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CS 76 W — An Introduction to Data Science in R

Quarter: Summer
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jun 21—Aug 27
Refund Deadline: Jun 24
Units: 2
Tuition: $655
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Registration opens May 17, 8:30 am (PT)
 
DOWNLOAD THE SYLLABUS » (subject to change)
Summer
Flex Online(About Formats)
Date(s)
Jun 21—Aug 27
10 weeks
Refund Date
Jun 24
2 Units
Fees
$655
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Recording
Yes
Registration opens May 17, 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 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 is the author of Applications of Mining Massive Time Series Data and has been a keynote speaker at more than forty 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.