An error occurred while loading the video player, or it takes a long time to initialize. You can try clearing your browser cache. Please try again later and contact the helpdesk if the problem persists.

Join this free online course to understand some of the basic statistical concepts and practices that are the foundations of data science and the way we analyze data. You’ll learn about the fundamental principles of statistics and how it can be used in your everyday life.

Self-paced since November 27, 2019
Language: English
Subtitles (auto-generated): Deutsch, English, Français, Español

Course information

Course Summary

This course will provide you with the knowledge to understand some of the basic statistical concepts and practices that are the foundations of data science and the way we analyze data. We’ll also be highlighting how statistics can be misused and abused, leading to accidental misunderstandings or deliberate distortions to support a particular prejudiced view.

Throughout this course, you’ll be looking at how data can be summarized in a variety of ways to give you a descriptive overview of large data sets and their variations. We’ll explore how data distributions can be understood and compared. You’ll also learn how you can start finding patterns in the data, where changes in one variable may be (partly) explained by changes in another. In addition, you’ll learn a little about how to estimate the likelihood of certain outcomes based on certain prior information. Finally, we’ll link this up to some of the common tools available that make these kinds of analyses easier.

The course is spread over six weeks and consists of lectures and weekly assignments.
Statistics can be a complex subject, but in this course, you’ll revisit the fundamental principles and start to appreciate how it can be used in your everyday life. We’ll focus primarily on the key principles.

Here is what some participants are saying about the course:

“It was a brilliant course! Thank you. At times, challenging - in a good way. I’m grateful to Stuart and Mike for navigating us through what felt like a mountain of new information…I’m also grateful to SAP for offering great courses. I’d definitely like to learn more about statistics. read the original post

“Once again an informative and gratifying course from Stuart Clark, now with the help of Mike Jordan! Big thanks to the whole team for creating it, you make learning truly enjoyable!” read the original post

“This was a great knowledge refreshment after long years from finishing university, Thank you for this great course, well elaborated and consistent content.“ read the original post

“This is a very great introductory course to Data Science. I can't wait to start other related openSAP courses on Data Science.” read the original post

“Thanks, I enjoyed this course very much. There should be many ways to explain about Statistics however the way explained here was step by step” read the original post

“openSAP platform is wonderful to enhance knowledge by customizing my own time. This course is excellent. I am excited to refresh my statistics knowledge after 10 years. All the tools, examples, web links and references are awesome.” read the original post

“Let me express my profound gratitude to the openSAP team for a course like this. This has indeed set me on way for a career in Business Intelligence and Reporting Analytics. I look forward to taking more openSAP courses in the near future.” read the original post

Course Characteristics

Course Content

Week 1: Introduction to Statistics
Week 2: Descriptive Statistics
Week 3: Correlation and Linear Regression
Week 4: Introduction to Probability
Week 5: Probability Distributions
Week 6: Connecting to Your SAP Solutions
Week 7: Final Exam

Target Audience

Anybody interested in learning basic statistical concepts

Course Requirements

Basic numeracy

Course contents

  • Week 1:

    Introduction to Statistics
  • Week 2:

    Descriptive Statistics
  • Week 3:

    Correlation and Linear Regression
  • Week 4:

    Introduction to Probability
  • Week 5:

    Probability Distributions
  • Week 6:

    Connecting to Your SAP Solutions
  • I Like, I Wish:

    We Love Your Feedback … And Want More
  • Final Exam:

    Good Luck!

Reactivate this course

You can access all graded assignments and earn a Record of Achievement with openSAP, course reactivation option. Learn more or

Enroll me for this course

The course is free. Just register for an account on openSAP and take the course!
Enroll me now

Learners

Current
Today
29,017
Course End
Nov 27, 2019
17,569
Course Start
Oct 08, 2019
13,144

Rating

This course was rated with 4.31 stars in average from 1816 votes.

Certificate Requirements

  • Gain a Record of Achievement by earning at least 50% of the maximum number of points from all graded assignments.
  • Gain a Confirmation of Participation by completing at least 50% of the course material.

Find out more in the certificate guidelines.

This course is offered by

Stuart Clarke

Stuart Clarke is a chief consultant with Global Consulting Delivery, Analytics and Insight, focusing on predictive analytics and data science.

He has specialized in operational research, data science, predictive analytics, and advanced analytics for over 25 years, working extensively in the telecommunications, retail, utilities, and financial services sectors.

At SAP, he provides predictive analytics consultancy, developing and implementing predictive models for SAP customers and delivering POCs for customers and prospects. He also holds introduction courses for SAP Predictive Analytics and deep-dive technical sessions to SAP customers, partners, and internals, world-wide.

Michael Jordan

Mike Jordan is an education portfolio manager at SAP, responsible for customer education on a range of topics including SAP’s sustainability solutions, AI/ML solutions, and data science. He is also an SAP academic ambassador which, for a number of years, has involved him teaching Masters students data science and speaking at conferences.

Mike has long been interested in the ethical use of technology and data science to support environmental and social goals, more recently through the circular economy.