Python for Data Science

This page contains links to html renders of Jupyter notebooks that I developed in Fall 2019 for use in the following courses offered at Maryville University:

• DSCI 303 - Introduction Python
• DSCI 503 - Python
• COSC 130 - Introduction Programming

Each of these three courses includes an introduction to general programming in Python. The courses DSCI 303 and DSCI 503 also cover applications of Python in scientific computing, statistics, machine learning, data analysis, and data visualization.

Disclaimer: These notebooks are not intended to be a standalone resource for learning Python. These were created to supplement lecture presentations.

• 01 - Intro to Programming
• 02 - Intro to Python
• 03 - Intro to Jupyter
• 04 - Arithmetic and Variables
• 05 - Strings
• 06 - Introduction to Lists
• 07 - Additional List Topics
• 08 - For Loops
• 09 - Boolean Variables
• 10 - Conditional Statements
• 11 - While Loops
• 12 - Dictionaries
• 13 - Working with Text Files
• 14 - Introduction to Functions
• 15 - Variable Scope
• 16 - Recursion
• 17 - Packages
• 18 - Classes
• 19 - Plotting with Matplotlib
• 20 - NumPy
• 21 - Random Number Generation
• 22 - 2D Arrays
• 23 - Pandas Dataframes
• 24 - Grouping and Aggregation
• 25 - Introduction to Machine Learning
• 26 - Introduction to Scikit-Learn
• 27 - Classification Metrics
• 28 - Loss Functions
• 29 - Linear Regression
• 30 - Logistic Regression
• 31 - Decision Tree Classifier
• 32 - Random Forests
• 33 - One-Hot Encoding
• 34 - Breast Cancer
• 35 - Titanic Dataset
• 36 - Cross Validation
• 37 - Grid Search