Coursera

The Power of Statistics (Coursera)

Offered by Google,

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.

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

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
By the end of this course, you will:
-Describe the use of statistics in data science
-Use descriptive statistics to summarize and explore data
-Calculate probability using basic rules
-Model data with probability distributions
-Describe the applications of different sampling methods
-Calculate sampling distributions
-Construct and interpret confidence intervals
-Conduct hypothesis tests
This course is part of the Google Advanced Data Analytics Professional Certificate.

Syllabus

WEEK 1
Introduction to statistics
You’ll explore the role of statistics in data science and identify the difference between descriptive and inferential statistics. You’ll learn how descriptive statistics can help you quickly summarize a dataset and measure the center, spread, and relative position of data.

WEEK 2
Probability
You’ll learn the basic rules for calculating probability for single events. Next, you’ll discover how data professionals use methods such as Bayes’ theorem to describe more complex events. Finally, you’ll learn how probability distributions such as the binomial, Poisson, and normal distribution can help you better understand the structure of data.

WEEK 3
Sampling
Data professionals use smaller samples of data to draw conclusions about large datasets. You’ll learn about the different methods they use to collect and analyze sample data and how they avoid sampling bias. You’ll also learn how sampling distributions can help you make accurate estimates.

WEEK 4
Confidence intervals
You’ll explore how data professionals use confidence intervals to describe the uncertainty of their estimates. You'll learn how to construct and interpret confidence intervals — and how to avoid some common misinterpretations.

WEEK 5
Introduction to hypothesis testing
Hypothesis testing helps data professionals determine if the results of a test or experiment are statistically significant or due to chance. You’ll learn about the basic steps for any hypothesis test and how hypothesis testing can help you draw meaningful conclusions about data.

WEEK 6
Course 4 end-of-course project
In this end-of-course project, you’ll use statistical methods such as hypothesis testing to analyze a workplace scenario dataset.

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

University of Virginia

Strategic Planning and Execution (Coursera)

Management & Leadership

Avoid the pitfalls of strategy planning and execution with the tools and skills from this course. You'll learn the pillars of strategy execution--analysis, formulation, and implementation--and how to use the 4A model to effectively approach strategy execution. Finally, a panel of leaders from entrepreneurs, nonprofits, and industry, share their expertise gleaned from years of successful strategy planning and execution.

Jun 29th 2026

4 Weeks

Business Strategic Planning Execution

Coursera

University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Jun 29th 2026

5-12 Weeks

Python Algorithms Machine Learning

Coursera

Rice University

Python Data Analysis (Coursera)

CS: Software Engineering CS: Programming

This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data.

Jun 29th 2026

4 Weeks

Programming Python Data Structures

Coursera

University of Michigan

Python Basics (Coursera)

CS: Programming

This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures. You'll program an on-screen Turtle to draw pretty pictures. You'll also learn to draw reference diagrams as a way to reason about program executions, which will help to build up your debugging skills.

Jun 29th 2026

4 Weeks

Programming Python Sequences

Coursera

University of California, San Diego,Higher School of Economics - HSE University

Combinatorics and Probability (Coursera)

CS: Theory Sci: Mathematics

Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics.

Jun 29th 2026

5-12 Weeks

Probability Combinatorics Algorithms

Coursera

University of Alberta

Problem Solving, Python Programming, and Video Games (Coursera)

CS: Programming

This course is an introduction to computer science and programming in Python. Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. However, a learner can take the knowledge and skills from this course and apply them to non-game problems, other programming languages, and other computer science courses.

Jun 29th 2026

5-12 Weeks

Programming Python Algorithms

Coursera

Wesleyan University

Regression Modeling in Practice (Coursera)

Statistics & Data Analysis

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Jul 3rd 2026

4 Weeks

Python Data Analysis Linear Regression

Coursera

Emory University

Meaningful Marketing Insights (Coursera)

Marketing & Communication Business

With marketers are poised to be the largest users of data within the organization, there is a need to make sense of the variety of consumer data that the organization collects. Surveys, transaction histories and billing records can all provide insight into consumers’ future behavior, provided that they are interpreted correctly. In Introduction to Marketing Analytics, we introduce the tools that learners will need to convert raw data into marketing insights. The included exercises are conducted using Microsoft Excel, ensuring that learners will have the tools they need to extract information from the data available to them.

Jun 29th 2026

5-12 Weeks

Marketing Data Analysis Microsoft Excel

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 29th 2026

1 Week

Statistics Machine Learning Data Management

Coursera

Rice University

Python Programming Essentials (Coursera)

CS: Software Engineering CS: Programming

This course will introduce you to the wonderful world of Python programming! We'll learn about the essential elements of programming and how to construct basic Python programs. We will cover expressions, variables, functions, logic, and conditionals, which are foundational concepts in computer programming. We will also teach you how to use Python modules, which enable you to benefit from the vast array of functionality that is already a part of the Python language. These concepts and skills will help you to begin to think like a computer programmer and to understand how to go about writing Python programs.

Jun 29th 2026

4 Weeks

Programming Python Debugging

Coursera

Duke University

Bayesian Statistics (Coursera)

Statistics & Data Analysis Data Science

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm.

Jun 29th 2026

5-12 Weeks

Statistics Data Analysis R Programming

Coursera

IBM

Machine Learning with Python (Coursera)

Data Science

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Jun 29th 2026

5-12 Weeks

Programming Python Machine Learning