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CS 76 W — Introduction to Data Science in R

Quarter: Winter
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jan 10—Mar 18
Refund Deadline: Jan 13
Units: 2
Tuition: $755
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Registration opens Nov 29, 8:30 am (PT)
 
DOWNLOAD THE SYLLABUS » (subject to change)
Winter
Flex Online(About Formats)
Date(s)
Jan 10—Mar 18
10 weeks
Refund Date
Jan 13
2 Units
Fees
$755
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Recording
Yes
Registration opens Nov 29, 8:30 am (PT)
DOWNLOAD THE SYLLABUS » (subject to change)
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 four main topics in data science: basic programming in R (a programming language that is widely used for data analysis), analysis and visualization of data, applied machine learning, and introductory deep learning topics. 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, retail, 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, save lives, or boost their businesses.

No computer science experience is necessary.

MOHAMMAD SHOKOOHI-YEKTA
Lead Applied Scientist, Microsoft

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

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.