adding more stata docu

content/01-python_v_stata/.ipynb_checkpoints/differences-checkpoint.md

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+# Summary of Differences
+
+While Python and Stata can both be effectively used for data analysis, they do have a few key differences, as summarized below:-
+
+## General Purpose vs Specialized
+
+Python is a versatile, general-purpose programming language that can be used for a wide variety of computing tasks, including web development, artificial intelligence, etc. It's general-purpose nature makes it suitable for a wide range of tasks beyond statistical analysis, allowing users to build end-to-end data science solutions and integrate seamlessly with various technologies. On the other hand, Stata is a specialized programming language, designed specifically for statistical analysis and data management. This specialization makes Stata really efficient and easy to use when conducting statistical analyses, but it comes at the expense of general versatility.
+
+## Syntax
+
+Python has a general syntax, allowing for multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows users to adopt different coding styles and adapt Python to various application domains. In contrast, Stata has a special command-driven syntax which works best for statistical analysis. This syntax is also designed to be intuitive and user-friendly, allowing it's users to focus moreso on the results.
+
+## Packages/Libraries
+
+There are multiple packages which can be easily installed and used with Python. Several of these often complement each other, allowing lots of flexibility in how to conduct your analysis. On the other hand, Stata is more reliant on it's built-in commands and functions. While there are user written packages in Stata, they lack the breadth of Python packages.
+
+## Data Management
+
+While both languages can be used for datasets of various sizes, Stata is known for being efficient when working with large datasets. However, Python is more flexible with handling diverse data structures beyond just databases.
+
+## Learning Curve
+
+Stat's syntax is designed to be very intuitive and easy to use, allowing for a small learning curve and enabling users to focus moreso on the results. In contrast, Python's learning curve may be steeper as it's a general purpose programming language.
+
+## Cost
+
+Python is free and open-source, allowing anyone to use it. On the other hand, Stata requires a license to access, with different pricing tiers depending on what you may need. You can learn more about Stata's pricing structure [here](https://www.stata.com/order/).
+
+## Community
+
+Python has a large and active community, with lots of tutorials, resources, and debugging assistance. While Stata also has an active community, it is not as large as Python's, making debugging tasks and learning new techniques harder.

content/01-python_v_stata/.ipynb_checkpoints/history-checkpoint.md

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 ###### <span style="color:red"> To be updated </span>
 
-<!-- ## History of Python
+<!-- From ChatGPT
 
-HI
+## History of Python:
 
-## History of Stata
+Python is a high-level, interpreted programming language that was created by Guido van Rossum. The language's development began in the late 1980s, and the first official Python release, version 0.9.0, came out in February 1991. Guido van Rossum aimed to create a language that emphasized code readability and ease of use, inspired by the ABC language.
 
-Stata was initially released in 1985, back when PCs were just being introduced on the market. The first few releases didn't have too many features. Things began changing with the release of the program command in Stata 1.3, which allowed users to add their own commands. -->
+The name "Python" was chosen as a tribute to the British comedy group Monty Python. Over the years, Python evolved, with significant releases such as Python 2.0 in 2000 and Python 3.0 in 2008. Python 3 introduced backward-incompatible changes to enhance the language's consistency and eliminate redundancy.
+
+Python gained popularity due to its simplicity, readability, and versatility. It became widely used in various domains, including web development, data science, artificial intelligence, and more. Python's extensive standard library and a vibrant community of developers contributed to its success.
+
+## History of Stata:
+
+Stata is a statistical software package designed for data analysis, manipulation, and visualization. It was developed by StataCorp, founded by William Gould, James Hardin, and Cleve Moler. Stata's development began in the early 1980s, with the first version, Stata 1.0, released in 1985.
+
+Stata was initially focused on providing tools for econometric and statistical analysis. It gained popularity among researchers, economists, and social scientists for its powerful data management capabilities and robust statistical features.
+
+Over the years, StataCorp released several versions of the software, adding new features and improving existing ones. Stata's scripting language allows users to automate tasks and create reproducible analyses. The software is widely used in academia, government, and industry for a variety of applications, including econometrics, public health research, and social sciences.
+
+Both Python and Stata have become essential tools in their respective domains, with Python's general-purpose nature making it versatile across many fields, and Stata's specialization in statistical analysis making it a go-to choice for researchers and analysts in specific disciplines. -->

content/01-python_v_stata/.ipynb_checkpoints/index-checkpoint.md

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+# Python vs Stata
+
+This chapter is intended to give you a brief overview of the differences between Python and Stata. We will cover the history of the programming languages, how they function and key differences regarding how they interact with data. 
+
+<!-- For more information, please look at [Python's official page](https://www.python.org/) and [Stata's official page](https://www.stata.com/) respectively. -->