Statistics for Data Science I
3 Credit Hours
Students will study fundamental concepts and tools to describe and summarize data, including data collection methods, measures of central tendency and spread, the relation between two variables, conditional/joint/marginal probabilities, discrete probability distributions, and continuous probability distributions. Also, students will learn classical foundations of statistical inference, including sampling distributions, point estimates, confidence intervals, null hypothesis significance testing, goodness-of-fit test, Fisher’s exact test, various two sample tests, ANOVA tests, etc. Additional nonparametric tests may be included. As Programming for Data Science is being taught simultaneously, programming tools like R and Python will be heavily used in the course.