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SCI 01 B — An Introduction to Data Science

Quarter: Spring
Day(s): Fridays
Course Format: On-campus
Duration: 8 weeks
Date(s): Apr 5—May 24
Time: 7:00—8:50 pm
Drop Deadline: Apr 18
Unit: 1
Tuition: $550
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 40
Spring
On-campus
Fridays
7:00—8:50 pm
Date(s)
Apr 5—May 24
8 weeks
Drop By
Apr 18
1 Unit
Fees
$550
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
40
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 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. Please note: Section A and Section B of “An Introduction to Data Science” cover the same content.

Mohammad Shokoohi-Yekta, Senior Data Scientist

Mohammad Shokoohi-Yekta is the co-founder of MedicalBlockchain.ai. Recently, he worked at Apple as a senior data scientist for four years. Before Apple, he worked for Samsung, Bosch, General Electric Research, and UCLA on predictive modeling projects. He received a PhD in computer science from the University of California, Riverside. He is the author of Applications of Mining Massive Time Series Data. He has also been a keynote speaker at more than thirty data summits and conferences around the globe.

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)