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.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
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.
Wayne Winston, Liberty J. Munson and Matthew Minton
Start your review of Essential Statistics for Data Analysis using Excel
Anonymous completed this course.
Don't get fooled by the title of this course - it says Essential Statistics yet it is a very comprehensive and challenging course if you are not coming from a serious statistical background. I completed this course as part of the Microsoft Data Science curriculum and this was perhaps the most difficult one from the 10 required modules of this course.
However, despite of the complexity of the subject the course itself is very good and really helps you to get speed up on essentials in statistics and provides you very good hands on skills using Excel's built in functionalities along with the explanations on what is really going on behind the scenes when applying certain statistical functions.
Anonymous completed this course.
Basic application of concepts and theory. Lacks depth and requires a lot of additional reading. Course modules changed halfway without prior warning, so you're not certain that you will actually finish the modules that you had chosen when you started. Certificates are relatively expensive for the content that is provided.