Update content for data analyst roadmap

src/data/roadmaps/data-analyst/content/100-introduction/102-keyconcepts-for-data/101-cleanup.md

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 # Cleanup 
 
-The Cleanup Under Key Concepts for 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.
+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.

src/data/roadmaps/data-analyst/content/101-excel/102-charting.md

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 # Charting 
 
-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 under Excel 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.
+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.

src/data/roadmaps/data-analyst/content/107-data-cleaning/103-data-transformation.md

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 # Data Transformation 
 
-Data Transformation under Data Cleaning, also known as Data Wrangling, is an essential part of a Data Analyst's role. This process involves the conversion of data from a raw format into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. Data Analysts transform data to make the data more suitable for analysis, ensure accuracy, and to improve data quality. The right transformation techniques can give the data a structure, multiply its value, and enhance the accuracy of the analytics performed by serving meaningful results.
+Data Transformation, also known as Data Wrangling, is an essential part of a Data Analyst's role. This process involves the conversion of data from a raw format into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. Data Analysts transform data to make the data more suitable for analysis, ensure accuracy, and to improve data quality. The right transformation techniques can give the data a structure, multiply its value, and enhance the accuracy of the analytics performed by serving meaningful results.

src/data/roadmaps/data-analyst/content/108-descriptive-analysis/100-central-tendency/100-mean.md

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 # Mean 
 
-In the realm of data analytics, the term "Mean" under "Central Tendency" holds significant importance. 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. 
+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. 

src/data/roadmaps/data-analyst/content/108-descriptive-analysis/100-central-tendency/101-median.md

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 # Median 
 
-Median, an essential tool under the concept of central tendency, 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.

src/data/roadmaps/data-analyst/content/108-descriptive-analysis/100-central-tendency/103-average.md

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 # Average 
 
-When focusing on data analysis, understanding key statistical concepts is crucial. Amongst these, central tendency is a foundational element. Central Tendency refers to the measure that determines the center of a distribution. The average, a specific measure under central tendency, is a commonly used statistical tool by which data analysts discern trends and patterns. As one of the most recognized forms of central tendency, figuring out the "average" involves summing all values in a data set and dividing by the number of values. This provides analysts with a 'typical' value, around which the remaining data tends to cluster, facilitating better decision-making based on existing data.
+When focusing on data analysis, understanding key statistical concepts is crucial. Amongst these, central tendency is a foundational element. Central Tendency refers to the measure that determines the center of a distribution. The average is a commonly used statistical tool by which data analysts discern trends and patterns. As one of the most recognized forms of central tendency, figuring out the "average" involves summing all values in a data set and dividing by the number of values. This provides analysts with a 'typical' value, around which the remaining data tends to cluster, facilitating better decision-making based on existing data.

src/data/roadmaps/data-analyst/content/108-descriptive-analysis/101-dispersion/100-range.md

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 # 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 under dispersion 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.

src/data/roadmaps/data-analyst/content/108-descriptive-analysis/103-visualising-distributions.md

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 # Visualising Distributions 
 
-Visualising Distributions under Descriptive Analysis, 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.

src/data/roadmaps/data-analyst/content/109-data-visualization/105-bar-charts.md

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 # Bar Charts in Data Visualization
 
-As a vital tool in the data analyst's arsenal, bar charts under data visualization 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.
+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.

src/data/roadmaps/data-analyst/content/110-statistical-analysis/100-hypothesis-testing.md

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 # 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 under statistical analysis 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.

src/data/roadmaps/data-analyst/content/110-statistical-analysis/101-correlation-analysis.md

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 # Correlation Analysis 
 
-Correlation Analysis is a quantitative method under statistical analysis that data analysts widely employ to determine if there is a significant relationship between two variables, and if so, how strong or weak, positive or negative that relationship might be. This form of analysis helps data analysts identify patterns and trends within datasets, and is often represented visually through scatter plots. By using correlation analysis, data analysts can derive valuable insights to inform decision-making processes within a wide range of fields, from marketing to finance. The implementation of correlation analysis is crucial to forecast future outcomes, develop strategies and drive business growth.
+Correlation Analysis is a quantitative method that data analysts widely employ to determine if there is a significant relationship between two variables, and if so, how strong or weak, positive or negative that relationship might be. This form of analysis helps data analysts identify patterns and trends within datasets, and is often represented visually through scatter plots. By using correlation analysis, data analysts can derive valuable insights to inform decision-making processes within a wide range of fields, from marketing to finance. The implementation of correlation analysis is crucial to forecast future outcomes, develop strategies and drive business growth.

src/data/roadmaps/data-analyst/content/110-statistical-analysis/102-regression.md

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 # Regression 
 
-As a data analyst, understanding regression under statistical analysis 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.

src/data/roadmaps/data-analyst/content/111-machine-learning-basics/108-model-evaluation-techniques.md

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 # Model Evaluation Techniques 
 
-As a data analyst, it's crucial to understand various model evaluation techniques under machine learning basics. 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.

src/data/roadmaps/data-analyst/content/112-big-data/100-concepts.md

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 # Big Data Concepts 
 
-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 under big data 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.
+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.

src/data/roadmaps/data-analyst/content/112-big-data/101-data-processing-techniques/index.md

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 # Data Processing Techniques 
 
-As a part of the modern business landscape, Data analysts constantly grapple with the challenges and opportunities that come with Big Data. Navigating through this complex environment requires understandings of certain key data processing techniques. These techniques are the tools that enable data analysts to effectively clean, transform, and interpret large volumes of data into actionable, data-driven insights. Leveraging these techniques properly can give businesses an edge, leading to more informed decision-making and strategy development. From MapReduce to Online Analytical Processing (OLAP), each technique has its unique approach and application, suitable for handling different Big Data cases. Significant improvements in processing speed, flexibility, and quality are possible when these techniques are appropriately applied by data analysts. Understanding the intricacies of data processing techniques under Big Data is thus a significant aspect of the data analyst's role.
+As a part of the modern business landscape, Data analysts constantly grapple with the challenges and opportunities that come with Big Data. Navigating through this complex environment requires understandings of certain key data processing techniques. These techniques are the tools that enable data analysts to effectively clean, transform, and interpret large volumes of data into actionable, data-driven insights. Leveraging these techniques properly can give businesses an edge, leading to more informed decision-making and strategy development. From MapReduce to Online Analytical Processing (OLAP), each technique has its unique approach and application, suitable for handling different Big Data cases. Significant improvements in processing speed, flexibility, and quality are possible when these techniques are appropriately applied by data analysts. Understanding the intricacies of data processing techniques is thus a significant aspect of the data analyst's role.

src/data/roadmaps/data-analyst/content/113-deep-learning/105-image-recognition.md

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 # Image Recognition 
 
-Image Recognition has become a significant domain under Deep Learning 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.

src/data/roadmaps/data-analyst/content/113-deep-learning/106-natural-language-processing.md

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 # Natural Language Processing 
 
-In the sphere of data analysis, Natural Language Processing (NLP) under Deep Learning has emerged as a critical aspect. NLP is a branch of artificial intelligence that involves the interaction between computers and human languages. It allows computers to understand, interpret, and generate human languages with meaning and context. This capability opens up potent avenues for data analysts, who often have to handle unstructured data such as customer reviews, comments, and other textual content. 
+In the sphere of data analysis, Natural Language Processing (NLP) has emerged as a critical aspect. NLP is a branch of artificial intelligence that involves the interaction between computers and human languages. It allows computers to understand, interpret, and generate human languages with meaning and context. This capability opens up potent avenues for data analysts, who often have to handle unstructured data such as customer reviews, comments, and other textual content. 
 
-Deep Learning, a subset of machine learning based on artificial neural networks, is particularly effective for NLP tasks, enabling computers to learn from vast amounts of data. For data analysts, understanding and utilizing the potentials of NLP under Deep Learning can greatly improve the efficiency of data processing and extraction of meaningful insights, especially when dealing with large or complex data sets. This knowledge can significantly enhance their ability to make data-driven decisions and predictions tailored to specific business objectives.
+Deep Learning, a subset of machine learning based on artificial neural networks, is particularly effective for NLP tasks, enabling computers to learn from vast amounts of data. For data analysts, understanding and utilizing the potentials of NLP can greatly improve the efficiency of data processing and extraction of meaningful insights, especially when dealing with large or complex data sets. This knowledge can significantly enhance their ability to make data-driven decisions and predictions tailored to specific business objectives.