Introduction to Statistics for Data Science

This course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering hypothesis testing for proportions, means, and categorical data.

The course includes:

• 10 hours of video lectures, using the innovative lightboard technology to deliver face-to-face lectures

• Supplementary lecture notes with each lesson covering important vocabulary, examples and explanations from the video lessons

• 19 quizzes to check your understanding

• 9 assignments with solutions to practice what you have learned

You will learn about:

• Common terminology to describe different types of data and learn about commonly used graphs

• Basic probability, including the concept of a random variable, probability mass functions, cumulative distribution functions, and the binomial distribution

• What is the normal distribution, why it is so important, and how to use z-scores and z-tables to compute probabilities

• Type I errors, alpha, critical values, and p-values

• How to conduct hypothesis tests for one and two proportions using a z-test

• How to conduct hypothesis tests for one and two means using a t-test

• Confidence Intervals for proportions and means, and the connection between hypothesis testing and confidence intervals

• How to conduct a chi-square goodness-of-fit test

• How to conduct a chi-square test of homogeneity and independence.

• An introduction to correlation and simple linear regression

This course is ideal for many types of students:

• Anyone who wants to learn the foundations of statistics and understand concepts like p-values and confidence intervals

• Students taking an introductory college or high school statistics class who would like further explanations and detailed examples

• Data science professionals who would like to refresh and expand their statistics knowledge to prepare for job interviews