Course Summary

This course is designed for absolute beginners to learn the Python programming language. The goal is to build good learning habits in small steps so you can become comfortable programming on your own to tackle data science problems either for personal projects or jobs.

Tentative Course Curriculum

Work in Progress
1 Lesson
- TBA
  Start
Why Python
1 Lesson
- Why Python?
  Start
Creating Python Environment
3 Lessons
- Installing Anaconda Python system
  Start
- Introduction to Google Colab (browser-based Python, no installation needed)
  Start
- Introduction to other browser-based Python systems
  Start
Python Fundamentals
5 Lessons
- Computer science basics (bits, bytes, memory)
  Start
- Built in Data structures (list, tuple, dictionary, set)
  Start
- Functional Programming concepts and applications
  Start
- Object Oriented Programming concepts and applications
  Start
- Understanding the language from a data scientist's perspective (not from a software engineer's perspective)
  Start
Numpy
2 Lessons
- Array
  Start
- Vectorization
  Start
Pandas
3 Lessons
- Handling external data
  Start
- Data cleaning
  Start
- Data transformation (join, reshape)
  Start
Data Visualization
3 Lessons
- With Matplotlib
  Start
- With Seaborn
  Start
- With plotnine
  Start
Statsmodel
5 Lessons
- Statistical computation
  Start
- Summary tables
  Start
- Linear Regression Analysis
  Start
- Regression diagnostics
  Start
- Logistic regression analysis
  Start
scikit-learn
2 Lessons
- Fitting regression models using scikit-learn
  Start
- Fitting logistic regression for classification
  Start
Simulation and Computation
4 Lessons
- Random number generation
  Start
- Plotting probability distributions
  Start
- Understanding confidence intervals using Monte Carlo simulation
  Start
- More examples involving simulation
  Start