Statistics and Data Analysis with Excel, Part 1

University of Colorado Boulder via Coursera

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Overview

The “Statistics and Data Analysis with Excel, Part 1” course offers a practical introduction to fundamental statistical concepts and data analysis techniques using Microsoft Excel. Designed for beginners and professionals alike, this course enables learners to leverage Excel’s powerful tools to organize, analyze, and visualize data effectively. Participants will explore key statistical measures including mean, median, mode, variance, and standard deviation, and understand how to apply them within Excel spreadsheets. The course covers essential data analysis methods such as sorting,...

Syllabus

WELCOME!

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.

Descriptive Statistics and Graphical Representation of Data

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.

Probability

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.

Discrete Probability Distributions

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.

Continuous Probability Distributions

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.