In our Statistics Fundamentals course, you’ll get an introduction to statistics and how this mathematical discipline is used in data science.
You will learn several techniques for sampling data such as random sampling and cluster sampling. You will also learn about concepts such as discrete variables and random variables in the context of frequency distributions, and the different types of charts and graphs you might use to visualize frequency distributions.
As you learn about these concepts and how they can be used for more robust data analysis, you will be working with a data set about basketball players in WNBA (Women's National Basketball Association), that contains general information about players, along with their metrics for the 2016-2017 season.
At the end of the course, you'll dive into a different data set and complete a portfolio project in which you'll investigate Fandango Movie Ratings to see whether Fandango could be inflating movie ratings on its site. This project is a chance for you to combine the statistics skills you’ve learned in this course, and an opportunity to learn to identify and overcome common setbacks in practical data analysis. And of course, your finished project could also make a great addition to your data science project portfolio.
By the end of this course, you'll be able to:
Learn Statistics Fundamentals
Simple Random Sampling
Learn about simple random sampling methods with R.
Statified Sampling and Cluster Sampling
Learn about stratified sampling and cluster sampling with R.
Variables in Statistics
Understand what variables are in statistics, and how they're measured.
Learn to generate and analyze frequency distributions with R.
Visualizing Frequency Distributions
Learn to generate graphs for frequency distributions with R.
Comparing Frequency Distributions
Learn how to compare frequency distributions with visualizations with R.
Investigating Fandango Movie Ratings
Learn to combine the skills you learned in this course to perform practical data analysis.