Introduction to Probability for Engineering and Data Science

Harvard Summer School

ENSC S-138

Section 1

CRN 34796

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This course introduces students to probability theory and statistics, and their applications in engineering and data science. Topics include random variables, distributions and densities, conditional expectations, statistical sampling, limit theorems, and Markov chains. The goal of this course is to prepare students with knowledge of probability theory and statistical methods that are widely used in several engineering disciplines and modern data science.

Registration Closes: June 20, 2024
Credits: 4

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Term

Summer Term 2024

Part of Term

Full Term

Format

Live Attendance Web Conference

Credit Status

Graduate, Noncredit, Undergraduate

Section Status

Open

Instructor Info

ENSC S-138: Introduction to Probability for Engineering and Data Science, PhD

Gordon McKay Professor of Electrical Engineering and of Applied Mathematics, Harvard University


Meeting Info

MW 6:30pm - 9:30pm (6/24 - 8/9)

Participation Option: Online Synchronous

Deadlines

Last day to register: June 20, 2024

Prerequisites

Mathematical knowledge at the level of MATH S-1a and MATH S-1b (set theory, Venn diagrams, basic algebra, basic differential and integral calculus, and matrices). Basic programming skills (in languages like Python or R) are optional but can be useful.

Notes

This course meets via web conference. Students must attend and participate at the scheduled meeting time. Open to admitted Secondary School Program students by petition. Harvard College students: This course is eligible for degree credit, but see important policy information.

Syllabus

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Tuition

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All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
34796 1 Online Synchronous Yue Lu Open MW 6:30pm - 9:30pm
 Jun 24 to Aug 9

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