refractor: improve data analyst roadmap (kamranahmedse#8104)

* refractor 36 topics

* refractor remaining topics - 16

Author: offensive-vk

src/data/roadmaps/data-analyst/content/[email protected]

@@ -2,5 +2,7 @@
 
 Excel is a powerful tool utilized by data analysts worldwide to store, manipulate, and analyze data. It offers a vast array of features such as pivot tables, graphs and a powerful suite of formulas and functions to help sift through large sets of data. A data analyst uses Excel to perform a wide range of tasks, from simple data entry and cleaning, to more complex statistical analysis and predictive modeling. Proficiency in Excel is often a key requirement for a data analyst, as its versatility and ubiquity make it an indispensable tool in the field of data analysis.
 
+Learn more from the following resources:
+
 - [@article@W3Schools - Excel](https://www.w3schools.com/excel/index.php)
 - [@course@Microsoft Excel Course](https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -5,4 +5,4 @@ Application Programming Interfaces, better known as APIs, play a fundamental rol
 Learn more from the following resources:
 
 - [@article@What is an API?](https://aws.amazon.com/what-is/api/)
-- [@article@A beginners guide to APIs](https://www.postman.com/what-is-an-api/)
+- [@article@A Beginner's Guide to APIs](https://www.postman.com/what-is-an-api/)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -1,8 +1,8 @@
-# Average 
+# 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 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.
 
 Learn more from the following resources:
 
-- [@article@How to calculate the average](https://support.microsoft.com/en-gb/office/calculate-the-average-of-a-group-of-numbers-e158ef61-421c-4839-8290-34d7b1e68283#:~:text=Average%20This%20is%20the%20arithmetic,by%206%2C%20which%20is%205.)
+- [@article@How to Calculate the Average](https://support.microsoft.com/en-gb/office/calculate-the-average-of-a-group-of-numbers-e158ef61-421c-4839-8290-34d7b1e68283#:~:text=Average%20This%20is%20the%20arithmetic,by%206%2C%20which%20is%205.)
 - [@article@Average Formula](https://www.cuemath.com/average-formula/)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -4,5 +4,5 @@ As a vital tool in the data analyst's arsenal, bar charts are essential for anal
 
 Learn more from the following resources:
 
-- [@article@A complete guide to bar charts](https://www.atlassian.com/data/charts/bar-chart-complete-guide)
-- [@video@What is a bar chart?](https://www.youtube.com/watch?v=WTVdncVCvKo)
+- [@article@A Complete Guide to Bar Charts](https://www.atlassian.com/data/charts/bar-chart-complete-guide)
+- [@video@What is a Bar Chart?](https://www.youtube.com/watch?v=WTVdncVCvKo)

src/data/roadmaps/data-analyst/content/big-data-technologies@_aUQZWUhFRvNu0MZ8CPit.md

@@ -1,3 +1,7 @@
 # Big Data and Data Analyst
 
-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.
+
+Learn more from the following resources:
+
+- [@article@Big Data Analytics](https://www.ibm.com/think/topics/big-data-analytics)

src/data/roadmaps/data-analyst/content/cleanup@nC7tViln4UyQFYP_-fyjB.md

@@ -4,5 +4,5 @@ The Cleanup of Data is a critical component of a Data Analyst's role. It involve
 
 Learn more from the following resources:
 
-- [@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)
+- [@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)
 - [@video@Master Data Cleaning Essentials on Excel in Just 10 Minutes](https://www.youtube.com/watch?v=jxq4-KSB_OA)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -1,3 +1,7 @@
 # Data Cleaning
 
-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.
+
+Learn more from the following resources:
+
+- [@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.)

src/data/roadmaps/data-analyst/content/data-collection@_sjXCLHHTbZromJYn6fnu.md

@@ -1,3 +1,7 @@
 # Data Collection
 
-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.
+
+Learn more from the following resources:
+
+- [@article@Data Collection](https://en.wikipedia.org/wiki/Data_collection)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -1,3 +1,9 @@
 # Data Manipulation Libraries
 
-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.
+
+Learn more from the following resources:
+
+- [@article@Pandas](https://pandas.pydata.org/)
+- [@article@NumPy](https://numpy.org/)
+- [@article@Top Python Libraries for Data Science](https://www.simplilearn.com/top-python-libraries-for-data-science-article)

src/data/roadmaps/data-analyst/content/[email protected]

@@ -4,5 +4,5 @@ As a business enterprise expands, so does its data. For data analysts, the surge
 
 Learn more from the following resources:
 
-- [@official@SQL Roadmap](https://roadmap.sh/sql)
-- [@official@PostgreSQL Roadmap](https://roadmap.sh/postgresql-dba)
+- [@roadmap@Visit Dedicated SQL Roadmap](https://roadmap.sh/sql)
+- [@roadmap@Visit Dedicated PostgreSQL Roadmap](https://roadmap.sh/postgresql-dba)