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CS 89 — Beginning Programming in R

Quarter: Fall
Day(s): Tuesdays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Sep 28—Dec 7
Time: 7:00—8:50 pm (PT)
Refund Deadline: Sep 30
Units: 2
Tuition: $560
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Open
Please Note: No class on November 23
DOWNLOAD THE SYLLABUS » (subject to change)
Fall
Live Online(About Formats)
Tuesdays
7:00—8:50 pm (PT)
Date(s)
Sep 28—Dec 7
10 weeks
Refund Date
Sep 30
2 Units
Fees
$560
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Recording
Yes
Open
Please Note: No class on November 23
DOWNLOAD THE SYLLABUS » (subject to change)
R is one of today's most powerful and hottest tools for data science and machine learning applications. This open source tool is widely used and beloved by users in both industry and academia. In this course, students will learn the fundamental programming concepts, problem-solving techniques, and most useful syntax and data structures in R. This is a very interactive and hands-on course; students will learn how to get their hands dirty on code. During the live sessions, we will do coding together and solve a great many exciting real problems. Inside and outside of class, the instructor will help each student to run and debug their code successfully. In addition to programming skill sets, we will discuss data processing and visualization techniques in R, and explore some of the most common toolsets and packages being used for this purpose such as ggplot2, Plotly, dplyr, Shiny, and others. We will also have multiple fireside chats to discuss real-world applications of R in industry and the AI world. After completing this course, you will understand how to break down complex problems, design, implement, optimize, and visualize your results in R.

No computer science experience is necessary.

MOHAMMAD SHOKOOHI-YEKTA
Tech Lead & Senior 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.