refractor: improve data analyst roadmap (#8104)
* refractor 36 topics * refractor remaining topics - 16pull/8121/head
Vedansh
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# Big Data and Data Analyst |
# Big Data and Data Analyst |
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In the modern digitized world, Big Data refers to extremely large datasets that are challenging to manage and analyze using traditional data processing applications. These datasets often come from numerous different sources and are not only voluminous but also diverse in nature, including structured and unstructured data. The role of a data analyst in the context of big data is crucial. Data analysts are responsible for inspecting, cleaning, transforming, and modeling big data to discover useful information, conclude and support decision-making. They leverage their analytical skills and various big data tools and technologies to extract insights that can benefit the organization and drive strategic business initiatives. |
In the modern digitized world, Big Data refers to extremely large datasets that are challenging to manage and analyze using traditional data processing applications. These datasets often come from numerous different sources and are not only voluminous but also diverse in nature, including structured and unstructured data. The role of a data analyst in the context of big data is crucial. Data analysts are responsible for inspecting, cleaning, transforming, and modeling big data to discover useful information, conclude and support decision-making. They leverage their analytical skills and various big data tools and technologies to extract insights that can benefit the organization and drive strategic business initiatives. |
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Learn more from the following resources: |
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- [@article@Big Data Analytics](https://www.ibm.com/think/topics/big-data-analytics) |
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# Data Cleaning |
# Data Cleaning |
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Data cleaning, which is often referred as data cleansing or data scrubbing, is one of the most important and initial steps in the data analysis process. As a data analyst, the bulk of your work often revolves around understanding, cleaning, and standardizing raw data before analysis. Data cleaning involves identifying, correcting or removing any errors or inconsistencies in datasets in order to improve their quality. The process is crucial because it directly determines the accuracy of the insights you generate - garbage in, garbage out. Even the most sophisticated models and visualizations would not be of much use if they're based on dirty data. Therefore, mastering data cleaning techniques is essential for any data analyst. |
Data cleaning, which is often referred as data cleansing or data scrubbing, is one of the most important and initial steps in the data analysis process. As a data analyst, the bulk of your work often revolves around understanding, cleaning, and standardizing raw data before analysis. Data cleaning involves identifying, correcting or removing any errors or inconsistencies in datasets in order to improve their quality. The process is crucial because it directly determines the accuracy of the insights you generate - garbage in, garbage out. Even the most sophisticated models and visualizations would not be of much use if they're based on dirty data. Therefore, mastering data cleaning techniques is essential for any data analyst. |
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Learn more from the following resources: |
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- [@article@Data Cleaning](https://www.tableau.com/learn/articles/what-is-data-cleaning#:~:text=tools%20and%20software-,What%20is%20data%20cleaning%3F,to%20be%20duplicated%20or%20mislabeled.) |
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# Data Collection |
# Data Collection |
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In the context of the Data Analyst role, data collection is a foundational process that entails gathering relevant data from various sources. This data can be quantitative or qualitative and may be sourced from databases, online platforms, customer feedback, among others. The gathered information is then cleaned, processed, and interpreted to extract meaningful insights. A data analyst performs this whole process carefully, as the quality of data is paramount to ensuring accurate analysis, which in turn informs business decisions and strategies. This highlights the importance of an excellent understanding, proper tools, and precise techniques when it comes to data collection in data analysis. |
Data collection is a foundational process that entails gathering relevant data from various sources. This data can be quantitative or qualitative and may be sourced from databases, online platforms, customer feedback, among others. The gathered information is then cleaned, processed, and interpreted to extract meaningful insights. A data analyst performs this whole process carefully, as the quality of data is paramount to ensuring accurate analysis, which in turn informs business decisions and strategies. This highlights the importance of an excellent understanding, proper tools, and precise techniques when it comes to data collection in data analysis. |
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Learn more from the following resources: |
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- [@article@Data Collection](https://en.wikipedia.org/wiki/Data_collection) |
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# Data Manipulation Libraries |
# 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. |
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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Learn more from the following resources: |
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- [@article@Pandas](https://pandas.pydata.org/) |
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- [@article@NumPy](https://numpy.org/) |
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- [@article@Top Python Libraries for Data Science](https://www.simplilearn.com/top-python-libraries-for-data-science-article) |
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# Data Visualisation Libraries |
# Data Visualization 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. |
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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Learn more from the following resources: |
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# Data Visualization |
# Data Visualization |
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Data Visualization is a fundamental fragment of the responsibilities of a data analyst. It involves the presentation of data in a graphical or pictorial format which allows decision-makers to see analytics visually. This practice can help them comprehend difficult concepts or establish new patterns. With interactive visualization, data analysts can take the data analysis process to a whole new level — drill down into charts and graphs for more detail, and interactively changing what data is presented or how it’s processed. Thereby it forms a crucial link in the chain of converting raw data to actionable insights which is one of the primary roles of a Data Analyst. |
Data Visualization is a fundamental fragment of the responsibilities of a data analyst. It involves the presentation of data in a graphical or pictorial format which allows decision-makers to see analytics visually. This practice can help them comprehend difficult concepts or establish new patterns. With interactive visualization, data analysts can take the data analysis process to a whole new level — drill down into charts and graphs for more detail, and interactively changing what data is presented or how it’s processed. Thereby it forms a crucial link in the chain of converting raw data to actionable insights which is one of the primary roles of a Data Analyst. |
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Learn more from the following resources: |
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- [@article@What is Data Visualization?](https://www.ibm.com/think/topics/data-visualization) |
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# Deep Learning and Data Analysis |
# Deep Learning and Data Analysis |
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Deep learning, a subset of machine learning technique, is increasingly becoming a critical tool for data analysts. Deep learning algorithms utilize multiple layers of neural networks to understand and interpret intricate structures in large data, a skill that is integral to the daily functions of a data analyst. With the ability to learn from unstructured or unlabeled data, deep learning opens a whole new range of possibilities for data analysts in terms of data processing, prediction, and categorization. It has applications in a variety of industries from healthcare to finance to e-commerce and beyond. A deeper understanding of deep learning methodologies can augment a data analyst's capability to evaluate and interpret complex datasets and provide valuable insights for decision making. |
Deep learning, a subset of machine learning technique, is increasingly becoming a critical tool for data analysts. Deep learning algorithms utilize multiple layers of neural networks to understand and interpret intricate structures in large data, a skill that is integral to the daily functions of a data analyst. With the ability to learn from unstructured or unlabeled data, deep learning opens a whole new range of possibilities for data analysts in terms of data processing, prediction, and categorization. It has applications in a variety of industries from healthcare to finance to e-commerce and beyond. A deeper understanding of deep learning methodologies can augment a data analyst's capability to evaluate and interpret complex datasets and provide valuable insights for decision making. |
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Learn more from the following resources: |
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- [@article@Deep Learning for Data Analysis](https://www.ibm.com/think/topics/deep-learning) |
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# Introduction to Data Analytics |
# Introduction to Data Analytics |
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Data Analytics is a core component of a Data Analyst's role. The field involves extracting meaningful insights from raw data to drive decision-making processes. It includes a wide range of techniques and disciplines ranging from the simple data compilation to advanced algorithms and statistical analysis. As a data analyst, you are expected to understand and interpret complex digital data, such as the usage statistics of a website, the sales figures of a company, or client engagement over social media, etc. This knowledge enables data analysts to support businesses in identifying trends, making informed decisions, predicting potential outcomes - hence playing a crucial role in shaping business strategies. |
Data Analytics is a core component of a Data Analyst's role. The field involves extracting meaningful insights from raw data to drive decision-making processes. It includes a wide range of techniques and disciplines ranging from the simple data compilation to advanced algorithms and statistical analysis. As a data analyst, you are expected to understand and interpret complex digital data, such as the usage statistics of a website, the sales figures of a company, or client engagement over social media, etc. This knowledge enables data analysts to support businesses in identifying trends, making informed decisions, predicting potential outcomes - hence playing a crucial role in shaping business strategies. |
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Learn more from the following resources: |
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- [@article@Introduction to Data Analytics](https://www.coursera.org/learn/introduction-to-data-analytics) |
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