CSE 357, Fall 2025: Statistical Methods for Data Science

News:
08/13: Piazza course sign-up link
08/13: Course website up.

CSE 357: Statistical Methods for Data Science
Fall 2025


When: Tu Th, 12:30pm - 1:50pm
Where: Old CS 2120

Instructor: Anshul Gandhi
Instructor Office Hours: Tu Th 2-3pm, NCS 357

Course TAs: Dilan, Emilia
TA Office Hours: Wed, 4:30-5:30pm, Old CS 2126

Course Description

This interdisciplinary course introduces the mathematical concepts required to interpret results and subsequently draw conclusions from data in an applied manner. The course presents different techniques for applied statistical inference and data analysis, including their implementation in Python, such as parameter and distribution estimators, hypothesis testing, Bayesian inference, and likelihood.

More informally, this 3-credit, undergraduate-level course covers probability and statistics topics required for data scientists to analyze and interpret data. The course will involve theoretical topics and some programming assignments. The course is targeted primarily for junior and senior undergraduate students who are comfortable with concepts relating to probability and are comfortable with basic programming. Undergraduates from Computer Science, Applied Mathematics and Statistics, and Electrical and Computer Engineering would be well suited for taking this class. Topics covered include Probability Theory, Random Variables, Stochastic Processes, Statistical Inference, Hypothesis Testing, and Regression. For more details, refer to the syllabus below.

The class is in-person, and is expected to be interactive and students are encouraged to participate in class discussions.

Grading will be on a curve, and will be based primarily on assignments and exams. For more details, refer to the section on grading below.

Prerequisites: C or higher in CSE 214; AMS 310; CSE or DAS major. See Bulletin for definitive information. Comfort in probability theory and proficiency with Python (since programming assignments tasks will be in Python) will be helpful.

Learning Objectives:An understanding of core concepts of probability theory and standard statistical techniques. An understanding of random variables, distributions, and hypothesis testing. An ability to apply quantitative research methods (correlation and regression), and modern techniques of optimization and machine learning such as clustering and prediction.

Syllabus & Schedule

Date Topic Readings Notes
Aug 26 (Tu)
[Lec 01]
Course introduction, class logistics
Aug 28 (Th)
[Lec 02]
Probability review - 1
  • Basics: sample space, outcomes, probability
  • Events: mutually exclusive, independent
  • Calculating probability: sets, counting, tree diagram
  • AoS 1.1 - 1.5
    MHB 3.1 - 3.4
    assignment 1 out, due Sep 8th
    Sep 02 (Tu)
    [Lec 03]
    Probability review - 2
  • Conditional probability
  • Law of total probability
  • Bayes' theorem
  • AoS 1.6, 1.7
    MHB 3.3 - 3.6
    Sep 04 (Th)
    [Lec 04]
    Random variables - 1
  • Mean, Moments, Variance
  • pmf, pdf, cdf
  • Bernoulli(p)
  • Indicator RV
  • Binomial(n, p)
  • Geometric(p)
  • AoS 2.1 - 2.3, 3.1 - 3.4
    MHB 3.7 - 3.9
    Python scripts:
    draw_Bernoulli, draw_Binomial, draw_Geometric


    Resources

    Grading (tentative)

    Academic Integrity Statement

    Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Professions, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at http://www.stonybrook.edu/commcms/academic_integrity/index.html.

    Critical Incident Management

    Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of Student Conduct and Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook.

    Student Accessibility Support Center Statement

    If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, Stony Brook Union Suite 107, (631) 632-6748, or at sasc@stonybrook.edu. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential.
     Please report any errors to the Instructor.