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.
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
Anybody interested in learning basic statistical concepts
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Stuart Clarke is a principal consultant in 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, Stuart provides predictive analytics consultancy, developing and implementing predictive models for SAP customers and delivering POCs for customers and prospects. He also delivers introduction courses for SAP Predictive Analytics and deep-dive technical sessions to SAP customers, partners, and internals worldwide.
Mike Jordan is a portfolio manager responsible for SAP education on topics such as machine learning, predictive analytics and data science. He is also an SAP academic ambassador, teaching data science to masters students.
Mike used to teach German and computer science to high school students, and left for what he thought would be a short career break in 1999 for a stint in industry, providing support and training for a data warehouse development tool.
In addition to his portfolio and academic responsibilities, Mike has a particular interest in the ethical use of technology and speaks at conferences on related topics.