Coursera

Statistics and Data Analysis with Excel, Part 1 (Coursera)

Offered by University of Colorado Boulder,
Statistics and Data Analysis with Excel, Part 1 (Coursera)

Designed for students with no prior statistics knowledge, this course will provide a foundation for further study in data science, data analytics, or machine learning. Topics include descriptive statistics, probability, and discrete and continuous probability distributions. Assignments are conducted in Microsoft Excel (Windows or Mac versions). Designed to be taken with the follow-up course, “Statistics and Data Analysis with Excel, Part 2.”

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.

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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Practical Predictive Analytics: Models and Methods (Coursera) Coursera
University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Jun 22nd 2026
4 Weeks
Machine Learning Models Methods
Details
Regression Models (Coursera) Coursera
Johns Hopkins University

Regression Models (Coursera)

Statistics & Data Analysis Data Science

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models.

Jun 22nd 2026
4 Weeks
Statistics Regression Linear Regression
Details
Marketing Analytics (Coursera) Coursera
University of Virginia

Marketing Analytics (Coursera)

Marketing & Communication Business

Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions.

Jun 22nd 2026
5-12 Weeks
Marketing Regression Data Analysis
Details
Framework for Data Collection and Analysis (Coursera) Coursera
University of Maryland, College Park

Framework for Data Collection and Analysis (Coursera)

Statistics & Data Analysis Data Science

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan.

Jun 22nd 2026
4 Weeks
Analysis Data Analysis Analytics
Details
Pattern Discovery in Data Mining (Coursera) Coursera
University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining (Coursera)

Statistics & Data Analysis Data Science

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Jun 22nd 2026
4 Weeks
Algorithms Data Mining Data Analysis
Details
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 22nd 2026
4 Weeks
Statistics Probability Data Analysis
Details
Exploring and Producing Data for Business Decision Making (Coursera) Coursera
University of Illinois at Urbana-Champaign

Exploring and Producing Data for Business Decision Making (Coursera)

Management & Leadership

This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will be introduced to statistics and how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals.

Jun 22nd 2026
4 Weeks
Probability Excel Inference
Details
Leadership Through Marketing (Coursera) Coursera
Northwestern University

Leadership Through Marketing (Coursera)

Management & Leadership

The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.

Jun 28th 2026
4 Weeks
Marketing Strategy Leadership
Details
Introduction to Probability and Data with R (Coursera) Coursera
Duke University

Introduction to Probability and Data with R (Coursera)

Statistics & Data Analysis Data Science

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Jun 22nd 2026
5-12 Weeks
Statistics Probability Inference
Details
Reproducible Research (Coursera) Coursera
Johns Hopkins University

Reproducible Research (Coursera)

Statistics & Data Analysis Data Science

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations.

Jun 22nd 2026
4 Weeks
Data Analysis Data Science Reproducible Research
Details