Probability & Statistics for Computing

Course Description

Probability & Statistics for Computing gives students the statistical toolkit that later machine learning, data systems, and empirical research coursework assumes: probability distributions, estimation, hypothesis testing, and enough statistical skepticism to question a dataset before trusting it.

Learning Outcomes

Apply core probability concepts to computing problems.
Work with common probability distributions and their properties.
Perform basic statistical estimation and hypothesis testing.
Critically evaluate claims drawn from a dataset or experiment.

Weekly Topics

Examines probability foundations from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend random variables, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on discrete distributions with an in-class exercise students carry into the week's assignment.

Focuses on common mistakes and misconceptions around continuous distributions, using student work from the previous assignment as material.

Introduces expectation & variance and immediately puts it to use in a small design or implementation task.

Situates joint distributions within the broader arc of the course, showing how it connects to what comes next.

Uses a guest dataset or scenario to explore mid-semester review in a setting closer to professional practice.

Introduces estimation through short lectures and worked examples, building the vocabulary the rest of the course relies on.

Works through confidence intervals in a lab-driven session, with guided exercises students complete and discuss in small groups.

Covers hypothesis testing in depth, connecting the underlying theory to a concrete example the class builds together.

Examines correlation & regression from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend applied data analysis, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on revision with an in-class exercise students carry into the week's assignment.

Assessment

Assignments — 20%

Practical Work — 15%

Mid-Semester — 25%

Final Examination — 40%

Prerequisite Map

Related Courses

Core300 Level

CSC305

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Sofia Reyes 4 cr