Python for Data Science

July 29 - 31, 2025
8:00am - 4:00pm

Location:

Center of Higher Learning Computer Lab / Building 1103 - Room 1005 / Stennis Space Center, MS

Course Information:

The Python for Data Science course teaches the fundamentals of Python for data analysis and visualization. Participants will work with key libraries like Pandas, NumPy, Matplotlib, and Seaborn to clean, transform, and analyze data. They will create interactive visualizations to communicate insights effectively and apply their skills through hands-on projects using Jupyter Notebook and real-world datasets.

Costs:

$1300 per person for a 3-day training course

 Prerequisites:

A working knowledge of Python to the level of Introduction to Python Training

Course Objectives:

  • Learn the fundamentals for Python for data analysis and visualization

  • Work with key libraries like Pandas, NumPy, Matplotlib, and Seaborn

  • Understand and create effective visualizations

  • Apply skills through hands-on projects using Jupyter Notebook and real-world datasets


Course Outline:

  • Module 1: Introduction to Python for Data Science

    • In this module, you will learn about the following:

      • Overview of Python and its role in data science

      • Setting up Python environments (Anaconda, Jupyter Notebooks)

      • Writing and running Python scripts

  • Module 2: Working with Jupyter Notebooks

    • In this module, you will learn about the following:

      • Introduction to Jupyter Notebooks

      • Markdown and code cells

      • Running, saving and sharing notebooks

  • Module 3: Numerical Computing and NumPy

    • In this module, you will learn about the following: 

      • Understanding arrays and their advantages

      • Creating and manipulating NumPy arrays

      • Mathematical operations and broadcasting

  • Module 4: Data Manipulation and Pandas

    • In this module, you will learn about the following: 

      • Understanding Series and DataFrames

      • Importing and exploring datasets

      • Filtering, sorting and transforming data

  • Module 5: Data Input and Output (I/O)

    • In this module, you will learn about the following: 

      • Reading and writing NetCDF and HDF5 files

      • Reading and writing Excel files

      • Working with CSV files

      • Connecting and querying SQL databases

  • Module 6: Converting Datasets to Pandas DataFrames

    • In this module, you will learn about the following: 

      • Transforming structured and unstructured data

      • Importing datasets from APIs and web sources

  • Module 7: Advanced Data Handling

    • In this module, you will learn about the following:

      • Altering specific data using custom functions

      • Handling missing data - filling, dropping and imputing values

      • Aggregating data using group operations

  • Module 8: Data Visualization with Matplotlib

    • In this module, you will learn about the following:

      • Creating fully customizable plots

      • Implementing custom figures and axis

      • Adding labels, legends and annotations

  • Module 9: Statistical Data Visualization with Seaborn

    • In this module, you will learn about the following:

      • Creating scatter plots

      • Generating distribution plots

      • Visualizing summary statistics with box plots

  • Module 10: Hands-on Projects and Real-World Applications

    • In this module, you will learn about the following:

      • Data analysis case studies

      • End-to-end data science project

      • Best practices for working with large datasets


  • Registration:

Seating is limited! To register, contact Ashley McGinty at Ashley.n.west@usm.edu or 228-688-3170. The deadline for registration is July 11, 2025.