Statistics from edX

A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

In many engineering master’s programs, statistics is used quite intensively. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). But you will also want to perform some analysis (inferential statistics): you may want to build a model that mimics reality, estimate some quantities, or test some hypotheses.

The statistics course in this series will help you refresh your knowledge on these topics. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.

This course offers enough depth to cover the statistics you need to succeed in your engineering master’s or profession in areas such as machine learning, data science and more.

This is a review courseThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.

This format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. You will get many exercises, to be solved using Grasple or R, for which you will receive intelligent, personal and immediate feedback.

What's inside

Learning objectives

Make and interpret numerical and graphical summaries of datasets.
Use various techniques to find estimators for unknown parameters and how to compare them.
Construct and interpret confidence intervals, learn how to perform hypothesis testing in various settings, and know how these two concepts are related.

Perform simple and multiple linear regression on quantitative and categorical variables.
Apply certain procedures (resampling, bootstrapping, non-parametric approach) when confronted with non-standard situations.
Use the r software package to perform all these tasks.

Make and interpret numerical and graphical summaries of datasets.
Use various techniques to find estimators for unknown parameters and how to compare them.
Construct and interpret confidence intervals, learn how to perform hypothesis testing in various settings, and know how these two concepts are related.
Perform simple and multiple linear regression on quantitative and categorical variables.
Apply certain procedures (resampling, bootstrapping, non-parametric approach) when confronted with non-standard situations.
Use the r software package to perform all these tasks.

Syllabus

Week 1: Descriptive statistics

graphical summaries of datasets

numerical summaries of datasets

connection with probability theory

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops the foundation for the statistics you need to succeed in your engineering master's or profession in areas such as machine learning, data science and more

Offers a comprehensive study of statistics as applied to engineering

Emphasizes the application of concepts and skills through exercises and immediate feedback

Taught by instructors who are experts in the field of statistics and engineering

Covers a wide range of topics, including descriptive statistics, hypothesis testing, and regression

Reviews summary

Intensive statistics review with r

According to learners, Statistics is a highly valuable review course for those looking to solidify their understanding of statistical concepts, especially for roles in data science, machine learning, or engineering master's programs. Students consistently praise the integration of the R software package and the practical, hands-on exercises with intelligent, immediate feedback as key strengths. While many find the high pace and assumed prior knowledge to be effective for a review, some learners caution that it can be overwhelming for those who are rusty or seeking an introductory experience, requiring supplementary resources.

Divided opinions on the balance between theory and intuition.

"While the R exercises were helpful, I found the course material itself quite dry and theoretical. I expected more intuition and less rote memorization."

"The instructor explains complex topics clearly and concisely, which was great for a quick recap."

"Sometimes the lectures felt a bit rushed, especially if I hadn't touched stats in a while, leading to less intuitive understanding."

Exercises with immediate, intelligent feedback are highly effective.

"Grasple feedback was really helpful. The exercises are challenging but fair, and the feedback system is top-notch."

"I appreciated the high quality of the challenging exercises, and the immediate feedback system for Grasple was truly excellent."

"The structured exercises and immediate feedback are a game-changer for learning efficiently and verifying my understanding."

Excellent for refreshing stats for ML/Data Science careers.

"Excellent course for refreshing statistical concepts needed for ML/Data Science."

"This course truly helps solidify my understanding of statistics for an engineering master's."

"Solid course for a quick brush-up. As a data science professional, this course was perfect for me."

Strong emphasis on applying concepts using R software.

"The R exercises were invaluable for applying theory. They truly helped solidify my understanding."

"I liked how R was integrated into each module. The way they introduce R alongside concepts makes it very hands-on."

"As a data science professional, this course was perfect for brushing up on the mathematical foundations. The R integration is crucial for practical application."

Course pace requires a solid statistical foundation.

"This is not a review course, it's a speed run. Absolutely no time to digest if you aren't already an expert."

"If you're rusty, you'll struggle. The instructor is knowledgeable but sometimes assumes too much prior understanding."

"I expected a review, but the pace was extremely fast, making it difficult to fully grasp topics without prior strong familiarity."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics with these activities:

Review probability theory

Show steps

This book provides a solid foundation in probability theory, which is essential for understanding the concepts covered in this course.

View Probability & Statistics for Engineers &... on Amazon

Show steps

Read Chapter 1: Introduction to Probability
Solve the practice problems at the end of the chapter

Learn R for Statistical Analysis

Show steps

This activity provides an opportunity to learn the basics of R, which is the statistical software used in this course.

Browse courses on R Programming

Show steps

Follow the R Tutorial
Complete the practice exercises

Practice hypothesis testing

Show steps

This activity provides practice in hypothesis testing, which is a key concept in this course.

Browse courses on Hypothesis Testing

Show steps

Solve the practice problems on hypothesis testing
Use R to conduct hypothesis tests

Three other activities

Expand to see all activities and additional details

Show all six activities

Form a study group

Show steps

This activity provides a supportive environment to discuss the course material and work on assignments with other students.

Show steps

Find other students who are taking the course
Schedule regular study sessions

Create a data visualization

Show steps

This activity provides an opportunity to apply the concepts learned in this course to real-world data.

Browse courses on Data Visualization

Show steps

Choose a dataset
Clean and prepare the data
Create a data visualization
Interpret the results

Participate in a data science competition

Show steps

This activity provides an opportunity to apply the skills learned in this course to a real-world problem.

Show steps

Find a data science competition that interests you
Form a team or work independently
Develop a solution to the problem
Submit your solution

Career center

Learners who complete Statistics will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, and interpret data to solve problems in a variety of fields, such as healthcare, finance, and engineering. This course may help you build a foundation in statistics, including descriptive statistics, estimator theory, hypothesis testing, and confidence intervals.

See salaries and explore the career path for Statistician

Actuary

Actuaries use statistics to assess risk and uncertainty, helping businesses make informed decisions. This course may help you develop the mathematical and statistical skills needed for this role, such as probability theory, risk assessment, and using the R software package.

See salaries and explore the career path for Actuary

Data Scientist

Data Scientists use statistics to analyze and interpret data, helping businesses make better decisions. This course may help you develop the quantitative skills needed for this role, such as statistical modeling, hypothesis testing, and using the R software package.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

Operations Research Analysts use statistics to improve decision-making in various industries, such as healthcare, transportation, and manufacturing. This course may help you develop the quantitative and analytical skills needed for this role, such as hypothesis testing, optimization, and using the R software package.

See salaries and explore the career path for Operations Research Analyst

Biostatistician

Biostatisticians use statistics to analyze and interpret data in the field of healthcare. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Biostatistician

Data Analyst

Data Analysts use statistics to collect, analyze, and interpret data. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to improve business processes and make better decisions. This course may help you develop the analytical skills needed for this role, such as data summarization, hypothesis testing, and regression analysis.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use statistics and financial modeling to make investment decisions. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Quantitative Analyst

Econometrician

Econometricians use statistics to analyze economic data and build models to predict economic behavior. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Econometrician

Epidemiologist

Epidemiologists use statistics to investigate the causes and patterns of disease. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Epidemiologist

Financial Analyst

Financial Analysts use statistics to analyze financial data and make investment recommendations. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers design and build systems for storing and processing data. This course may help you develop the statistical and programming skills needed for this role, such as data summarization, hypothesis testing, and using the R software package.

See salaries and explore the career path for Data Engineer

Market Researcher

Market Researchers use statistics to collect and analyze data about consumer behavior and market trends. This course may help you develop the statistical and analytical skills needed for this role, such as survey design, hypothesis testing, and using the R software package.

See salaries and explore the career path for Market Researcher

Risk Manager

Risk Managers use statistics to assess and manage risk. This course may help you develop the statistical and programming skills needed for this role, such as hypothesis testing, regression analysis, and using the R software package.

See salaries and explore the career path for Risk Manager