Main Content
Data is changing the way our society makes decisions. Data scientists design and develop solutions that help solve complex problems posed by the three Vs of data: volume, variety and velocity.
Students in the Data Science Certificate program learn the fundamentals of how to store, manage, analyze, search, and model data.
Who should register
Working professionals with experience in the field of data analysis as well as young professionals seeking a career in data science who have:
- Experience with programming (must) and data analysis (optional)
- College-level basics of statistics, probability theory, linear algebra, calculus (Note: Although we will not thoroughly teach them again, we will review them when they are used)
Learning format
In this program with three modules, one per quarter, we examine concepts, elements, strategies, and skills covering the general discipline of data science. The instruction is based on practical examples of data science applications in a variety of areas within the industry as well as in academic research.
Building on theoretical foundations from lectures, exercises, discussions and quizzes, students gain hands-on experience through workshops/tutorials with related independent assignments. The series culminates in milestone projects that demonstrate comprehensive data science skills.
See Program courses for details.
Technology requirements
Students should have:
- A computer with a recent OS and a current web browser with at least 4GB and preferably 8GB of RAM
- High-speed internet access
- A Google account with Colab access
- Headset and webcam (recommended)
Instructor
Juhua Hu, Ph.D., Assistant Professor of Computer Science & Systems
Juhua Hu is also the director of Center for Data Science at the University of Washington Tacoma.
Quick info
Recent Info SessionJuly 26th, 2024: 12:30pm-1:00pm, recording Contact usDr. Juhua Hu (juhuah@uw.edu) |
Program Courses
Upcoming session: Autumn 2024 cohort
Data Science Life Cycle
Wednesdays, September 25 to December 4
Learn to formulate a problem, design data for the problem, visualize and analyze data, and conduct prediction/inference.
No class on November 27
CEUs: 3 (upon successful completion of the course)
Machine Learning
Wednesdays, January 8 to March 12
Learn state-of-the-art machine learning technologies to turn data into actionable insights.
CEUs: 3 (upon successful completion of the course)
Big Data
Wednesdays, April 2 to June 4
Understand distributed data management and computing technologies.
CEUs: 3 (upon successful completion of the course)