Coursera

Data Driven Decision Making (Coursera)

Offered by University of Colorado Boulder,

Once we have generated data, we need to answer the research question by performing an appropriate statistical analysis. Engineers and business professionals need to know which test or tests to use. Through this class, you will be able to perform one sample tests for comparison to historical data. You will also be able to determine statistically significant relationships between two variables. You will be able to perform two sample tests for both independent and dependent data. Finally, you will analyze data with more than two groups using the Analysis of Variance.

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

This course is part of the The Data Driven Manager Specialization.

Syllabus

One Sample Tests
Module 1
Upon completion of this module, students will be able compare generated data to historical data for both continuous and discrete data using RStudio and ROIStat.

Correlation and Association
Module 2
Upon completion of this module, students will be able to determine relationships between two variables for both continuous and discrete data using RStudio and ROIStat.

Two Sample Tests for Independent Data
Module 3
Upon completion of this module, students will be able to compare two independent samples for both continuous and discrete data using RStudio and ROIStat.

Two Sample Tests for Dependent Data
Module 4
Upon completion of this module, students will be able to compare two dependent samples for both continuous and discrete data using RStudio and ROIStat.

The One Way Analysis of Variance
Module 5
Upon completion of this module, students will be able to analyze continuous data with more than 2 groups using RStudio and ROIStat.

Go to Class

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

Related Courses

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 15th 2026

4 Weeks

Data Analysis Data Science Reproducible Research

Coursera

Copenhagen Business School

Strategy Formulation (Coursera)

Management & Leadership

The purpose of this course is to present, analyze and discuss the different facets of business strategy formulation. Emphasizing that strategy can be seen as a unified theme that provides coherence and direction to the actions and decisions of a firm, we will cover a variety of business strategy topics concerned with firm positioning in the context of different markets, industries and locations.

Jun 15th 2026

5-12 Weeks

Innovation Strategy Business Strategy

Coursera

University of California, San Diego

Introduction to Big Data (Coursera)

Statistics & Data Analysis Data Science

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world!

Jun 15th 2026

3 Weeks

Big Data Data Analysis Hadoop

Coursera

Johns Hopkins University

R Programming (Coursera)

Statistics & Data Analysis Data Science

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Jun 15th 2026

4 Weeks

Programming Debugging Data Analysis

Coursera

Johns Hopkins University

Exploratory Data Analysis (Coursera)

Statistics & Data Analysis Data Science

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Jun 15th 2026

4 Weeks

Statistics Data Analysis Data Science

Coursera

Wesleyan University

Data Management and Visualisation (Coursera)

Statistics & Data Analysis Data Science

Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly.

Jun 15th 2026

4 Weeks

Python Data Management Data Analysis

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 15th 2026

4 Weeks

Statistics Probability Data Analysis

Coursera

Duke University

Data Visualization and Communication with Tableau (Coursera)

Statistics & Data Analysis Data Science

One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses.

Jun 15th 2026

5-12 Weeks

Excel Communication Computer Graphics

Coursera

University of California, San Diego

Teaching Impacts of Technology: Data Collection, Use, and Privacy (Coursera)

Education

In this course you’ll focus on how constant data collection and big data analysis have impacted us, exploring the interplay between using your data and protecting it, as well as thinking about what it could do for you in the future. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level.

Jun 17th 2026

4 Weeks

Education Computing Technology

Coursera

Johns Hopkins University

Data Science in Real Life (Coursera)

Management & Leadership Statistics & Data Analysis

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

Jun 15th 2026

1 Week

Statistics Machine Learning Data Management

Coursera

Google

Analyze Data to Answer Questions (Coursera)

Statistics & Data Analysis Data Science

This is the fifth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll explore the “analyze” phase of the data analysis process. You’ll take what you’ve learned to this point and apply it to your analysis to make sense of the data you’ve collected. You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. You’ll also find out how to perform complex calculations on your data to complete business objectives.

Jun 16th 2026

4 Weeks

Data Databases SQL

Coursera

Duke University

Mastering Data Analysis in Excel (Coursera)

Statistics & Data Analysis Data Science

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits.

Jun 15th 2026

5-12 Weeks

Business Excel Data Analysis