Loading...

Course Description

In this course, you will learn the rich set of tools, libraries, and packages that comprise the highly popular and practical Python data analysis ecosystem. This course is primarily taught via screen-sharing programming videos. Topics taught range from basic Python syntax all the way to more advanced topics like supervised and unsupervised machine learning techniques.

 Prior knowledge of the Python language is required for this course. Students should have completed Intro to Programming (Python) or have equivalent knowledge before taking this course.

Learner Outcomes

  • Installing Python/Jupyter/IPython on Windows and Mac
  • Python Basics (variables, strings, simple math, conditional logic, for loops, lists, tuples, dictionaries, etc.)
  • Using the Pandas library to manipulate data (filtering and sorting data, combining files, GroupBy, etc.)
  • Plotting data in Python using Matplotlib and Seaborn
  • Logistic Regression using Scikit-Learn
  • Classification and Regression Metrics
  • Decision Trees using Scikit-Learn
  • Random Forests (Scikit-Learn)
  • Clustering Algorithms (K-Means, Hierarchical Clustering)

Prerequisites

Prior knowledge of the Python language is required for this course. Students should have completed Introduction to Programming or have equivalent knowledge before taking this course.

Applies Towards the Following Certificates

Loading...

Enroll Now - Select a section to enroll in

Type
Online
Dates
Oct 05, 2026 to Dec 11, 2026
Delivery Options
Online Asynchronous  
Course Fee(s)
Course Fee credit (3 units) $750.00
Available for Credit
3 units
Instructors

Section Notes

No refunds after 10/4/26
A full refund is given (less the $40 non-refundable administrative fee per course) if notice is received 5 days or more before the start date of the course. A 50% refund is given (less the $40 non-refundable administrative fee per course) if notice is received 1–4 days before the start date of the course. No refunds are given after the course begins. See the full Drop Policy here.
 

Prerequisite: Prior knowledge of the Python language is required for this course. Students should have completed Introduction to Programming or have equivalent knowledge before taking this course.