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SCI 01 W — An Introduction to Data Science in R

Quarter: Fall
Course Format: Online (System Requirements)
Duration: 8 weeks
Date(s): Oct 7—Dec 6
Drop Deadline: Oct 10
Unit: 1
Tuition: $645
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Status: Cancelled
Please Note: No class the week of November 25

Online courses have a new refund policy. The full tuition refund deadline for this course is October 10th at 5:00 pm (PT); 50% tuition refund deadline is October 15th at 5:00 pm (PT).
Fall
Date(s)
Oct 7—Dec 6
8 weeks
Drop By
Oct 10
1 Unit
Fees
$645
Instructor(s):
Mohammad Shokoohi-Yekta
Limit
25
Cancelled
Please Note: No class the week of November 25

Online courses have a new refund policy. The full tuition refund deadline for this course is October 10th at 5:00 pm (PT); 50% tuition refund deadline is October 15th at 5:00 pm (PT).
Please note: This course has been rescheduled for the upcoming Winter 2020 quarter. Please check our website on November 18 for full schedule information. Winter Quarter registration opens on Monday, December 2 at 8:30 am (PT).

COURSE DESCRIPTION:

Living in the information age, we are 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.


WHAT MAKES OUR ONLINE COURSES UNIQUE:

  • Course sizes are limited.
    You won't have 5,000 classmates. This course's enrollment is capped at 25 participants.

  • Frequent interaction with the instructor.
    You aren't expected to work through the material alone. Instructors will answer questions and interact with students on the discussion board and through weekly video meetings.

  • Study with a vibrant peer group.
    Stanford Continuing Studies courses attract thoughtful and engaged students who take courses for the love of learning. Students in each course will exchange ideas with one another through easy-to-use message boards as well as optional weekly real-time video conferences.

  • Direct feedback from the instructor.
    Instructors will review and offer feedback on assignment submissions. Students are not required to turn in assignments, but for those who do, their work is graded by the instructor.

  • Courses offer the flexibility to participate on your own schedule.
    Course work is completed on a weekly basis when you have the time. You can log in and participate in the class whenever it's convenient for you. If you can’t attend the weekly video meetings, the sessions are always recorded for you and your instructor is just an email away.

  • This course is offered through Stanford Continuing Studies.
    To learn more about the program, visit our About Us page. For more information on the online format, please visit the FAQ page.

No computer science experience is necessary.

Mohammad Shokoohi-Yekta, Senior Data & Applied Scientist, Microsoft; AI Director, Bioxytech Retina, Inc.

Mohammad Shokoohi-Yekta was a senior data scientist at Apple for several years. Earlier, he worked for Samsung, Bosch, General Electric Research, and UCLA. He received a PhD in computer science from UC Riverside. He is the author of Applications of Mining Massive Time Series Data and has been a keynote speaker at more than thirty data summits and conferences around the world.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.