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Python for Data Analysis

A required course in the Python for Data Science, Web and Core Programming Specialized Studies Program.

Course closed to new registrations: Call ( 949 ) 824-5414 for more information or sign up below to be notified when this course becomes available.×

Course Description

Python for Data Analysis is a course for students with some experience using Python who want to learn how to import and analyze data using the popular programming language. Students can immediately use what they have learned to ingest data, produce plots and analysis, and fit models. Note that not everything with-in the python language will be covered (such as user interfaces, web services, and object oriented programming). The main python libraries introduced will be numpy, matplotlib, pandas, and scikit-learn. Major topics include: how to import data and manipulate it efficiently using numpy, how to produce plots and data visualizations with matplotlib, how to run statistical analysis using pandas, and how to build predictive models scikit-learn. A final project will help to tie the main concepts together. Additional topics include: how to use eclipse, a very handy development environment! Prerequisites: I&C SCI X426.64 Introduction to Programming with Python; Knowledge of Python programming is required.

Prerequisites: I&C SCI X426.64 Introduction to Programming with Python; Knowledge of Python programming is required.

NOTE: This course may have live sessions via Zoom. A device with audio and visual will be needed to participate. The following student guide provides additional resources/information on how to use and access your courses Zoom sessions.

  • Be notified when this class becomes available!

  • Details
  • $795
  • April 04, 2022 to May 08, 2022
  • Delivery Mode: Online
  • Reg#: 00238
  • ID/Units: I&C SCI X426.62  (1.50)
    ( Section 1 )
  • Quarter: SPRING 2022


Yu Zhang, Ph.D., has over 5 years of experience of in python programming and uses the big query SQL, python colab, and Tableau in previous and current work. She has over 5 years of experience in academic in statistical analysis and 3 years of experience in industry and government working on complex data and machine learning projects. She is technically sound with full experience in data validation and machine learning modeling. In addition, she has over 6 year’s teaching experience as a lecturer, teaching assistant, and mentor working in UC Santa Barbara, UC Davis, UC Irvine. She is passionate about using data analytics for real-world problem solving and working with diverse students.

Textbook Information

Textbooks for your course may be purchased from any vendor or bookseller of your choice.

No textbook information is available for this course.

Meeting Schedule

EventDateDayStart TimeEnd TimeLocationRoom
START04/04/2022Monday------Online (Access Begins)---
OL-LEC04/06/2022Wednesday7:00 PM8:00 PMZoom---
OL-LEC04/13/2022Wednesday7:00 PM8:00 PMZoom---
OL-LEC04/20/2022Wednesday7:00 PM8:00 PMZoom---
OL-LEC04/27/2022Wednesday7:00 PM8:00 PMZoom---
OL-LEC05/04/2022Wednesday7:00 PM8:00 PMZoom---
END05/08/2022Sunday------Online (Access Ends)---