This four-part workshop introduces the core Python skills needed for data science — from loading raw data to building a basic machine learning model.

You will learn how to clean, explore, visualize, and analyze data using Python. By the end of the workshop, you will understand the complete workflow: data preprocessing → exploratory analysis → model building → evaluation.

Many people use AI tools to generate Python code. In this workshop, you will learn the fundamentals behind that code so you can understand it, check for errors, and use it confidently.

The workshop meets once a week on Tuesdays for four weeks. No prior Python experience is required. Participation is free, but registration is required.

We will use Google Colab, a free cloud-based platform for Python coding. If you are new to Colab, I recommend attending the introductory workshop first: https://calendar.lib.olemiss.edu/event/16040287

Weekly Topics

Week 1: Getting Started with Python

• Load data

• Inspect and summarize data

• Create simple visualizations

Week 2: Data Cleaning & Preparation

• Handle missing values

• Filter and rename data

• Create new features

• Detect outliers

Week 3: Exploring the Data (EDA)

• Descriptive statistics

• Grouping and aggregation

• Visual exploration

• Interpreting results

Week 4: Introduction to Machine Learning

• Split data into training and test sets

• Train a simple model

• Evaluate performance

• Understand feature importance

• k-fold cross-validation

Contact

For questions, please contact:
Dr. Sian Lee
slee63@olemiss.edu