnel' shape is symbolic of a typical customer conversion process, going from initial engagement to close of sale. As Data Analysts, understanding and interpreting funnel charts can provide significant insights to drive optimal decision making. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is a Funnel Chart?](https://www.atlassian.com/data/charts/funnel-chart-complete-guide) |
||||
- [@video@Explain your data with a funnel chart](https://www.youtube.com/watch?v=AwFB9Qg96Ek) |
||||
@ -1,3 +1,8 @@ |
||||
# ggplot2 |
||||
|
||||
When it comes to data visualization in R programming, ggplot2 stands tall as one of the primary tools for data analysts. This data visualization library, which forms part of the tidyverse suite of packages, facilitates the creation of complex and sophisticated visual narratives. With its grammar of graphics philosophy, ggplot2 enables analysts to build graphs and charts layer by layer, thereby offering detailed control over graphical features and design. Its versatility in creating tailored and aesthetically pleasing graphics is a vital asset for any data analyst tackling exploratory data analysis, reporting, or dashboard building. |
||||
When it comes to data visualization in R programming, ggplot2 stands tall as one of the primary tools for data analysts. This data visualization library, which forms part of the tidyverse suite of packages, facilitates the creation of complex and sophisticated visual narratives. With its grammar of graphics philosophy, ggplot2 enables analysts to build graphs and charts layer by layer, thereby offering detailed control over graphical features and design. Its versatility in creating tailored and aesthetically pleasing graphics is a vital asset for any data analyst tackling exploratory data analysis, reporting, or dashboard building. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@ggplot2 website] |
||||
- [@video@Make beautiful graphs in R](https://www.youtube.com/watch?v=qnw1xDnt_Ec) |
||||
@ -1,3 +1,8 @@ |
||||
# Hadoop |
||||
|
||||
Hadoop is a critical element in the realm of data processing frameworks, offering an effective solution for storing, managing, and analyzing massive amounts of data. Unraveling meaningful insights from a large deluge of data is a challenging pursuit faced by many data analysts. Regular data processing tools fail to handle large-scale data, paving the way for advanced frameworks like Hadoop. This open-source platform by Apache Software Foundation excels at storing and processing vast data across clusters of computers. Notably, Hadoop comprises two key modules - the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. Hadoop’s ability to handle both structured and unstructured data further broadens its capacity. For any data analyst, a thorough understanding of Hadoop can unlock powerful ways to manage data effectively and construct meaningful analytics. |
||||
Hadoop is a critical element in the realm of data processing frameworks, offering an effective solution for storing, managing, and analyzing massive amounts of data. Unraveling meaningful insights from a large deluge of data is a challenging pursuit faced by many data analysts. Regular data processing tools fail to handle large-scale data, paving the way for advanced frameworks like Hadoop. This open-source platform by Apache Software Foundation excels at storing and processing vast data across clusters of computers. Notably, Hadoop comprises two key modules - the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. Hadoop’s ability to handle both structured and unstructured data further broadens its capacity. For any data analyst, a thorough understanding of Hadoop can unlock powerful ways to manage data effectively and construct meaningful analytics. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Apache Hadoop Website](https://hadoop.apache.org/) |
||||
- [@article@What Is Hadoop?](https://www.databricks.com/glossary/hadoop) |
||||
@ -1,3 +1,8 @@ |
||||
# Heatmap |
||||
|
||||
Heatmaps are a crucial component of data visualization that Data Analysts regularly employ in their analyses. As one of many possible graphical representations of data, heatmaps show the correlation or scale of variation between two or more variables in a dataset, making them extremely useful for pattern recognition and outlier detection. Individual values within a matrix are represented in a heatmap as colors, with differing intensities indicating the degree or strength of an occurrence. In short, a Data Analyst would use a heatmap to decode complex multivariate data and turn it into an easily understandable visual that aids in decision making. |
||||
Heatmaps are a crucial component of data visualization that Data Analysts regularly employ in their analyses. As one of many possible graphical representations of data, heatmaps show the correlation or scale of variation between two or more variables in a dataset, making them extremely useful for pattern recognition and outlier detection. Individual values within a matrix are represented in a heatmap as colors, with differing intensities indicating the degree or strength of an occurrence. In short, a Data Analyst would use a heatmap to decode complex multivariate data and turn it into an easily understandable visual that aids in decision making. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@A complete guide to heatmaps](https://www.hotjar.com/heatmaps/) |
||||
- [@article@What is a heatmap?](https://www.atlassian.com/data/charts/heatmap-complete-guide) |
||||
@ -1,3 +1,8 @@ |
||||
# Histograms |
||||
|
||||
As a Data Analyst, understanding and representing complex data in a simplified and comprehensible form is of paramount importance. This is where the concept of data visualization comes into play, specifically the use of histograms. A histogram is a graphical representation that organizes a group of data points into a specified range. It provides an visual interpretation of numerical data by indicating the number of data points that fall within a specified range of values, known as bins. This highly effective tool allows data analysts to view data distribution over a continuous interval or a certain time period, which can further aid in identifying trends, outliers, patterns, or anomalies present in the data. Consequently, histograms are instrumental in making informed business decisions based on these data interpretations. |
||||
As a Data Analyst, understanding and representing complex data in a simplified and comprehensible form is of paramount importance. This is where the concept of data visualization comes into play, specifically the use of histograms. A histogram is a graphical representation that organizes a group of data points into a specified range. It provides an visual interpretation of numerical data by indicating the number of data points that fall within a specified range of values, known as bins. This highly effective tool allows data analysts to view data distribution over a continuous interval or a certain time period, which can further aid in identifying trends, outliers, patterns, or anomalies present in the data. Consequently, histograms are instrumental in making informed business decisions based on these data interpretations. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@How a histogram works to display data](https://www.investopedia.com/terms/h/histogram.asp) |
||||
- [@article@What is a histogram](https://www.mathsisfun.com/data/histograms.html) |
||||
@ -1,3 +1,8 @@ |
||||
# Hypothesis Testing |
||||
|
||||
In the context of a Data Analyst, hypothesis testing plays an essential role to make inferences or predictions based on data. Hypothesis testing is an approach used to test a claim or theory about a parameter in a population, using data measured in a sample. This method allows Data Analysts to determine whether the observed data deviates significantly from the status quo or not. Essentially, it provides a probability-based mechanism to quantify and deal with the uncertainty inherent in conclusions drawn from not completely reliable data. |
||||
In the context of a Data Analyst, hypothesis testing plays an essential role to make inferences or predictions based on data. Hypothesis testing is an approach used to test a claim or theory about a parameter in a population, using data measured in a sample. This method allows Data Analysts to determine whether the observed data deviates significantly from the status quo or not. Essentially, it provides a probability-based mechanism to quantify and deal with the uncertainty inherent in conclusions drawn from not completely reliable data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Hypothesis Testing](https://latrobe.libguides.com/maths/hypothesis-testing) |
||||
- [@article@Hypothesis Testing - 4 Step](https://www.investopedia.com/terms/h/hypothesistesting.asp) |
||||
@ -1,3 +1,8 @@ |
||||
# If |
||||
|
||||
The IF function in Excel is a crucial tool for data analysts, enabling them to create conditional statements, clean and validate data, perform calculations based on specific conditions, create custom metrics, apply conditional formatting, automate tasks, and generate dynamic reports. Data analysts use IF to categorize data, handle missing values, calculate bonuses or custom metrics, highlight trends, and enhance visualizations, ultimately facilitating informed decision-making through data analysis. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@IF Function](https://support.microsoft.com/en-gb/office/if-function-69aed7c9-4e8a-4755-a9bc-aa8bbff73be2) |
||||
- [@article@Excel IF Function](https://exceljet.net/functions/if-function) |
||||
@ -1,3 +1,8 @@ |
||||
# Image Recognition |
||||
|
||||
Image Recognition has become a significant domain because of its diverse applications, including facial recognition, object detection, character recognition, and much more. As a Data Analyst, understanding Image Recognition under Deep Learning becomes crucial. The data analyst's role in this context involves deciphering complex patterns and extracting valuable information from image data. This area of machine learning combines knowledge of data analysis, image processing, and deep neural networks to provide accurate results, contributing significantly to the progression of fields like autonomous vehicles, medical imaging, surveillance, among others. Therefore, proficiency in this field paves the way for proficient data analysis, leading to innovative solutions and improved decision-making. |
||||
Image Recognition has become a significant domain because of its diverse applications, including facial recognition, object detection, character recognition, and much more. As a Data Analyst, understanding Image Recognition under Deep Learning becomes crucial. The data analyst's role in this context involves deciphering complex patterns and extracting valuable information from image data. This area of machine learning combines knowledge of data analysis, image processing, and deep neural networks to provide accurate results, contributing significantly to the progression of fields like autonomous vehicles, medical imaging, surveillance, among others. Therefore, proficiency in this field paves the way for proficient data analysis, leading to innovative solutions and improved decision-making. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is image recognition?](https://www.techtarget.com/searchenterpriseai/definition/image-recognition) |
||||
- [@article@Image Recognition: Definition, Algorithms & Uses](https://www.v7labs.com/blog/image-recognition-guide) |
||||
@ -1,3 +1,5 @@ |
||||
# Introduction to Data Analysis |
||||
|
||||
Data Analysis plays a crucial role in today's data-centric world. It involves the practice of inspecting, cleansing, transforming, and modeling data to extract valuable insights for decision-making. A **Data Analyst** is a professional primarily tasked with collecting, processing, and performing statistical analysis on large datasets. They discover how data can be used to answer questions and solve problems. With the rapid expansion of data in modern firms, the role of a data analyst has been evolving greatly, making them a significant asset in business strategy and decision-making processes. |
||||
Data Analysis plays a crucial role in today's data-centric world. It involves the practice of inspecting, cleansing, transforming, and modeling data to extract valuable insights for decision-making. A **Data Analyst** is a professional primarily tasked with collecting, processing, and performing statistical analysis on large datasets. They discover how data can be used to answer questions and solve problems. With the rapid expansion of data in modern firms, the role of a data analyst has been evolving greatly, making them a significant asset in business strategy and decision-making processes. |
||||
|
||||
Learn more from the following resources: |
||||
@ -1,3 +1,8 @@ |
||||
# Kmeans |
||||
|
||||
Kmeans is a fundamentally important method in data analysis and falls under the broad umbrella of machine learning basics. A data analyst using Kmeans clusters large data sets into subgroups or clusters based upon specific characteristics or parameters. The primary purpose is to derive insights from similarities/dissimilarities within the dataset, which can then be used for understanding patterns, trends, and predictive modeling. Accurate use of Kmeans can lead to enhanced decision-making, forecasting and strategic planning based on the data. |
||||
Kmeans is a fundamentally important method in data analysis and falls under the broad umbrella of machine learning basics. A data analyst using Kmeans clusters large data sets into subgroups or clusters based upon specific characteristics or parameters. The primary purpose is to derive insights from similarities/dissimilarities within the dataset, which can then be used for understanding patterns, trends, and predictive modeling. Accurate use of Kmeans can lead to enhanced decision-making, forecasting and strategic planning based on the data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@K-Means Clustering](https://en.wikipedia.org/wiki/K-means_clustering) |
||||
- [@article@K-Means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) |
||||
@ -1,3 +1,8 @@ |
||||
# KNN |
||||
|
||||
K-Nearest Neighbors (KNN) is a simple yet powerful algorithm used in the field of machine learning, which a Data Analyst might employ for tasks such as classification or regression. It works based on the principle of proximity, where the prediction of new instance's category depends upon the category of its nearest neighbors. For a Data Analyst working with complex data sets, it's crucial to understand how the KNN algorithm operates, its applicability, pros, and cons. This will facilitate making well-informed decisions about when to utilize it for the best possible outcome in data analysis. |
||||
K-Nearest Neighbors (KNN) is a simple yet powerful algorithm used in the field of machine learning, which a Data Analyst might employ for tasks such as classification or regression. It works based on the principle of proximity, where the prediction of new instance's category depends upon the category of its nearest neighbors. For a Data Analyst working with complex data sets, it's crucial to understand how the KNN algorithm operates, its applicability, pros, and cons. This will facilitate making well-informed decisions about when to utilize it for the best possible outcome in data analysis. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is the k-nearest neighbors (KNN) algorithm?](https://www.ibm.com/topics/knn#:~:text=The%20k%2Dnearest%20neighbors%20(KNN,used%20in%20machine%20learning%20today.) |
||||
- [@article@Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html) |
||||
@ -1,3 +1,8 @@ |
||||
# Line Chart |
||||
|
||||
Data visualization is a crucial skill for every Data Analyst and the Line Chart is one of the most commonly used chart types in this field. Line charts act as powerful tools for summarizing and interpreting complex datasets. Through attractive and interactive design, these charts allow for clear and efficient communication of patterns, trends, and outliers in the data. This makes them valuable for data analysts when presenting data spanning over a period of time, forecasting trends or demonstrating relationships between different data sets. |
||||
Data visualization is a crucial skill for every Data Analyst and the Line Chart is one of the most commonly used chart types in this field. Line charts act as powerful tools for summarizing and interpreting complex datasets. Through attractive and interactive design, these charts allow for clear and efficient communication of patterns, trends, and outliers in the data. This makes them valuable for data analysts when presenting data spanning over a period of time, forecasting trends or demonstrating relationships between different data sets. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Line Graph: Definition, Types, Parts, Uses, and Examples](https://www.investopedia.com/terms/l/line-graph.asp) |
||||
- [@video@What is a line graph?](https://www.youtube.com/watch?v=rw-MxkzymEw) |
||||
@ -1,3 +1,8 @@ |
||||
# Logistic |
||||
|
||||
Logistic Regression is one of the foundational techniques that a data analyst must understand in machine learning. This method is a predictive analysis algorithm based on the concept of probability. It’s used for categorizing data into distinct classes, making it particularly useful for binary classification problems. It should be understood that despite its name, logistic regression is used in classification problems, not regression tasks. Data analysts use this algorithm to build machine learning models to solve various real-world problems such as email spam, credibility of loan applicants, development of marketing strategies and so on. |
||||
Logistic Regression is one of the foundational techniques that a data analyst must understand in machine learning. This method is a predictive analysis algorithm based on the concept of probability. It’s used for categorizing data into distinct classes, making it particularly useful for binary classification problems. It should be understood that despite its name, logistic regression is used in classification problems, not regression tasks. Data analysts use this algorithm to build machine learning models to solve various real-world problems such as email spam, credibility of loan applicants, development of marketing strategies and so on. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Everything you need to know about Logistic Regression](https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-logistic-regression/) |
||||
- [@article@Logistic Regression for Machine Learning](https://machinelearningmastery.com/logistic-regression-for-machine-learning/) |
||||
@ -1,3 +1,8 @@ |
||||
# Machine Learning - A Key Concept for Data Analysts |
||||
|
||||
Machine learning, a subset of artificial intelligence, is an indispensable tool in the hands of a data analyst. It provides the ability to automatically learn, improve from experience and make decisions without being explicitly programmed. In the context of a data analyst, machine learning contributes significantly in uncovering hidden insights, recognising patterns or making predictions based on large amounts of data. Through the use of varying algorithms and models, data analysts are able to leverage machine learning to convert raw data into meaningful information, making it a critical concept in data analysis. |
||||
Machine learning, a subset of artificial intelligence, is an indispensable tool in the hands of a data analyst. It provides the ability to automatically learn, improve from experience and make decisions without being explicitly programmed. In the context of a data analyst, machine learning contributes significantly in uncovering hidden insights, recognising patterns or making predictions based on large amounts of data. Through the use of varying algorithms and models, data analysts are able to leverage machine learning to convert raw data into meaningful information, making it a critical concept in data analysis. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@What is Machine Learning?](https://www.youtube.com/watch?v=9gGnTQTYNaE) |
||||
- [@article@What is Machine Learning (ML)?](https://www.ibm.com/topics/machine-learning) |
||||
@ -1,3 +1,8 @@ |
||||
# Map Reduce |
||||
# MapReduce |
||||
|
||||
Map Reduce is a prominent data processing technique used by Data Analysts around the world. It allows them to handle large data sets with complex, unstructured data efficiently. Map Reduce breaks down a big data problem into smaller sub-tasks (Map) and then takes those results to create an output in a more usable format (Reduce). This technique is particularly useful in conducting exploratory analysis, as well as in handling big data operations such as text processing, graph processing, or more complicated machine learning algorithms. |
||||
MapReduce is a prominent data processing technique used by Data Analysts around the world. It allows them to handle large data sets with complex, unstructured data efficiently. MapReduce breaks down a big data problem into smaller sub-tasks (Map) and then takes those results to create an output in a more usable format (Reduce). This technique is particularly useful in conducting exploratory analysis, as well as in handling big data operations such as text processing, graph processing, or more complicated machine learning algorithms. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@MapReduce](https://www.databricks.com/glossary/mapreduce) |
||||
- [@article@What is Apache MapReduce?](https://www.ibm.com/topics/mapreduce) |
||||
@ -1,3 +1,8 @@ |
||||
# Matplotlib |
||||
|
||||
Matplotlib is a paramount data visualization library used extensively by data analysts for generating a wide array of plots and graphs. Through Matplotlib, data analysts can convey results clearly and effectively, driving insights from complex data sets. It offers a hierarchical environment which is very natural for a data scientist to work with. Providing an object-oriented API, it allows for extensive customization and integration into larger applications. From histograms, bar charts, scatter plots to 3D graphs, the versatility of Matplotlib assists data analysts in the better comprehension and compelling representation of data. |
||||
Matplotlib is a paramount data visualization library used extensively by data analysts for generating a wide array of plots and graphs. Through Matplotlib, data analysts can convey results clearly and effectively, driving insights from complex data sets. It offers a hierarchical environment which is very natural for a data scientist to work with. Providing an object-oriented API, it allows for extensive customization and integration into larger applications. From histograms, bar charts, scatter plots to 3D graphs, the versatility of Matplotlib assists data analysts in the better comprehension and compelling representation of data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@Learn Matplotlib in 6 minutes](https://www.youtube.com/watch?v=nzKy9GY12yo) |
||||
- [@article@Matplotlib Website](https://matplotlib.org/) |
||||
@ -1,3 +1,8 @@ |
||||
# Mean |
||||
|
||||
Central tendency refers to the statistical measure that identifies a single value as representative of an entire distribution. The mean or average is one of the most popular and widely used measures of central tendency. For a data analyst, calculating the mean is a routine task. This single value provides an analyst with a quick snapshot of the data and could be useful for further data manipulation or statistical analysis. Mean is particularly helpful in predicting trends and patterns within voluminous data sets or adjusting influencing factors that may distort the 'true' representation of the data. It is the arithmetic average of a range of values or quantities, computed as the total sum of all the values divided by the total number of values. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Measures of Central Tendency](https://statistics.laerd.com/statistical-guides/measures-central-tendency-mean-mode-median.php) |
||||
- [@article@Central Tendency | Understanding the Mean, Median & Mode](https://www.scribbr.co.uk/stats/measures-of-central-tendency/) |
||||
@ -1,3 +1,8 @@ |
||||
# Median |
||||
|
||||
Median signifies the middle value in a data set when arranged in ascending or descending order. As a data analyst, understanding, calculating, and interpreting the median is crucial. It is especially helpful when dealing with outliers in a dataset as the median is less sensitive to extreme values. Thus, providing a more realistic 'central' value for skewed distributions. This measure is a reliable reflection of the dataset and is widely used in fields like real estate, economics, and finance for data interpretation and decision-making. |
||||
Median signifies the middle value in a data set when arranged in ascending or descending order. As a data analyst, understanding, calculating, and interpreting the median is crucial. It is especially helpful when dealing with outliers in a dataset as the median is less sensitive to extreme values. Thus, providing a more realistic 'central' value for skewed distributions. This measure is a reliable reflection of the dataset and is widely used in fields like real estate, economics, and finance for data interpretation and decision-making. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@How to find the median value](https://www.mathsisfun.com/median.html) |
||||
- [@article@Median: What It Is and How to Calculate It](https://www.investopedia.com/terms/m/median.asp) |
||||
@ -1,3 +1,8 @@ |
||||
# Min / Max Function |
||||
|
||||
Understanding the minimum and maximum values in your dataset is critical in data analysis. These basic functions, often referred to as Min-Max functions, are statistical tools that data analysts use to inspect the distribution of a particular dataset. By identifying the lowest and highest values, data analysts can gain insight into the range of the dataset, identify possible outliers, and understand the data's variability. Beyond their use in descriptive statistics, Min-Max functions also play a vital role in data normalization, shaping the accuracy of predictive models in Machine Learning and AI fields. |
||||
Understanding the minimum and maximum values in your dataset is critical in data analysis. These basic functions, often referred to as Min-Max functions, are statistical tools that data analysts use to inspect the distribution of a particular dataset. By identifying the lowest and highest values, data analysts can gain insight into the range of the dataset, identify possible outliers, and understand the data's variability. Beyond their use in descriptive statistics, Min-Max functions also play a vital role in data normalization, shaping the accuracy of predictive models in Machine Learning and AI fields. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@MIN Function](https://support.microsoft.com/en-gb/office/min-function-61635d12-920f-4ce2-a70f-96f202dcc152) |
||||
- [@article@MAX Function](https://support.microsoft.com/en-gb/office/max-function-e0012414-9ac8-4b34-9a47-73e662c08098) |
||||
@ -1,3 +1,8 @@ |
||||
# Model Evaluation Techniques |
||||
|
||||
As a data analyst, it's crucial to understand various model evaluation techniques. These techniques involve different methods to measure the performance or accuracy of machine learning models. For instance, using confusion matrix, precision, recall, F1 score, ROC curves or Root Mean Squared Error (RMSE) among others. Knowing how to apply these techniques effectively not only helps in selecting the best model for a specific problem but also guides in tuning the performance of the models for optimal results. Understanding these model evaluation techniques also allows data analysts to interpret evaluation results and determine the effectiveness and applicability of a model. |
||||
As a data analyst, it's crucial to understand various model evaluation techniques. These techniques involve different methods to measure the performance or accuracy of machine learning models. For instance, using confusion matrix, precision, recall, F1 score, ROC curves or Root Mean Squared Error (RMSE) among others. Knowing how to apply these techniques effectively not only helps in selecting the best model for a specific problem but also guides in tuning the performance of the models for optimal results. Understanding these model evaluation techniques also allows data analysts to interpret evaluation results and determine the effectiveness and applicability of a model. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is model evaluation](https://domino.ai/data-science-dictionary/model-evaluation) |
||||
- [@article@Model evaluation metrics](https://www.markovml.com/blog/model-evaluation-metrics) |
||||
@ -1,3 +1,8 @@ |
||||
# MPI |
||||
|
||||
Message Passing Interface (MPI) is a pioneering technique in the broader realm of data processing strategies. As a data analyst, understanding and implementing MPI is pivotal for managing massive data sets. MPI is an authorized standard for performing parallel computing, which allows concurrent data processing, maintaining a highly efficient and time-saving operation. This system exchanges data between separate tasks and aids in solving complex problems related to computations and data analysis. By leveraging MPI in data processing, analysts can expect to optimize their work and contribute to faster decision-making, thereby enhancing the overall organizational efficiency. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Message Passing Interface Forum](https://www.mpi-forum.org/) |
||||
- [@article@Microsoft MPI](https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi) |
||||
@ -1,3 +1,8 @@ |
||||
# Naive Bayes |
||||
|
||||
As a data analyst, understanding various machine learning algorithms is crucial. Naive Bayes is one of such basic yet powerful algorithms used for predictive modeling and data classification. This algorithm applies the principles of probability and statistics, specifically Bayes' theorem, with a 'naive' assumption of independence among the predictors. Ideal for dealing with large volumes of data, Naive Bayes is a competitive algorithm for text classification, spam filtering, recommendation systems, and more. Understanding Naive Bayes can significantly improve the ability of a data analyst to create more effective models and deliver superior analytical results. |
||||
As a data analyst, understanding various machine learning algorithms is crucial. Naive Bayes is one of such basic yet powerful algorithms used for predictive modeling and data classification. This algorithm applies the principles of probability and statistics, specifically Bayes' theorem, with a 'naive' assumption of independence among the predictors. Ideal for dealing with large volumes of data, Naive Bayes is a competitive algorithm for text classification, spam filtering, recommendation systems, and more. Understanding Naive Bayes can significantly improve the ability of a data analyst to create more effective models and deliver superior analytical results. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What are Naïve Bayes classifiers?](https://www.ibm.com/topics/naive-bayes) |
||||
- [@article@Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html) |
||||
@ -1,3 +1,8 @@ |
||||
# Neural Networks |
||||
|
||||
Neural Networks play a pivotal role in the landscape of deep learning, offering a plethora of benefits and applications for data analysts. They are computational models that emulate the way human brain processes information, enabling machines to make intelligent decisions. As a data analyst, understanding and utilizing neural networks can greatly enhance decision-making process as it allows to quickly and effectively analyze large datasets, recognize patterns, and forecast future trends. In deep learning, these networks are used for creating advanced models that can tackle complex tasks such as image recognition, natural language processing, and speech recognition, to name but a few. Therefore, an in-depth knowledge of neural networks is a significant asset for any aspiring or professional data analyst. |
||||
Neural Networks play a pivotal role in the landscape of deep learning, offering a plethora of benefits and applications for data analysts. They are computational models that emulate the way human brain processes information, enabling machines to make intelligent decisions. As a data analyst, understanding and utilizing neural networks can greatly enhance decision-making process as it allows to quickly and effectively analyze large datasets, recognize patterns, and forecast future trends. In deep learning, these networks are used for creating advanced models that can tackle complex tasks such as image recognition, natural language processing, and speech recognition, to name but a few. Therefore, an in-depth knowledge of neural networks is a significant asset for any aspiring or professional data analyst. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is a neural network?](https://aws.amazon.com/what-is/neural-network/) |
||||
- [@article@Explained: Neural networks](https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414) |
||||
@ -1,3 +1,8 @@ |
||||
# Pandas |
||||
|
||||
Pandas is a widely acknowledged and highly useful data manipulation library in the world of data analysis. Known for its robust features like data cleaning, wrangling and analysis, pandas has become one of the go-to tools for data analysts. Built on NumPy, it provides high-performance, easy-to-use data structures and data analysis tools. In essence, its flexibility and versatility make it a critical part of the data analyst's toolkit, as it holds the capability to cater to virtually every data manipulation task. |
||||
Pandas is a widely acknowledged and highly useful data manipulation library in the world of data analysis. Known for its robust features like data cleaning, wrangling and analysis, pandas has become one of the go-to tools for data analysts. Built on NumPy, it provides high-performance, easy-to-use data structures and data analysis tools. In essence, its flexibility and versatility make it a critical part of the data analyst's toolkit, as it holds the capability to cater to virtually every data manipulation task. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Pandas Website](https://pandas.pydata.org/) |
||||
- [@video@NumPy vs Pandas](https://www.youtube.com/watch?v=KHoEbRH46Zk) |
||||
@ -1,3 +1,8 @@ |
||||
# Pandas for Data Cleaning |
||||
|
||||
In the realms of data analysis, data cleaning is a crucial preliminary process, this is where `pandas` - a popular python library - shines. Primarily used for data manipulation and analysis, pandas adopts a flexible and powerful data structure (DataFrames and Series) that greatly simplifies the process of cleaning raw, messy datasets. Data analysts often work with large volumes of data, some of which may contain missing or inconsistent data that can negatively impact the results of their analysis. By utilizing pandas, data analysts can quickly identify, manage and fill these missing values, drop unnecessary columns, rename column headings, filter specific data, apply functions for more complex data transformations and much more. Thus, making pandas an invaluable tool for effective data cleaning in data analysis. |
||||
In the realms of data analysis, data cleaning is a crucial preliminary process, this is where `pandas` - a popular python library - shines. Primarily used for data manipulation and analysis, pandas adopts a flexible and powerful data structure (DataFrames and Series) that greatly simplifies the process of cleaning raw, messy datasets. Data analysts often work with large volumes of data, some of which may contain missing or inconsistent data that can negatively impact the results of their analysis. By utilizing pandas, data analysts can quickly identify, manage and fill these missing values, drop unnecessary columns, rename column headings, filter specific data, apply functions for more complex data transformations and much more. Thus, making pandas an invaluable tool for effective data cleaning in data analysis. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Pandas Website](https://pandas.pydata.org/) |
||||
- [@video@NumPy vs Pandas](https://www.youtube.com/watch?v=KHoEbRH46Zk) |
||||
@ -1,3 +1,8 @@ |
||||
# Parallel Processing |
||||
|
||||
Parallel processing is an efficient form of data processing that allows Data Analysts to deal with larger volumes of data at a faster pace. It is a computational method that allows multiple tasks to be performed concurrently, instead of sequentially, thus, speeding up data processing. Parallel processing proves to be invaluable for Data Analysts, as they are often tasked with analyzing huge data sets and compiling reports in real-time. As the demand for rapid data processing and quick analytics is on the rise, the technique of parallel processing forms a critical element in the versatile toolkit of a Data Analyst. |
||||
Parallel processing is an efficient form of data processing that allows Data Analysts to deal with larger volumes of data at a faster pace. It is a computational method that allows multiple tasks to be performed concurrently, instead of sequentially, thus, speeding up data processing. Parallel processing proves to be invaluable for Data Analysts, as they are often tasked with analyzing huge data sets and compiling reports in real-time. As the demand for rapid data processing and quick analytics is on the rise, the technique of parallel processing forms a critical element in the versatile toolkit of a Data Analyst. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is parallel processing?](https://www.spiceworks.com/tech/iot/articles/what-is-parallel-processing/) |
||||
- [@article@How parallel computing works?](https://computer.howstuffworks.com/parallel-processing.htm) |
||||
@ -1,3 +1,8 @@ |
||||
# Pie Chart |
||||
|
||||
As a data analyst, understanding and efficiently using various forms of data visualization is crucial. Among these, Pie Charts represent a significant tool. Essentially, pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice of the pie corresponds to a particular category. The pie chart's beauty lies in its simplicity and visual appeal, making it an effective way to convey relative proportions or percentages at a glance. For a data analyst, it's particularly useful when you want to show a simple distribution of categorical data. Like any tool, though, it's important to use pie charts wisely—ideally, when your data set has fewer than seven categories, and the proportions between categories are distinct. |
||||
As a data analyst, understanding and efficiently using various forms of data visualization is crucial. Among these, Pie Charts represent a significant tool. Essentially, pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice of the pie corresponds to a particular category. The pie chart's beauty lies in its simplicity and visual appeal, making it an effective way to convey relative proportions or percentages at a glance. For a data analyst, it's particularly useful when you want to show a simple distribution of categorical data. Like any tool, though, it's important to use pie charts wisely—ideally, when your data set has fewer than seven categories, and the proportions between categories are distinct. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@What is a a pie chart](https://www.youtube.com/watch?v=GjJdZaQrItg) |
||||
- [@article@A complete guide to pie charts](https://www.atlassian.com/data/charts/pie-chart-complete-guide) |
||||
@ -1,3 +1,9 @@ |
||||
# Pivot Tables |
||||
|
||||
Data Analysts recurrently find the need to summarize, investigate, and analyze their data to make meaningful and insightful decisions. One of the most powerful tools to accomplish this in Microsoft Excel is the Pivot Table. Pivot Tables allow analysts to organize and summarize large quantities of data in a concise, tabular format. The strength of pivot tables comes from their ability to manipulate data dynamically, leading to quicker analysis and richer insights. Understanding and employing Pivot Tables efficiently is a fundamental skill for any data analyst, as it directly impacts their ability to derive significant information from raw datasets. |
||||
Data Analysts recurrently find the need to summarize, investigate, and analyze their data to make meaningful and insightful decisions. One of the most powerful tools to accomplish this in Microsoft Excel is the Pivot Table. Pivot Tables allow analysts to organize and summarize large quantities of data in a concise, tabular format. The strength of pivot tables comes from their ability to manipulate data dynamically, leading to quicker analysis and richer insights. Understanding and employing Pivot Tables efficiently is a fundamental skill for any data analyst, as it directly impacts their ability to derive significant information from raw datasets. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@articles@Create a pivot table](https://support.microsoft.com/en-gb/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576) |
||||
- [@article@Pivot tables in excel](https://www.excel-easy.com/data-analysis/pivot-tables.html) |
||||
- [@video@How to create a pivot table in excel](https://www.youtube.com/watch?v=PdJzy956wo4) |
||||
@ -1,3 +1,8 @@ |
||||
# PowerBI |
||||
|
||||
PowerBI, an interactive data visualization and business analytics tool developed by Microsoft, plays a crucial role in the field of a data analyst's work. It helps data analysts to convert raw data into meaningful insights through it's easy-to-use dashboards and reports function. This tool provides a unified view of business data, allowing analysts to track and visualize key performance metrics and make better-informed business decisions. With PowerBI, data analysts also have the ability to manipulate and produce visualizations of large data sets that can be shared across an organization, making complex statistical information more digestible. |
||||
PowerBI, an interactive data visualization and business analytics tool developed by Microsoft, plays a crucial role in the field of a data analyst's work. It helps data analysts to convert raw data into meaningful insights through it's easy-to-use dashboards and reports function. This tool provides a unified view of business data, allowing analysts to track and visualize key performance metrics and make better-informed business decisions. With PowerBI, data analysts also have the ability to manipulate and produce visualizations of large data sets that can be shared across an organization, making complex statistical information more digestible. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Power BI Website](https://www.microsoft.com/en-us/power-platform/products/power-bi) |
||||
- [@video@Power BI for beginners](https://www.youtube.com/watch?v=NNSHu0rkew8) |
||||
@ -1,3 +1,8 @@ |
||||
# Predictive Analysis |
||||
|
||||
Predictive analysis is a crucial type of data analytics that any competent data analyst should comprehend. It refers to the practice of extracting information from existing data sets in order to determine patterns and forecast future outcomes and trends. Data analysts apply statistical algorithms, machine learning techniques, and artificial intelligence to the data to anticipate future results. Predictive analysis enables organizations to be proactive, forward-thinking, and strategic by providing them valuable insights on future occurrences. It's a powerful tool that gives companies a significant competitive edge by enabling risk management, opportunity identification, and strategic decision-making. |
||||
Predictive analysis is a crucial type of data analytics that any competent data analyst should comprehend. It refers to the practice of extracting information from existing data sets in order to determine patterns and forecast future outcomes and trends. Data analysts apply statistical algorithms, machine learning techniques, and artificial intelligence to the data to anticipate future results. Predictive analysis enables organizations to be proactive, forward-thinking, and strategic by providing them valuable insights on future occurrences. It's a powerful tool that gives companies a significant competitive edge by enabling risk management, opportunity identification, and strategic decision-making. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@What is predictive analytics?](https://www.youtube.com/watch?v=cVibCHRSxB0) |
||||
- [@article@What is predictive analytics? - Google](https://cloud.google.com/learn/what-is-predictive-analytics) |
||||
@ -1,3 +1,8 @@ |
||||
# Prescriptive Analytics |
||||
|
||||
Prescriptive analytics, a crucial type of data analytics, is essential for making data-driven decisions in business and organizational contexts. As a data analyst, the goal of prescriptive analytics is to recommend various actions using predictions on the basis of known parameters to help decision makers understand likely outcomes. Prescriptive analytics employs a blend of techniques and tools such as algorithms, machine learning, computational modelling procedures, and decision-tree structures to enable automated decision making. Therefore, prescriptive analytics not only anticipates what will happen and when it will happen, but also explains why it will happen, contributing to the significance of a data analyst’s role in an organization. |
||||
Prescriptive analytics, a crucial type of data analytics, is essential for making data-driven decisions in business and organizational contexts. As a data analyst, the goal of prescriptive analytics is to recommend various actions using predictions on the basis of known parameters to help decision makers understand likely outcomes. Prescriptive analytics employs a blend of techniques and tools such as algorithms, machine learning, computational modelling procedures, and decision-tree structures to enable automated decision making. Therefore, prescriptive analytics not only anticipates what will happen and when it will happen, but also explains why it will happen, contributing to the significance of a data analyst’s role in an organization. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@Examples of Prescriptive Analysis](https://www.youtube.com/watch?v=NOo8Nc9zG20) |
||||
- [@article@What is Prescriptive Analysis?](https://www.investopedia.com/terms/p/prescriptive-analytics.asp) |
||||
@ -1,3 +0,0 @@ |
||||
# Python as a Programming Language |
||||
|
||||
Python is a powerful, flexible, open-source programming language that is incredibly impactful in the realm of data analysis. As a data analyst, you are typically required to clean, interpret, visualize and present data, and Python, being versatile and well-supported, has libraries and frameworks like Pandas, Numpy, Matplotlib, and Seaborn which make these tasks easier and efficient. It is a favorite language among data analysts and data scientists due to its simplicity to learn and readability. Understanding Python can greatly enhance the capabilities and effectiveness of a data analyst. |
||||
@ -1,3 +1,8 @@ |
||||
# PyTorch |
||||
|
||||
PyTorch, an open-source machine learning library, has gained considerable popularity among data analysts due to its simplicity and high performance in tasks such as natural language processing and artificial intelligence. Specifically, in the domain of deep learning, PyTorch stands out due to its dynamic computational graph, allowing for a highly intuitive and flexible platform for building complex models. For data analysts, mastering PyTorch can open up a broad range of opportunities for data model development, data processing, and integration of machine learning algorithms. |
||||
PyTorch, an open-source machine learning library, has gained considerable popularity among data analysts due to its simplicity and high performance in tasks such as natural language processing and artificial intelligence. Specifically, in the domain of deep learning, PyTorch stands out due to its dynamic computational graph, allowing for a highly intuitive and flexible platform for building complex models. For data analysts, mastering PyTorch can open up a broad range of opportunities for data model development, data processing, and integration of machine learning algorithms. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@PyTorch Website](https://pytorch.org/) |
||||
- [@video@PyTorch in 100 seconds](https://www.youtube.com/watch?v=ORMx45xqWkA) |
||||
@ -1,3 +1,8 @@ |
||||
# R |
||||
|
||||
R is a powerful language profoundly used by data analysts and statisticians across the globe. Offering a wide array of statistical and graphical techniques, R proves to be an excellent tool for data manipulation, statistical modeling and visualization. With its comprehensive collection of packages and built-in functions for data analysis, R allows data analysts to perform complex exploratory data analysis, build sophisticated models and create stunning visualizations. Moreover, given its open-source nature, R consistently advances with contributions from the worldwide statistical community. |
||||
R is a powerful language profoundly used by data analysts and statisticians across the globe. Offering a wide array of statistical and graphical techniques, R proves to be an excellent tool for data manipulation, statistical modeling and visualization. With its comprehensive collection of packages and built-in functions for data analysis, R allows data analysts to perform complex exploratory data analysis, build sophisticated models and create stunning visualizations. Moreover, given its open-source nature, R consistently advances with contributions from the worldwide statistical community. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@R Website](https://www.r-project.org/about.html) |
||||
- [@video@R vs Python | Which is Better for Data Analysis?](https://www.youtube.com/watch?v=1gdKC5O0Pwc) |
||||
|
||||
@ -1,3 +1,7 @@ |
||||
# Range |
||||
|
||||
The concept of Range refers to the spread of a dataset, primarily in the realm of statistics and data analysis. This measure is crucial for a data analyst as it provides an understanding of the variability amongst the numbers within a dataset. Specifically in a role such as Data Analyst, understanding the range and dispersion aids in making more precise analyses and predictions. Understanding the dispersion within a range can highlight anomalies, identify standard norms, and form the foundation for statistical conclusions like the standard deviation, variance, and interquartile range. It allows for the comprehension of the reliability and stability of particular datasets, which can help guide strategic decisions in many industries. Therefore, range is a key concept that every data analyst must master. |
||||
The concept of Range refers to the spread of a dataset, primarily in the realm of statistics and data analysis. This measure is crucial for a data analyst as it provides an understanding of the variability amongst the numbers within a dataset. Specifically in a role such as Data Analyst, understanding the range and dispersion aids in making more precise analyses and predictions. Understanding the dispersion within a range can highlight anomalies, identify standard norms, and form the foundation for statistical conclusions like the standard deviation, variance, and interquartile range. It allows for the comprehension of the reliability and stability of particular datasets, which can help guide strategic decisions in many industries. Therefore, range is a key concept that every data analyst must master. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@How to find the range of a data set](https://www.scribbr.co.uk/stats/range-statistics/) |
||||
@ -1,3 +1,8 @@ |
||||
# Regression |
||||
|
||||
As a data analyst, understanding regression is of paramount importance. Regression analysis is a form of predictive modelling technique which investigates the relationship between dependent and independent variables. It is used for forecast, time series modelling and finding the causal effect relationship between variables. In essence, Regression techniques are used by data analysts to predict a continuous outcome variable (dependent variable) based on one or more predictor variables (independent variables). The main goal is to understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This understanding of regression takes data analysis from a reactive position to a more powerful, predictive one, equipping data analysts with an integral tool in their work. |
||||
As a data analyst, understanding regression is of paramount importance. Regression analysis is a form of predictive modelling technique which investigates the relationship between dependent and independent variables. It is used for forecast, time series modelling and finding the causal effect relationship between variables. In essence, Regression techniques are used by data analysts to predict a continuous outcome variable (dependent variable) based on one or more predictor variables (independent variables). The main goal is to understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This understanding of regression takes data analysis from a reactive position to a more powerful, predictive one, equipping data analysts with an integral tool in their work. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Regression: Definition, Analysis, Calculation, and Example](https://www.investopedia.com/terms/r/regression.asp) |
||||
- [@article@A Refresher on Regression Analysis - Harvard](https://hbr.org/2015/11/a-refresher-on-regression-analysis) |
||||
@ -1,3 +1,8 @@ |
||||
# Reinforcement |
||||
|
||||
Reinforcement learning is a key topic within the broader realm of machine learning. Data analysts and other professionals dealing with data often utilize reinforcement learning techniques. In simple, it can be considered as a type of algorithm that uses trial and error to come up with solutions to problems. Notably, these algorithms learn the ideal behaviour within a specific context, with the intention of maximizing performance. As a data analyst, understanding reinforcement learning provides a crucial expertise, especially when dealing with complex data structures and making strategic decisions based on that data. |
||||
Reinforcement learning is a key topic within the broader realm of machine learning. Data analysts and other professionals dealing with data often utilize reinforcement learning techniques. In simple, it can be considered as a type of algorithm that uses trial and error to come up with solutions to problems. Notably, these algorithms learn the ideal behaviour within a specific context, with the intention of maximizing performance. As a data analyst, understanding reinforcement learning provides a crucial expertise, especially when dealing with complex data structures and making strategic decisions based on that data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is reinforcement learning](https://aws.amazon.com/what-is/reinforcement-learning/#:~:text=Reinforcement%20learning%20(RL)%20is%20a,use%20to%20achieve%20their%20goals.) |
||||
- [@article@What is reinforcement learning - IBM](https://www.ibm.com/topics/reinforcement-learning) |
||||
@ -1,3 +1,8 @@ |
||||
# Removing Duplicates |
||||
|
||||
In the world of data analysis, a critical step is data cleaning, that includes an important sub-task: removing duplicate entries. Duplicate data can distort the results of data analysis by giving extra weight to duplicate instances and leading to biased or incorrect conclusions. Despite the quality of data collection, there's a high probability that datasets may contain duplicate records due to various factors like human error, merging datasets, etc. Therefore, data analysts must master the skill of identifying and removing duplicates to ensure that their analysis is based on a unique, accurate, and diverse set of data. This process contributes to more accurate predictions and inferences, thus maximizing the insights gained from the data. |
||||
In the world of data analysis, a critical step is data cleaning, that includes an important sub-task: removing duplicate entries. Duplicate data can distort the results of data analysis by giving extra weight to duplicate instances and leading to biased or incorrect conclusions. Despite the quality of data collection, there's a high probability that datasets may contain duplicate records due to various factors like human error, merging datasets, etc. Therefore, data analysts must master the skill of identifying and removing duplicates to ensure that their analysis is based on a unique, accurate, and diverse set of data. This process contributes to more accurate predictions and inferences, thus maximizing the insights gained from the data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Handling Duplicate Values and Outliers in a dataset](https://medium.com/@ayushmandurgapal/handling-duplicate-values-and-outliers-in-a-dataset-b00ce130818e) |
||||
- [@video@How To Remove Duplicates in a Dataset and Find Unique Values](https://www.youtube.com/watch?v=KBzYrvjUsps) |
||||
@ -1,5 +0,0 @@ |
||||
# Replace/Substitute |
||||
|
||||
When working with datasets, there is often a need for a Data Analyst to alter or adjust certain values. This necessity might arise due to incorrect or inaccurate entries, outliers affecting the results, or simply the need to rewrite certain values for better interpretation and analysis of the data. One of the key basic functions that allow for such alterations in the data is the 'replace' or 'substitute' function. |
||||
|
||||
The replace or substitute function provides an efficient way to replace certain values in a dataset with another. This fundamental function is not only applicable to numerals but it is also functional with categorical data. In data analysis, this replace or substitute function is absolutely critical, contributing greatly to data cleaning, manipulation, and subsequently, the accuracy and reliability of the analytical results obtained. |
||||
@ -0,0 +1 @@ |
||||
# REPLACE / SUBSTITUTE |
||||
@ -1,3 +1,8 @@ |
||||
# Scatter Plot |
||||
|
||||
A scatter plot, a crucial aspect of data visualization, is a mathematical diagram using Cartesian coordinates to represent values from two different variables. As a data analyst, understanding and interpreting scatter plots can be instrumental in identifying correlations and trends within a dataset, drawing meaningful insights, and showcasing these findings in a clear, visual manner. In addition, scatter plots are paramount in predictive analytics as they reveal patterns which can be used to predict future occurrences. |
||||
A scatter plot, a crucial aspect of data visualization, is a mathematical diagram using Cartesian coordinates to represent values from two different variables. As a data analyst, understanding and interpreting scatter plots can be instrumental in identifying correlations and trends within a dataset, drawing meaningful insights, and showcasing these findings in a clear, visual manner. In addition, scatter plots are paramount in predictive analytics as they reveal patterns which can be used to predict future occurrences. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@Mastering scatter plots](https://www.atlassian.com/data/charts/what-is-a-scatter-plot) |
||||
- [@video@Scatter Graphs: What are they and how to plot them](https://www.youtube.com/watch?v=Vyg9qmBsgAc) |
||||
@ -1,3 +1,8 @@ |
||||
# Seaborn |
||||
|
||||
Seaborn is a robust, comprehensive Python library focused on the creation of informative and attractive statistical graphics. As a data analyst, seaborn plays an essential role in elaborating complex visual stories with the data. It aids in understanding the data by providing an interface for drawing attractive and informative statistical graphics. Seaborn is built on top of Python's core visualization library Matplotlib, and is integrated with data structures from Pandas. This makes seaborn an integral tool for data visualization in the data analyst's toolkit, making the exploration and understanding of data easier and more intuitive. |
||||
Seaborn is a robust, comprehensive Python library focused on the creation of informative and attractive statistical graphics. As a data analyst, seaborn plays an essential role in elaborating complex visual stories with the data. It aids in understanding the data by providing an interface for drawing attractive and informative statistical graphics. Seaborn is built on top of Python's core visualization library Matplotlib, and is integrated with data structures from Pandas. This makes seaborn an integral tool for data visualization in the data analyst's toolkit, making the exploration and understanding of data easier and more intuitive. |
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|
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Learn more from the following resources: |
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|
||||
- [@official@Seaborn Website](https://seaborn.pydata.org/) |
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- [@video@Seaborn Tutorial : Seaborn Full Course](https://www.youtube.com/watch?v=6GUZXDef2U0) |
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@ -1,3 +1,8 @@ |
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# Spark |
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|
||||
As a big data processing framework, Apache Spark showcases immense importance in the field of data analysis. Abreast with the ability to handle both batch and real-time analytics, Spark offers an interface for programming entire clusters with implicit data parallelism and fault tolerance. As a data analyst, mastery over Spark becomes essential in order to efficiently process and analyze complex and high-volume data. This powerful open-source tool can simplify the daunting task of gleaning actionable insights from massive, disparate data sets. |
||||
As a big data processing framework, Apache Spark showcases immense importance in the field of data analysis. Abreast with the ability to handle both batch and real-time analytics, Spark offers an interface for programming entire clusters with implicit data parallelism and fault tolerance. As a data analyst, mastery over Spark becomes essential in order to efficiently process and analyze complex and high-volume data. This powerful open-source tool can simplify the daunting task of gleaning actionable insights from massive, disparate data sets. |
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|
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Learn more from the following resources: |
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|
||||
- [@official@Apache Spark Website](https://spark.apache.org/) |
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- [@opensource@apache/spark](https://github.com/apache/spark) |
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@ -1,3 +1,8 @@ |
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# Stacked Chart |
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|
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A stacked chart is an essential tool for a data analyst in the field of data visualization. This type of chart presents quantitative data in a visually appealing manner and allows users to easily compare different categories while still being able to compare the total sizes. These charts are highly effective when trying to measure part-to-whole relationships, displaying accumulated totals over time or when presenting data with multiple variables. Data analysts often use stacked charts to detect patterns, trends and anomalies which can aid in strategic decision making. |
||||
A stacked chart is an essential tool for a data analyst in the field of data visualization. This type of chart presents quantitative data in a visually appealing manner and allows users to easily compare different categories while still being able to compare the total sizes. These charts are highly effective when trying to measure part-to-whole relationships, displaying accumulated totals over time or when presenting data with multiple variables. Data analysts often use stacked charts to detect patterns, trends and anomalies which can aid in strategic decision making. |
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|
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Learn more from the following resources: |
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|
||||
- [@article@What is a stacked chart?](https://www.spotfire.com/glossary/what-is-a-stacked-chart) |
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- [@article@A Complete Guide to Stacked Bar Charts](https://www.atlassian.com/data/charts/stacked-bar-chart-complete-guide) |
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@ -1,3 +1,8 @@ |
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# Standard Deviation |
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|
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In the realm of data analysis, the concept of dispersion plays a critical role in understanding and interpreting data. One of the key measures of dispersion is the Standard Deviation. As a data analyst, understanding the standard deviation is crucial as it gives insight into how much variation or dispersion exists from the average (mean), or expected value. A low standard deviation indicates that the data points are generally close to the mean, while a high standard deviation implies that the data points are spread out over a wider range. By mastering the concept of standard deviation and other statistical tools related to dispersion, data analysts are better equipped to provide meaningful analyses and insights from the available data. |
||||
In the realm of data analysis, the concept of dispersion plays a critical role in understanding and interpreting data. One of the key measures of dispersion is the Standard Deviation. As a data analyst, understanding the standard deviation is crucial as it gives insight into how much variation or dispersion exists from the average (mean), or expected value. A low standard deviation indicates that the data points are generally close to the mean, while a high standard deviation implies that the data points are spread out over a wider range. By mastering the concept of standard deviation and other statistical tools related to dispersion, data analysts are better equipped to provide meaningful analyses and insights from the available data. |
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|
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Learn more from the following resources: |
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|
||||
- [@article@Standard Deviation Formula and Uses vs. Variance](https://www.investopedia.com/terms/s/standarddeviation.asp) |
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- [@video@Standard Deviation](https://www.youtube.com/watch?v=esskJJF8pCc) |
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@ -1,3 +1,8 @@ |
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# Statistical Analysis: A Key Concept for Data Analysts |
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|
||||
Statistical analysis plays a critical role in the daily functions of a data analyst. It encompasses collecting, examining, interpreting, and present data, enabling data analysts to uncover patterns, trends and relationships, deduce insights and support decision-making in various fields. By applying statistical concepts, data analysts can transform complex data sets into understandable information that organizations can leverage for actionable insights. This cornerstone of data analysis enables analysts to deliver predictive models, trend analysis, and valuable business insights, making it indispensable in the world of data analytics. It is vital for data analysts to grasp such statistical methodologies to effectively decipher large data volumes they handle. |
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|
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Learn more from the following resources: |
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|
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- [@article@Understanding Statistical Analysis](https://www.simplilearn.com/what-is-statistical-analysis-article) |
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- [@video@Statistical Analysis](https://www.youtube.com/watch?v=XjMBZE1DuBY) |
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@ -1,3 +1,5 @@ |
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# Statistical Analysis |
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|
||||
Statistical analysis is a core component of a data analyst's toolkit. As professionals dealing with vast amount of structured and unstructured data, data analysts often turn to statistical methods to extract insights and make informed decisions. The role of statistical analysis in data analytics involves gathering, reviewing, and interpreting data for various applications, enabling businesses to understand their performance, trends, and growth potential. Data analysts use a range of statistical techniques from modeling, machine learning, and data mining, to convey vital information that supports strategic company actions. |
||||
Statistical analysis is a core component of a data analyst's toolkit. As professionals dealing with vast amount of structured and unstructured data, data analysts often turn to statistical methods to extract insights and make informed decisions. The role of statistical analysis in data analytics involves gathering, reviewing, and interpreting data for various applications, enabling businesses to understand their performance, trends, and growth potential. Data analysts use a range of statistical techniques from modeling, machine learning, and data mining, to convey vital information that supports strategic company actions. |
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|
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Learn more from the following resources: |
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@ -1,3 +1,8 @@ |
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# Sum |
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|
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Sum is one of the most fundamental operations in data analysis. As a data analyst, the ability to quickly and accurately summarize numerical data is key to draw meaningful insights from large data sets. The operation can be performed using various software and programming languages such as Excel, SQL, Python, R etc., each providing distinct methods to compute sums. Understanding the 'sum' operation is critical for tasks such as trend analysis, forecasting, budgeting, and essentially any operation involving quantitative data. |
||||
Sum is one of the most fundamental operations in data analysis. As a data analyst, the ability to quickly and accurately summarize numerical data is key to draw meaningful insights from large data sets. The operation can be performed using various software and programming languages such as Excel, SQL, Python, R etc., each providing distinct methods to compute sums. Understanding the 'sum' operation is critical for tasks such as trend analysis, forecasting, budgeting, and essentially any operation involving quantitative data. |
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|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@SUM Function](https://support.microsoft.com/en-gb/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89) |
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- [@video@How to use the SUM function in excel](https://www.youtube.com/watch?v=-u-9f3QrdAQ) |
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@ -1,3 +1,8 @@ |
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# Supervised Machine Learning Basics for Data Analysts |
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|
||||
Supervised machine learning forms an integral part of the toolset for a Data Analyst. With a direct focus on building predictive models from labeled datasets, it involves training an algorithm based on these known inputs and outputs, helping Data Analysts establish correlations and make reliable predictions. Fortifying a Data Analyst's role, supervised machine learning enables the accurate interpretation of complex data, enhancing decision-making processes. |
||||
Supervised machine learning forms an integral part of the toolset for a Data Analyst. With a direct focus on building predictive models from labeled datasets, it involves training an algorithm based on these known inputs and outputs, helping Data Analysts establish correlations and make reliable predictions. Fortifying a Data Analyst's role, supervised machine learning enables the accurate interpretation of complex data, enhancing decision-making processes. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is supervised learning?](https://cloud.google.com/discover/what-is-supervised-learning) |
||||
- [@article@Supervised Machine Learning](https://www.datacamp.com/blog/supervised-machine-learning) |
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@ -1,3 +1,8 @@ |
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# Tableau in Data Visualization |
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|
||||
Tableau is a powerful data visualization tool utilized extensively by data analysts worldwide. Its primary role is to transform raw, unprocessed data into an understandable format without any technical skills or coding. Data analysts use Tableau to create data visualizations, reports, and dashboards that help businesses make more informed, data-driven decisions. They also use it to perform tasks like trend analysis, pattern identification, and forecasts, all within a user-friendly interface. Moreover, Tableau's data visualization capabilities make it easier for stakeholders to understand complex data and act on insights quickly. |
||||
Tableau is a powerful data visualization tool utilized extensively by data analysts worldwide. Its primary role is to transform raw, unprocessed data into an understandable format without any technical skills or coding. Data analysts use Tableau to create data visualizations, reports, and dashboards that help businesses make more informed, data-driven decisions. They also use it to perform tasks like trend analysis, pattern identification, and forecasts, all within a user-friendly interface. Moreover, Tableau's data visualization capabilities make it easier for stakeholders to understand complex data and act on insights quickly. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Tableau Website](https://www.tableau.com/en-gb) |
||||
- [@video@What is Tableau?](https://www.youtube.com/watch?v=NLCzpPRCc7U) |
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@ -1,3 +1,8 @@ |
||||
# Tensor Flow |
||||
|
||||
TensorFlow, developed by Google Brain Team, has become a crucial tool in the realm of data analytics, particularly within the field of deep learning. It's an open-source platform for machine learning, offering a comprehensive and flexible ecosystem of tools, libraries, and community resources. As a data analyst, understanding and implementing TensorFlow for deep learning models allows us to identify complex patterns and make insightful predictions which standard analysis could miss. It's in-demand skill that enhances our ability to generate accurate insights from colossal and complicated structured or unstructured data sets. |
||||
TensorFlow, developed by Google Brain Team, has become a crucial tool in the realm of data analytics, particularly within the field of deep learning. It's an open-source platform for machine learning, offering a comprehensive and flexible ecosystem of tools, libraries, and community resources. As a data analyst, understanding and implementing TensorFlow for deep learning models allows us to identify complex patterns and make insightful predictions which standard analysis could miss. It's in-demand skill that enhances our ability to generate accurate insights from colossal and complicated structured or unstructured data sets. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@official@Tensorflow Website](https://www.tensorflow.org/) |
||||
- [@video@Tensorflow in 100 seconds](https://www.youtube.com/watch?v=i8NETqtGHms) |
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@ -1,3 +1,8 @@ |
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# Trim |
||||
|
||||
Trim is considered a basic yet vital function within the scope of data analysis. It plays an integral role in preparing and cleansing the dataset, which is key to analytical accuracy. Trim allows data analysts to streamline dataset by removing extra spaces, enhancing the data quality. Furthermore, Trim functions can help in reducing the errors, enhancing the efficiency of data modelling and ensuring reliable data insight generation. Understanding Trim function is thus an essential part of a data analyst's toolbox. |
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|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@TRIM Function](https://corporatefinanceinstitute.com/resources/excel/trim-function/) |
||||
- [@article@Excel TRIM Function](https://support.microsoft.com/en-gb/office/trim-function-410388fa-c5df-49c6-b16c-9e5630b479f9) |
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@ -1,3 +1,8 @@ |
||||
# Unsupervised Learning in Machine Learning Basics |
||||
|
||||
Unsupervised learning, as a fundamental aspect of Machine Learning, holds great implications in the realm of data analytics. It is an approach where a model learns to identify patterns and relationships within a dataset that isn't labelled or classified. It is especially useful for a Data Analyst as it can assist in recognizing unforeseen trends, providing new insights or preparing data for other machine learning tasks. This ability to infer without direct supervision allows a vast potential for latent structure discovery and new knowledge derivation from raw data. |
||||
Unsupervised learning, as a fundamental aspect of Machine Learning, holds great implications in the realm of data analytics. It is an approach where a model learns to identify patterns and relationships within a dataset that isn't labelled or classified. It is especially useful for a Data Analyst as it can assist in recognizing unforeseen trends, providing new insights or preparing data for other machine learning tasks. This ability to infer without direct supervision allows a vast potential for latent structure discovery and new knowledge derivation from raw data. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is unsupervised learning?](https://cloud.google.com/discover/what-is-unsupervised-learning) |
||||
- [@article@Introduction to unsupervised learning](https://www.datacamp.com/blog/introduction-to-unsupervised-learning) |
||||
@ -1,3 +1,9 @@ |
||||
# Upper, Lower, Proper Functions |
||||
|
||||
In the field of data analysis, the Upper, Lower, and Proper functions serve as fundamental tools for manipulating and transforming text data. A data analyst often works with a vast array of datasets, where the text data may not always adhere to a consistent format. To tackle such issues, the Upper, Lower, and Proper functions are used. 'Upper' converts all the text to uppercase, while 'Lower' does the opposite, transforming all text to lowercase. The 'Proper' function is used to capitalize the first letter of each word, making it proper case. These functions are indispensable when it comes to cleaning and preparing data, a major part of a data analyst's role. |
||||
In the field of data analysis, the Upper, Lower, and Proper functions serve as fundamental tools for manipulating and transforming text data. A data analyst often works with a vast array of datasets, where the text data may not always adhere to a consistent format. To tackle such issues, the Upper, Lower, and Proper functions are used. 'Upper' converts all the text to uppercase, while 'Lower' does the opposite, transforming all text to lowercase. The 'Proper' function is used to capitalize the first letter of each word, making it proper case. These functions are indispensable when it comes to cleaning and preparing data, a major part of a data analyst's role. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@UPPER Function](https://support.microsoft.com/en-gb/office/upper-function-c11f29b3-d1a3-4537-8df6-04d0049963d6) |
||||
- [@article@LOWER Function](https://support.microsoft.com/en-gb/office/lower-function-3f21df02-a80c-44b2-afaf-81358f9fdeb4) |
||||
- [@article@PROPER Function](https://support.microsoft.com/en-gb/office/proper-function-52a5a283-e8b2-49be-8506-b2887b889f94) |
||||
@ -1,3 +1,8 @@ |
||||
# Variance as a Measure of Dispersion |
||||
|
||||
Data analysts heavily rely on statistical concepts to analyze and interpret data, and one such fundamental concept is variance. Variance, an essential measure of dispersion, quantifies the spread of data, providing insight into the level of variability within the dataset. Understanding variance is crucial for data analysts as the reliability of many statistical models depends on the assumption of constant variance across observations. In other words, it helps analysts determine how much data points diverge from the expected value or mean, which can be pivotal in identifying outliers, understanding data distribution, and driving decision-making processes. However, variance can't be interpreted in the original units of measurement due to its squared nature, which is why it is often used in conjunction with its square root, the standard deviation. |
||||
Data analysts heavily rely on statistical concepts to analyze and interpret data, and one such fundamental concept is variance. Variance, an essential measure of dispersion, quantifies the spread of data, providing insight into the level of variability within the dataset. Understanding variance is crucial for data analysts as the reliability of many statistical models depends on the assumption of constant variance across observations. In other words, it helps analysts determine how much data points diverge from the expected value or mean, which can be pivotal in identifying outliers, understanding data distribution, and driving decision-making processes. However, variance can't be interpreted in the original units of measurement due to its squared nature, which is why it is often used in conjunction with its square root, the standard deviation. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@](https://www.investopedia.com/terms/v/variance.asp) |
||||
- [@article@How to calculate variance](https://www.scribbr.co.uk/stats/variance-meaning/ |
||||
@ -1,3 +1,8 @@ |
||||
# Visualization - A Key Concept for Data Analysts |
||||
|
||||
The visualization of data is an essential skill in the toolkit of every data analyst. This practice is about transforming complex raw data into a graphical format that allows for an easier understanding of large data sets, trends, outliers, and important patterns. Whether pie charts, line graphs, bar graphs, or heat maps, data visualization techniques not only streamline data analysis, but also facilitate a more effective communication of the findings to others. This key concept underscores the importance of presenting data in a digestible and visually appealing manner to drive data-informed decision making in an organization. |
||||
The visualization of data is an essential skill in the toolkit of every data analyst. This practice is about transforming complex raw data into a graphical format that allows for an easier understanding of large data sets, trends, outliers, and important patterns. Whether pie charts, line graphs, bar graphs, or heat maps, data visualization techniques not only streamline data analysis, but also facilitate a more effective communication of the findings to others. This key concept underscores the importance of presenting data in a digestible and visually appealing manner to drive data-informed decision making in an organization. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@Data Visualisation in 2024](https://www.youtube.com/watch?v=loYuxWSsLNc) |
||||
- [@article@Data visualisation beginner's guide](https://www.tableau.com/en-gb/learn/articles/data-visualization) |
||||
@ -1,3 +1,8 @@ |
||||
# Visualising Distributions |
||||
|
||||
Visualising Distributions, from a data analyst's perspective, plays a key role in understanding the overall distribution and identifying patterns within data. It aids in summarising, structuring, and plotting structured data graphically to provide essential insights. This includes using different chart types like bar graphs, histograms, and scatter plots for interval data, and pie or bar graphs for categorical data. Ultimately, the aim is to provide a straightforward and effective manner to comprehend the data's characteristics and underlying structure. A data analyst uses these visualisation techniques to make initial conclusions, detect anomalies, and decide on further analysis paths. |
||||
Visualising Distributions, from a data analyst's perspective, plays a key role in understanding the overall distribution and identifying patterns within data. It aids in summarising, structuring, and plotting structured data graphically to provide essential insights. This includes using different chart types like bar graphs, histograms, and scatter plots for interval data, and pie or bar graphs for categorical data. Ultimately, the aim is to provide a straightforward and effective manner to comprehend the data's characteristics and underlying structure. A data analyst uses these visualisation techniques to make initial conclusions, detect anomalies, and decide on further analysis paths. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@video@Visualising Distributions in Power BI](https://www.youtube.com/watch?v=rOemr3sz2vw) |
||||
- [@article@Data Visualizations that Capture Distributions](https://www.datacamp.com/blog/data-demystified-data-visualizations-that-capture-distributions) |
||||
@ -1,3 +1,8 @@ |
||||
# Web Scraping |
||||
|
||||
Web scraping plays a significant role in collecting unique datasets for data analysis. In the realm of a data analyst's tasks, web scraping refers to the method of extracting information from websites and converting it into a structured usable format like a CSV, Excel spreadsheet, or even into databases. This technique allows data analysts to gather large sets of data from the internet, which otherwise could be time-consuming if done manually. The capability of web scraping and parsing data effectively can give data analysts a competitive edge in their data analysis process, from unlocking in-depth, insightful information to making data-driven decisions. |
||||
Web scraping plays a significant role in collecting unique datasets for data analysis. In the realm of a data analyst's tasks, web scraping refers to the method of extracting information from websites and converting it into a structured usable format like a CSV, Excel spreadsheet, or even into databases. This technique allows data analysts to gather large sets of data from the internet, which otherwise could be time-consuming if done manually. The capability of web scraping and parsing data effectively can give data analysts a competitive edge in their data analysis process, from unlocking in-depth, insightful information to making data-driven decisions. |
||||
|
||||
Learn more from the following resources: |
||||
|
||||
- [@article@What is web scraping what is it used for?](https://www.parsehub.com/blog/what-is-web-scraping/) |
||||
- [@video@What is web scraping?](https://www.youtube.com/watch?v=dlj_QL-ENJM) |
||||
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Reference in new issue