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.
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.