CS 122 - Introduction to Statistical Analysis and Experimentation

4.0 Credits
Apply statistical techniques to datasets to produce useful and non-biased results. Topics include probability theory, central tendency, characterizing distribution types, hypothesis testing, statistical significance and cognitive bias.
Prerequisite Admission into the Data Analytics Certificate (Beginner/Early Career) program or permission from the instructor.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:

Demonstrate the application of the basics of probability theory and apply conditional probability and Bayes’ Theorem to determine likelihood of outcomes.
Define concepts in Central Tendency, and examine its applications in interpreting data skewness and kurtosis.
Illustrate how to describe data and its variability through summary statistics such as mean, median, variance and quantiles.
Characterize similarities and differences between various probability distributions including probability density functions and cumulative density functions.
Define hypothesis testing and statistical significance, and evaluate how these are relevant in A/B testing.
Analyze cognitive biases using case studies.

Course Typically Offered
Fall