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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.
 

Learner Outcomes

  • 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

Introduction to Programming or demonstration of equivalency.
 

Notes

You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.

Software: Anaconda, Visual Studio Code, Jupyter Notebook.
Hardware: Computer with Windows or Mac OS preferred.

Applies Towards the Following Certificates

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Type
Online
Dates
Jun 28, 2021 to Sep 10, 2021
Delivery Options
Online on Demand  
Course Fee(s)
Course Fee credit (3 units) $725.00
Potential Discount(s)
Available for Credit
3 units
Instructors
Section Notes
 


Steps to Enroll
 


How to Access Your Online Course:

 

One business day after enrollment but no sooner than 2 weeks before the beginning of the course, you will receive an email with detailed instructions on how to access your online course. You don’t need to take any action until you receive that email. Please note that you will not be able to access your online course until all the steps highlighted in that email are complete.
 


Required Textbook(s): None
 

No refunds after 7/5/21