fullscreen background
Skip to main content

Winter Quarter

Winter Registration Now Open
Most Classes Begin Jan 09
shopping cart icon0

Courses

« Back to Professional & Personal Development

CS 76 W — Introduction to Data Science in R

Quarter: Winter
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Jan 9—Mar 17
Refund Deadline: Jan 12
Units: 2
Tuition: $700
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Open
 
DOWNLOAD THE SYLLABUS » (subject to change)
Winter
Flex Online(About Formats)
Date(s)
Jan 9—Mar 17
10 weeks
Refund Date
Jan 12
2 Units
Fees
$700
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Recording
Yes
Open
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 areas. 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
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 50 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.