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Essential Statistics for Data Analysis using Excel

About This Course

If you’re considering a career as a data analyst, you need to know about histograms, Pareto charts, Boxplots, Bayes’ theorem, and much more. In this applied statistics course, the second in our Microsoft Excel Data Analyst XSeries, use the powerful tools built into Excel, and explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives. 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 DAT205x: 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, DAT206x Analyzing and Visualizing Data with Excel. This course includes excerpts from Microsoft Excel 2016: Data Analysis and Business Modeling from Microsoft Press and authored by course instructor Wayne Winston.

This course is also part of the Microsoft Excel for the Data Analyst XSeries.

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.

Meet the instructors

Course Staff Image #1

Liberty J. Munson

Liberty is the Principal Psychometrician and Quality Lead for Microsoft Learning’s technical certification and assessment program. She is responsible for ensuring that psychometric standards are rigorously applied during all phases of the exam and assessment lifecycle and that the design and implementation of Microsoft’s Certification program results in valid and reliable measurements of candidate skills. She received her BS in Psychology from Iowa State University and her MA and PhD in Industrial/Organizational Psychology with minors in Quantitative Psychology and Human Resource Management from the University of Illinois at Urbana-Champaign.

Course Staff Image #2

Matthew Minton

Matthew is a Senior Content Publishing Manager at Microsoft. He leads a dedicated team of learning professionals that plan and create courses for aspiring and experienced data and analytics professionals.

Course Staff Image #3

Wayne Winston

Wayne is Professor Emeritus of Decision Sciences at the Kelly School of Business at Indiana University. He has written over a dozen textbooks, won over 40 teaching awards, and consulted for dozens of organizations including the New York Knicks and Dallas Mavericks.

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  4. Estimated Effort

    12-24 hours in total