Under Development

This course is currently under development. I am currently pursuing my Executive MBA, so I will develop more lessions when I have time. By the end of Module 3, you will me more than competent to continue learning in your own direction.

100 Days of Python

This exercise is based off the 100 Days of Code framework to teach the basics of python 3. Legacy python will be deprecated on Jan 1, 2020; therefore, this course will be based off the latest version of python.

Why 100 Days of Code?

You first need to ask yourself why you want to learn python. Are you looking for a career change or to strengthen your current prospects? Is it because Glassdoor has ranked Data Scientist as the best job in America since 2016? Along that line, what avenue of programming are you interested? In order to succeed at completing this program, you first need to understand why you want to do it.

Python is an excellent language for software development, web development, automation, data engineering, data analytics, data science, and more. The 100 Days of Code framework is meant to instill a new habit while simultaneously accomplishing your goal. According to a study published in the European Journal of Social Psychology, it takes 66 days on average to start a new habit. The technical fields change extremely rapidly; therefore, it is critical to form the habit of continual learning if you wish to succeed as a programmer. Good luck on your learning journey.

Rules

1. Use these GitHub instructions to learn how to fork the GitHub repo into your own account and how to add git functionality with your IDE.
2. The exercise is segmented in to seven modules that each span 2 weeks. You should expect to spend One Hour each day on average.
3. At the conclusion of each day, modify the log file with the topic, completion date, and what you learned.
4. Upload any created during the day's exercise with the format: moduleX_dayX_topic.py for python scripts or moduleX_dayX_topic.ipynb for Jupyter Notebook files
5. Get into the practice of commenting your code to explain the expected output or function.

Table of Contents

1. Resources
2. Log File
3. Git Instructions
4. Module 1: Foundational Python I
5. Module 2: Foundational Python II
6. Module 3: Automation with Python
7. Module 4: Working with Data
8. Module 5: Introduction to Data Science with Python
9. Module 6: Web Development with Python
10. Module 7: Advanced Python Techniques
11. Module 8: Capstone Project