In 2012, Harvard Business Review named data science the "sexiest job of the 21st century." Why are data scientists in such demand these days? The answer is that over the past decade, there has been an explosion in the data generated and retained by companies, and they need to leverage and exploit it. Data scientists are the people who make sense out of all this data and figure out just what can be done with it.
If you’re interested in learning about data science, this course will introduce you to the fundamentals of data preparation, predictive modeling, data science, and the deployment and maintenance of models in a business environment following a tried and tested project methodology.
In the first week, we’ll cover the fundamental challenges of business problem understanding and identifying the appropriate analytical approach. In the second week, we’ll cover data preparation, selecting variables, and data encoding. Weeks three and four will introduce you to a wide range of algorithms such as decision trees, regression, neural networks, basket analysis, and simulation. Week five explains how we evaluate the performance of our models and the approaches we take to improve them. In week 6, you’ll learn about model deployment and maintenance, and we’ll debunk some common myths.
Data science is a complex subject to understand, but in this course you’ll learn about the fundamental principles, look at how the algorithms can add value to your business, and we’ll demystify the complex processes. You don’t have to be a rocket scientist to be a data scientist.
Week 1: Business & Data Understanding
Week 2: Data Preparation
Week 3: Modeling (1)
Week 4: Modeling (2)
Week 5: Evaluation
Week 6: Deployment & Maintenance
Week 7: Final Exam
Anybody with a basic understanding of data, data analysis, and simple mathematics.
John MacGregor graduated with a Master’s Degree in Operations Research, after which he spent several years as a management consultant in Unilever’s UK Operation Research Group. John then joined the University of North London as a senior lecturer in Statistics and Operations Research. At the same time, he worked with the first BI products and OLAP servers to be created. John has a strong business background, having started and run his own software distributorship company in Australia as well as taking on the role of managing director for Pilot Software and Gentia Software, ANZ and UK.
Within SAP, John worked as the Australia & NZ regional director for Crystal Decisions before returning to the UK to lead the data mining developments of Crystal Decisions and then BusinessObjects. He is currently a member of the data science team in IoT Predictive Maintenance, part of LOB Digital Assets & IoT, with the official title of VP, Predictive Analysis. John created and delivers the Data Science 101 course for the SAP Development University. He is also the author of “Predictive Analysis with SAP – The Comprehensive Guide”, published by SAP Press.
If you would like to enroll for this course, there are no formal prerequisites or limitations. The course is free and open for everyone. Just register for an account on openSAP and go for the course!Enroll me now
This course was held from Feb 01, 2017 through Mar 23, 2017.
28271 learners enrolled.
This course was rated with 4.32 stars in average from 1453 votes.
Find out more in the certificate guidelines.
Stuart Clarke is a principal 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.