Method
Live-Online
Term
WINTER
Units
3.0 QUARTER UNITS
Cost
$980

Skills you will gain

  • Advanced Python Programming: Develop functions and scripts for complex calculations in engineering, finance, and scientific fields.
  • Data Management & Analysis: Manipulate, merge, clean, and transform data using Python libraries like Pandas and NumPy.
  • Machine Learning Workflow: Manage end-to-end machine learning processes, from data preparation to model optimization.
  • Statistical & Predictive Modeling: Apply statistical analysis and predictive modeling techniques with Python’s core libraries.
  • Real-World Project Experience: Gain practical, hands-on experience through a complete machine learning project.

Course Description

Python is the language of modern data science. This course teaches data manipulation, statistical analysis, and predictive modeling using NumPy, Pandas, scikit-learn, and Matplotlib, covering supervised and unsupervised learning techniques. You'll finish with a real, end-to-end machine learning project under your belt.

Syllabus Library
You may enroll in this offering at any time.
Sara Saatchi is an instructor for UCSC Extension Technology Courses.
Sara Saatchi
Jan. 8 - Mar. 12, Fri, 5:30pm - 8:30pm
  • Live-Online Attend via Zoom at scheduled times.
DBDA.X427.(21)
Schedule
Date Start Time End Time Meeting Type Location
Fri, 01-08-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 01-15-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 01-22-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 01-29-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 02-05-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 02-12-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 02-19-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 02-26-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 03-05-2027 5:30pm 8:30pm Live-Online REMOTE
Fri, 03-12-2027 5:30pm 8:30pm Live-Online REMOTE
 

This class is offered in an online synchronous format. Students are expected to log into this course via Canvas at the start time of scheduled meetings and participate via Zoom, for the duration of each scheduled class meeting.

To see all meeting dates, click “Full Schedule” below.

You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.

Required Tools & Materials: 

Students are expected to have computers with Python 3.x, Jupyter Notebooks, and libraries: Pandas, Matplotlib and Numpy installed. Installing the Anaconda distribution of Python, gives access to Jupyter Notebooks and all the required libraries. Free Individual Edition can be obtained from: https://www.anaconda.com/products/individual

Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems; Aurélien Géron; O'Reilly Media Inc.; 2025. ISBN: 979-8341607989

Recommended Textbooks: 

Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/ 

Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690. Available at: https://learning.oreilly.com/library/view/machine-learning-with/9781098…;

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, O'Reilly Media, Inc., 2022-10-04, ISBN: 9781098122478.   
Students can use this textbook as an alternative to the required textbook. Available at O'Reilly for Public Libraries.

This course applies to these programs:

Demo