Elements of Data Science and Statistical Learning with R

Harvard Extension School

CSCI E-63C

Section 1

CRN 24748

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One of the broad goals of data science is examining raw data with the purpose of identifying its structure and trends, and of deriving conclusions and hypotheses from it. In the modern world awash with data, data analytics is more important than ever to fields ranging from biomedical research, space and weather science, finance, business operations and production, to marketing and social media applications. This course introduces various statistical learning methods and their applications. The R programming language, a very popular and powerful platform for scientific and statistical analysis and visualization, is introduced and used throughout the course. We discuss the fundamentals of statistical testing and learning, and cover topics of linear and non-linear regression, clustering and classification, support vector machines, and decision trees. The datasets used in the examples are drawn from diverse domains such as finance, genomics, and customer sales and survey data.

Instructor Info

Andrey Sivachenko, PhD

Scientist IV, Head of Bioinformatics, Cystic Fibrosis Foundation Therapeutics Lab


Victor A Farutin, PhD

Senior Director, Computational Biology, Verve Therapeutics


Meeting Info

Th 7:40pm - 9:40pm (1/27 - 5/17)

Participation Option: Online Asynchronous or Online Synchronous

In online asynchronous courses, you are not required to attend class at a particular time. Instead you can complete the course work on your own schedule each week.

Deadlines

Last day to register: January 23, 2025

Additional Time Commitments

Optional sections to be arranged.

Prerequisites

Good programming skills, preferably in R or solid experience in other languages; good understanding of probability and statistics at the level of CSCI E-106 or STAT E-109. See the syllabus for the recommended pretest.

Notes

This course meets via web conference. Students may attend at the scheduled meeting time or watch recorded sessions asynchronously. Recorded sessions are typically available within a few hours of the end of class and no later than the following business day.

Syllabus

All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
15123 1 Online Asynchronous, Online Synchronous Team Taught Open T 8:10pm - 10:10pm
Sep 3 to Dec 21
34799 1 Online Asynchronous, Online Synchronous Andrey Sivachenko Open TTh 6:30pm - 9:30pm
Jun 24 to Aug 9
24748 1 Online Asynchronous, Online Synchronous Team Taught Open Th 7:40pm - 9:40pm
Jan 27 to May 17