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SCI 01 W — An Introduction to Data Science and Machine Learning

Quarter: Fall
Course Format: Flex Online (About Formats)
Duration: 8 weeks
Date(s): Oct 5—Dec 4
Drop Deadline: Oct 8
Unit: 1
Tuition: $645
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Status: Closed
Please Note: No class the week of November 23. In addition, some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
Fall
Flex Online(About Formats)
Date(s)
Oct 5—Dec 4
8 weeks
Drop By
Oct 8
1 Unit
Fees
$645
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Closed
Please Note: No class the week of November 23. In addition, some of our refund deadlines have changed. See this course's drop deadline above and click here for the full policy.
Living in the information age, we are surrounded and overwhelmed by data, making it imperative for us to find ways to identify the data we need, classify and organize it, and draw conclusions from it. Data science is a very practical discipline with many applications in business, science, and government, such as targeted marketing, web analysis, disease diagnosis and outcome prediction, weather forecasting, credit risk and loan approval, customer relationship modeling, and fraud detection. This course presents a high-level overview of three main topics in data science: basic analysis and visualization of data, introductory machine learning concepts, and basic programming in R (a programming language that is widely used for data analysis). The course will include lectures and hands-on, interactive problem-solving. Examples will come from real-world problems in weather, marketing, biology, stocks, neuroscience, medicine, and other disciplines. By the end of the course, students will be able to apply data science techniques to real-world applications in order to draw meaningful conclusions.

No computer science experience is necessary.

Mohammad Shokoohi-Yekta, Senior Data and Applied Scientist, Microsoft

Mohammad Shokoohi-Yekta received a PhD in computer science from UC Riverside. He was a data scientist at Apple and earlier worked for Samsung, Bosch, GE, and UCLA Research Labs. He is the author of Applications of Mining Massive Time Series Data. He has also been a keynote speaker at more than forty data summits and conferences around the world.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)