University of Colorado Boulder
Statistics and Data Analysis with Excel, Part 1
University of Colorado Boulder

Statistics and Data Analysis with Excel, Part 1

Taught in English


Gain insight into a topic and learn the fundamentals

Charlie Nuttelman

Instructor: Charlie Nuttelman

Beginner level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Calculate descriptive statistics (traditional and robust estimators).

  • Understand probability and apply probability rules.

  •  Utilize statistical functions in Microsoft Excel.

  •  Visualize univariate and bivariate data in Microsoft Excel.

Details to know

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January 2024


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There are 5 modules in this course

Welcome to the course! In this module, you will orient yourself to the course policies and will learn a few of the basics related to statistics.

What's included

3 videos6 readings2 quizzes1 discussion prompt

During Week 2, you will learn how to calculate population and sample statistics as well as quartiles and percentiles. Data visualization is important in the field of statistics - you will learn all about histograms, which are used for presenting univariate data in graphical format, as well as scatter plots and column plots. You will learn how to visualize univariate data in a box plot, which is a nice technique for identifying outliers. Finally, you will learn how to clean and transform data and use robust estimators in data sets that are highly affected by outliers.

What's included

17 videos3 readings3 quizzes1 discussion prompt

In Week 3, you will learn all about probability and counting techniques. A thorough understanding of probability is paramount for the study of statistics. There are several rules and axioms that govern probability, and you will explore these rules in several screencasts. Finally, you will learn about conditional probability, which is the foundation for Bayes' Theorem.

What's included

11 videos3 readings3 quizzes1 discussion prompt

Week 4 focuses on discrete probability distributions, in which the random variable is constrained to discrete values. Discrete probability distributions allow statisticians to make probabilistic predictions related to discrete stochastic models. These distributions include the binomial, geometric, negative binomial, hypergeometric, multinomial, and Poisson distributions.

What's included

13 videos4 readings3 quizzes1 discussion prompt

Building on what you learned about probability distributions in Week 4, you will explore continuous random variables and continuous probability distributions in Week 5. These distributions include the common normal distribution and standard normal distribution, but we'll also delve into the exponential distribution, gamma distribution, and others. These distributions allow us to make probabilistic predictions related to stochastic models.

What's included

14 videos4 readings4 quizzes1 discussion prompt


Charlie Nuttelman
University of Colorado Boulder
8 Courses401,455 learners

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