Introductory and Intermediate Python for Data Science


Instructor: Richard Lawrence

Time: Friday, Apr 5, 12, 19, and 26, 2024 — 10:00AM-4:00PM CT

Location: Blocker 220

Prerequisites: None

This course covers the most important core components of Python programming at the introductory level. Students will learn fundamental programming concepts such as variables, data structures, flow control, and object-oriented programming. Topics and exercises are selected to be relevant for scientific research applications.

This course also covers a selection of scientific programming tools commonly used in Python programming at the intermediate level. Students will learn research techniques such as manipulating and visualizing data, exploring functions, modeling, and retrieving data from the internet. Topics and exercises are selected to be relevant for data science applications. Tools are drawn primarily from the libraries NumPy, SciPy, Matplotlib, and Pandas.

Learning takes place using the Google Colab integrated development environment.

The CC* SWEETER project is supported by a National Science Foundation (NSF) award number 1925764.

Course Materials

Previous Semester Materials


This course focuses, among others, on the following topics:

  • Using Google Colaboratory
  • Comments
  • Data Types
  • Operators
  • Variables
  • Functions
  • Tuples
  • Multi-line Statements
  • Control Structures
  • Loops
  • Conditionals
  • Lists
  • Dictionaries
  • Methods
  • Modules
  • Arrays
  • Data Frames
  • Plotting
  • Data Manipulation

Note: During the class sessions, students will do Python exercises using Google Colaboratory and take quizzes along the way to earn a microcredential.