Updated Structure

Author: Sampreet

README.md

@@ -3,21 +3,88 @@
 ![Code Size](https://img.shields.io/github/languages/code-size/sampreet/python-for-physicists?style=for-the-badge)
 ![Last Commit](https://img.shields.io/github/last-commit/sampreet/python-for-physicists?style=for-the-badge)
 
-> A complete tutorial series for programming in Python, specifically aimed to suffice simulation and visualization requirements in physics.
+> A complete series for programming in Python aimed to suffice simulation and visualization requirements in Physics.
 
 *Python* is an interpreted, high-level, general-purpose language supporting object-oriented programming with more emphasis on code-readibility and extensibility. 
-It has a wide range of applications ranging from scientific computing libraries like TensorFlow, to stand-alone applications like Blender.
+It has a wide range of applications and is the backbone of widely-used scientific computing libraries like TensorFlow, and even stand-alone applications like Blender.
 
-This tutorial series gradually traverses from basic to advanced topics of Python for scientific computing purposes pertaining to the area of physical sciences. 
-The tutorials and their corresponding notebooks are clubbed into different modules based on the topics covered in the [modules folder](./modules/).
+This series gradually traverses from basic to advanced topics of Python for numerical simulations, deriving analytical expressions and visualizing results pertaining to the area of physical sciences. 
+These tutorials and their corresponding notebooks are clubbed into different modules based on the topics covered in the [modules folder](./modules/).
 
-Currently, the following modules are being updated with time:
+## Beginner-level Topics
+---
 
-- [x] Getting Started
-- [ ] Fundamentals of Python
-- [ ] Visualizing Data
-- [ ] Scientific Computing
-- [ ] Best Practices
-- [ ] Mechine Learning
-- [ ] Deep Learning
-- [x] Quantum Computing
+The course starts with setting up Python with a brief introduction to the ecosystem and the development environment in [*Module 01 - Getting Started*](./modules/m01-getting-started/README.md). 
+Next, the basic data-types of python as well as looping and function representations are covered in [*Module 02 - Fundamentals of Python*](./modules/m02-fundamentals-of-python/README.md).
+[*Module 03 - Visualizing Data*](./modules/m03-visualizing-data/README.md) introduces the powerful library Matplotlib and [*Module 04 - Scientific Computing*](./modules/m04-scientific-computing/README.md) illustrates the working of numerical libraries Numpy and SciPy.
+Finally, usage-oriented practices that speed up and help in debugging are hinted in [*Module 05 - Best Practices*](./modules/m05-best-practices/README.md).
+The individual topics covered in this level are:
+
+Topic ID | Topic Name | Tutorial | Notebook | 
+--- | --- | --- | --- |
+M01T01 | Setting Up Python | [link](./modules/m01-getting-started/m01t01-setting-up-python.md) | |
+M01T02 | The Python Interpreter | [link](./modules/m01-getting-started/m01t02-the-python-interpreter.md) | |
+M01T03 | The Spyder IDE | [link](./modules/m01-getting-started/m01t03-the-spyder-ide.md) | |
+M01T04 | Jupyter Notebooks | [link](./modules/m01-getting-started/m01t04-jupyter-notebooks.md) | |
+M01T05 | Python in VSCode | [link](./modules/m01-getting-started/m01t05-python-in-vscode.md) | |
+M02T01 | Constants and Variables | | |
+M02T02 | Tuples and Lists | | |
+M02T03 | Dictionaries and Sets | | |
+M02T04 | Strings and Formatting | | |
+M02T05 | Conditional Statements | | |
+M02T06 | For and While Loops | | |
+M02T07 | List Comprehension | | |
+M02T08 | Functions and Lambda Expressions | | |
+M03T01 | Matplotlib | | |
+M04T01 | Numpy | | |
+M04T02 | SciPy | | |
+M05T01 | Importing Modules | | |
+M05T02 | Logging Events | | |
+
+## Intermediate-level Topics
+---
+
+With the basics covered, the course jumps into a few more plotting and computational libraries in [*Module 03 - Visualizing Data*](./modules/m03-visualizing-data/README.md) and [*Module 04 - Scientific Computing*](./modules/m04-scientific-computing/README.md).
+Next, [*Module 05 - Best Practices*](./modules/m05-best-practices/README.md) introduces object-oriented programming, exception handling and ways to parallelize the code.
+[*Module 06 - Machine Learning*](./modules/m06-machine-learning/README.md) and [*Module 08 - Quantum Computing*](./modules/m08-quantum-computing/README.md)then highlights two rapidly growing areas, introducing crude learning paradigms and concepts pertaning to quantum computing.
+The individual topics covered in this level are:
+
+Topic ID | Topic Name | Tutorial | Notebook | 
+--- | --- | --- | --- |
+M03T02 | Plotly | | |
+M03T03 | Seaborn | | |
+M04T03 | Pandas | | |
+M04T04 | SymPy | | |
+M05T03 | Objects and Classes | | |
+M05T04 | Handling Scenarios | | |
+M05T05 | Parallelism | | |
+M06T01 | Bayesian Probability | | |
+M06T02 | Regression Analysis | | |
+M06T03 | Classification | | |
+M06T04 | Clustering | | |
+M06T05 | Dimensionality Reduction | | |
+M06T06 | Support Vector Machines | | |
+M08T01 | CBits to QBits | [link](./modules/m08-quantum-computing/m08t01-cbits-to-qbits.md) | |
+M08T02 | Circuits and Gates | | |
+M08T03 | Measurements | | |
+M08T04 | Algorithms | | |
+M08T05 | The Qiskit SDK | [link](./modules/m08-quantum-computing/m08t05-the-qiskit-sdk.md) | [link](./modules/m08-quantum-computing/m08t05-the-qiskit-sdk.ipynb) |
+
+## Advanced-level topics
+---
+
+With all the stages set, the course dives into deep learning in [*Module 07 - Deep Learning*](./modules/m07-deep-learning/README.md) and further engrossing topics in [*Module 08 - Quantum Computing*](./modules/m08-quantum-computing/README.md) and [*Module 09 - Quantum ML*](./modules/m09-quantum-ml/README.md).
+The individual topics covered in this level are:
+
+Topic ID | Topic Name | Tutorial | Notebook | 
+--- | --- | --- | --- |
+M07T01 | Neural Networks | | |
+M07T02 | TensorFlow and Keras | | |
+M07T03 | Principal Component Analysis | | |
+M07T04 | Feedforward Neural Networks | | |
+M07T05 | Recurrent Neural Networks | | |
+M07T06 | Boltzmann Machines and Autoencoders | | |
+M07T07 | Reinforcement Learning | | |
+M08T06 | Grover's Algorithm | | [link](./modules/m08-quantum-computing/m08t06-algorithm-grover.ipynb) |
+M09T01 | Quantum Machine Learning | | |
+M09T02 | Quantum Generative Adversarial Networks | | |

modules/README.md

@@ -1,6 +1,7 @@
 # Modules - Python for Physicists
 
-## [Module 01 - Getting Started](./m01-getting-started/)
+## [Module 01 - Getting Started](./m01-getting-started/README.md)
+---
 
 ### Contents
 
@@ -18,7 +19,8 @@ M01T05 | Python in VSCode | [link](./m01-getting-started/m01t05-python-in-vscode
 * Running Python scripts and IPython notebooks.
 * Using Visual Studio Code to run and debug.
 
-## [Module 02 - Fundamentals of Python](./m02-fundamentals-of-python/)
+## [Module 02 - Fundamentals of Python](./m02-fundamentals-of-python/README.md)
+---
 
 ### Contents
 
@@ -33,7 +35,8 @@ M02T06 | For and While Loops | | |
 M02T07 | List Comprehension | | |
 M02T08 | Functions and Lambda Expressions | | |
 
-## [Module 03 - Visualizing Data](./m03-visualizing-data/)
+## [Module 03 - Visualizing Data](./m03-visualizing-data/README.md)
+---
 
 ### Contents
 
@@ -43,7 +46,8 @@ M03T01 | Matplotlib | | |
 M03T02 | Plotly | | |
 M03T03 | Seaborn | | |
 
-## [Module 04 - Scientific Computing](./m04-scientific-computing/)
+## [Module 04 - Scientific Computing](./m04-scientific-computing/README.md)
+---
 
 ### Contents
 
@@ -54,19 +58,21 @@ M04T02 | SciPy | | |
 M04T03 | Pandas | | |
 M04T04 | SymPy | | |
 
-## [Module 05 - Best Practices](./m05-best-practices/)
+## [Module 05 - Best Practices](./m05-best-practices/README.md)
+---
 
 ### Contents
 
 Topic ID | Topic Name | Tutorial | Notebook | 
 --- | --- | --- | --- |
 M05T01 | Importing Modules | | |
-M05T02 | Objects and Classes | | |
-M05T03 | Handling Scenarios | | |
-M05T04 | Logging Events | | |
+M05T02 | Logging Events | | |
+M05T03 | Objects and Classes | | |
+M05T04 | Handling Scenarios | | |
 M05T05 | Parallelism | | |
 
-## [Module 06 - Machine Learning](./m06-machine-learning/)
+## [Module 06 - Machine Learning](./m06-machine-learning/README.md)
+---
 
 ### Contents
 
@@ -78,9 +84,9 @@ M06T03 | Classification | | |
 M06T04 | Clustering | | |
 M06T05 | Dimensionality Reduction | | |
 M06T06 | Support Vector Machines | | |
-M06T07 | Quantum Machine Learning | | |
 
-## [Module 07 - Deep Learning](./m07-deep-learning/)
+## [Module 07 - Deep Learning](./m07-deep-learning/README.md)
+---
 
 ### Contents
 
@@ -93,13 +99,9 @@ M07T04 | Feedforward Neural Networks | | |
 M07T05 | Recurrent Neural Networks | | |
 M07T06 | Boltzmann Machines and Autoencoders | | |
 M07T07 | Reinforcement Learning | | |
-M07T08 | Quantum Generative Adversarial Networks | | |
 
-### Key Takeaways
-
-* Modelling neural networks.
-
-## [Module 08 - Quantum Computing](./m08-quantum-computing/)
+## [Module 08 - Quantum Computing](./m08-quantum-computing/README.md)
+---
 
 ### Contents
 
@@ -117,3 +119,14 @@ M08T06 | Grover's Algorithm | | [link](./m08-quantum-computing/m08t06-algorithm-
 * Building quantum circuits and implementing quantum algorithms.
 * Executing quantum circuits on IBM's prototype quantum computers.
 
+## [Module 09 - Quantum ML](./m09-quantum-ml/README.md)
+---
+
+### Contents
+
+Topic ID | Topic Name | Tutorial | Notebook | 
+--- | --- | --- | --- |
+M09T01 | Quantum Machine Learning | | |
+M09T02 | Quantum Generative Adversarial Networks | | |
+
+

modules/m01-getting-started/README.md

@@ -3,6 +3,7 @@
 > Stepping into a dark room? Let's take it one step at a time!
 
 ## Tutorials
+---
 
 ### [M01T01 - Setting Up Python](./m01t01-setting-up-python.md)
 
@@ -16,4 +17,4 @@ This tutorial walks through the minimal steps required to install Python via the
 
 ### [M01T05 - Python in VSCode](./m01t04-python-in-vscode.md)
 
-[\[Next\] Module 02 - Fundamentals of Python →](../m02-fundamentals-of-python/)
+[\[Next\] Module 02 - Fundamentals of Python →](../m02-fundamentals-of-python/README.md)

modules/m01-getting-started/m01t01-setting-up-python.md

@@ -8,6 +8,7 @@ Each of them use their own package managers (`pip` and `conda` respectively) to
 Due to some advantages (package dependency analysis and math kernel optimization libraries) of the Anaconda distribution over the official Python one, by default, we will be using the Anaconda distribution unless specifically mentioned otherwise.
 
 ## Installing the Anaconda Individual Edition
+---
 ![Conda](https://img.shields.io/conda/vn/conda-forge/conda?label=version&style=flat-square)
 ![Conda](https://img.shields.io/conda/dn/conda-forge/conda?style=flat-square)
 
@@ -46,6 +47,7 @@ bash path/to/downloaded/file.sh
 ***Installing and initializing conda will override the default `python` command in the terminal with Anaconda's version of Python.***
 
 ## Initializing the conda Environment
+---
 
 *conda-forge* is a community-driven packaging system for conda dedicated for the scientific computing community. It provides a wide collection of recipies, build infrastructure and distributions. 
 To add it as the first-hit channel, open the Anaconda command prompt in the elevated (administrator) mode and execute the following lines:
@@ -76,6 +78,7 @@ Python 3.x.x
 *In Linux systems, the initialization scripts for `conda` are available inside the `$HOME/.bashrc` file.*
 
 ## Creating Python Environments
+---
 
 It is useful to create individual virtual environments to run different versions of Python depending on the requirements of specific packages. 
 And the `conda` package provides an easy way to manage such virtual environments.
@@ -99,6 +102,7 @@ conda env remove --name py38
 ```
 
 ## Managing Anaconda Packages
+---
 
 To install a package, use the following command by replacing `package` with its name and `version` with its requied version:
 

modules/m01-getting-started/m01t02-the-python-interpreter.md

@@ -5,12 +5,13 @@
 [← \[Previous\] M01T01 - Setting Up Python](./m01t01-setting-up-python.md)
 
 ## Using the Interactive Mode
+---
 
 Access the Python interpreter in the *interactive mode* by running `python` in the command line. 
 A welcome message containing the version, current date and time, and a line of default commands will be displayed, followd by a *primary prompt* (`>>>`) in the final line as below:
 
 ```bash
-Python 3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 22:22:21) [MSC v.1916 64 bit (AMD64)] on win32
+Python 3.8.12 (default, Oct 12 2021, 03:01:40) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
 Type "help", "copyright", "credits" or "license" for more information.
 >>>
 ```

modules/m01-getting-started/m01t05-python-in-vscode.md

@@ -5,13 +5,15 @@
 [← \[Previous\] M01T04 - Jupyter Notebooks](./m01t04-jupyter-notebooks.md)
 
 ## Installing Visual Studio Code
+---
 
 [![GitHub package.json version](https://img.shields.io/github/package-json/v/Microsoft/vscode?style=flat-square)](https://github.com/microsoft/vscode)
 
 *Visual Studio Code* (VSCode) is an open-source, highly-customizable and versatile source-code editor developed by Microsoft which supports debugging, version control, syntax highlighting, intelligent code completion, snippets, and code refactoring. 
 Download the appropriate setup file for your operating system (Windows/Linux/MacOSX) from the [official page](https://code.visualstudio.com/download) and install. Easy!
 
 ## Installing Microsoft's Python Extension of VSCode
+---
 
 [![GitHub package.json version](https://img.shields.io/visual-studio-marketplace/v/ms-python.python?style=flat-square)](https://github.com/microsoft/vscode)
 [![GitHub package.json version](https://img.shields.io/visual-studio-marketplace/i/ms-python.python?style=flat-square)](https://github.com/microsoft/vscode)

modules/m02-fundamentals-of-python/README.md

@@ -2,6 +2,6 @@
 
 > Coming soon...
 
-[← \[Previous\] Module 01 - Getting Started](../m01-getting-started/) 
+[← \[Previous\] Module 01 - Getting Started](../m01-getting-started/README.md) 
 
-[\[Next\] Module 03 - Visualizing Data →](../m03-visualizing-data/)
+[\[Next\] Module 03 - Visualizing Data →](../m03-visualizing-data/README.md)

modules/m03-visualizing-data/README.md

@@ -2,6 +2,6 @@
 
 > Coming soon...
 
-[← \[Previous\] Module 02 - Fundamentals of Python](../m02-fundamentals-of-python/) 
+[← \[Previous\] Module 02 - Fundamentals of Python](../m02-fundamentals-of-python/README.md) 
 
-[\[Next\] Module 04 - Scientific Computing →](../m04-scientific-computing/)
+[\[Next\] Module 04 - Scientific Computing →](../m04-scientific-computing/README.md)

modules/m04-scientific-computing/README.md

@@ -2,6 +2,6 @@
 
 > Coming soon...
 
-[← \[Previous\] Module 03 - Visualizing Data](../m03-visualizing-data/) 
+[← \[Previous\] Module 03 - Visualizing Data](../m03-visualizing-data/README.md) 
 
-[\[Next\] Module 05 - Best Practices →](../m05-best-practices/)
+[\[Next\] Module 05 - Best Practices →](../m05-best-practices/README.md)

modules/m04-scientific-computing/m04t03-pandas.md

@@ -0,0 +1,20 @@
+# M04T03 - Pandas
+
+[![Anaconda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
+
+> Shooting towards Data Science!
+
+[`pandas`](https://pandas.pydata.org/) is an open-source Python library that offers analytical tools for datasets to import, clean and process them quickly.
+Originally written in 2008, the name is arguably derived from *Panel Data*, or according to some, *Python Data Analysis Library*.
+Its primary advantages include:
+
+* Extremely streamlined for representing and interpreting of large data.
+* Functions to calculate statistical measures, and to clean or filter data. 
+* Supports Python's native data types as well as NumPy's data arrays.
+* Import from and export to CSV, XLS, HTML, JSON, SQL databases. 
+
+## Getting Started
+---
+
+`pandas` comes with the `anaconda` distribution and relevant commands to set it up can be found in the [package page](https://anaconda.org/anaconda/pandas/).
+It primarily works with data tables containing `DataFrame` objects.