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SCI 01 B — An Introduction to Data Science

Quarter: Spring
Day(s): Thursdays
Course Format: On-campus course
Duration: 8 weeks
Date(s): Apr 13—Jun 1
Time: 7:00—8:50 pm
Drop Deadline: Apr 26
Unit: 1
Tuition: $475
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Spring
On-campus course
Thursdays
7:00—8:50 pm
Date(s)
Apr 13—Jun 1
8 weeks
Drop By
Apr 26
1 Unit
Fees
$475
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Closed
Living in the information age, we find ourselves 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: 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). 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. The course will include lectures and handson, interactive problem-solving. Examples will come from real-world problems in weather, marketing, biology, stocks, neuroscience, medicine, and other disciplines.

No computer science experience is necessary. Students are required to bring a laptop computer to class.

This course covers the same content as SCI 01 A.

Mohammad Shokoohi-Yekta, Data Scientist, Apple

Mohammad Shokoohi-Yekta is a data scientist on the Antenna Design team at Apple. Prior to joining Apple, he worked for Samsung, Bosch, General Electric, and UCLA, and taught at UC Riverside and Cal Poly Pomona. He received a PhD in computer science from UC Riverside and is the author of Applications of Mining Massive Time Series Data.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)