Learn about descriptive statistics, basic probability, random variables, sampling and confidence intervals, and hypothesis testing. And see how to apply these concepts and principles using the environment, functions, and visualizations of Excel.
As a data science pro, the ability to analyze data helps you to make better decisions, and a solid foundation in statistics and basic probability helps you to better understand your data. Using real-world concepts applicable to many industries, including medical, business, sports, insurance, and much more, learn from leading experts why Excel is one of the top tools for data analysis and how its built-in features make Excel a great way to learn essential skills.
Before taking this course, you should be familiar with organizing and summarizing data using Excel analytic tools, such as tables, pivot tables, and pivot charts. You should also be comfortable (or willing to try) creating complex formulas and visualizations. Want to start with the basics? Check out Introduction to Data Analysis using Excel. As you learn these concepts and get more experience with this powerful tool that can be extremely helpful in your journey as a data analyst or data scientist, you may want to also take the third course in our series, Analyzing and Visualizing Data with Excel.
What you'll learn
- Descriptive statistics
- Basic probability
- Random variables
- Sampling and confidence intervals
- Hypothesis testing
Course Syllabus
Module 1: Descriptive Statistics
You will learn how to describe data using charts and basic statistical measures. Full use will be made of the new histograms, Pareto charts, Boxplots, and Treemap and Sunburst charts in Excel 2016.
Module 2: Basic Probability
You will learn basic probability including the law of complements, independent events, conditional probability and Bayes Theorem.
Module 3: Random Variables
You will learn how to find the mean and variance of random variables and then learn about the binomial, Poisson, and Normal random variables. We close with a discussion of the beautiful and important Central Limit Theorem.
Module 4: Sampling and Confidence Intervals
You will learn the mechanics of sampling, point estimation, and interval estimation of population parameters.
Module 5: Hypothesis Testing
You will learn null and alternative hypotheses, Type I and Type II error, One sample tests for means and proportions, Tests for difference between means of two populations, and the Chi Square Test for Independence.
Prerequisites
- Secondary school (high school) algebra
- Ability to work with tables, formulas, and charts in Excel
- Ability to organize and summarize data using Excel analytic tools such as tables, pivot tables, and pivot charts
- Excel 2016 is required for the full course experience. Excel 2013 will work but will not support all the visualizations and functions