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CS 89 — Beginning Programming in R

Quarter: Fall
Day(s): Thursdays
Course Format: Live Online (About Formats)
Duration: 10 weeks
Date(s): Sep 29—Dec 8
Time: 7:00—8:50 pm (PT)
Refund Deadline: Oct 1
Units: 2
Tuition: $600
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Registration opens Aug 22, 8:30 am (PT)
Please Note: No class on November 24
DOWNLOAD THE SYLLABUS » (subject to change)
Live Online(About Formats)
7:00—8:50 pm (PT)
Sep 29—Dec 8
10 weeks
Refund Date
Oct 1
2 Units
Mohammad Shokoohi-Yekta
Registration opens Aug 22, 8:30 am (PT)
Please Note: No class on November 24
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 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.

Lead Applied Scientist, Microsoft

Mohammad Shokoohi-Yekta is the coauthor 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.