Introduction to Statistics for Data Science

Lessons and examples from a former Google data scientist to master hypothesis tests, confidence intervals, and more

Ratings: 4.49 / 5.00




Description

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


What You Will Learn!

  • Build a strong statistical vocabulary and foundation in probability
  • Learn to tests hypotheses for proportions and means
  • Learn how to create confidence intervals, and their connection to hypothesis tests
  • Learn how to perform chi-square tests for categorical data

Who Should Attend!

  • Self-learners who want a strong college-level foundational course in statistics
  • College and high school students who need to supplement their course with high-quality lectures and example problems
  • Data science professionals looking to refresh or expand their knowledge to prepare for job interviews