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SCI 01 — An Introduction to Data Science

Quarter: Winter
Day(s): Thursdays
Course Format: On-campus course
Duration: 8 weeks
Date(s): Jan 18—Mar 8
Time: 7:00—8:50 pm
Drop Deadline: Jan 31
Unit: 1
Tuition: $565
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Status: Registration opens on 12/04/2017
Winter
On-campus course
Thursdays
7:00—8:50 pm
Date(s)
Jan 18—Mar 8
8 weeks
Drop By
Jan 31
1 Unit
Fees
$565
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Registration opens on 12/04/2017
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 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. Students are required to bring a fully charged laptop computer to class.

Mohammad Shokoohi-Yekta, Data Scientist, Apple

Mohammad Shokoohi-Yekta works 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. He is the author of the book 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)