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Join this free online course to learn about data science. The 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 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.
Here is what some participants are saying about the course:
" Much Thanks to Stuart and all in team who assisted. Loved Stuart's Passion and non-technical way of teaching. Very Good Course - much more than a Introduction " Read the original post
" Great foundation into Data Science & AI. Thanks openSAP and the Instructor, this was a very good course to take and broaden my insight into the field." Read the original post
" Good Data Science Introduction. Thanks Stuart for your brilliant session. It makes me understand more skill and tech used in DS area. I feel I come back to university and listen to my professor's class." Read the original post
" Excellent Course. I enjoyed the course and the facilitator, well presented." Read the original post
" Thank you a bunch. I liked the way the course was delivered. Very informative and helpful. Thank you for the references." Read the original post
" This course provided more than a basic understanding of the data science concepts supported by very good links to both paid and free material to support further steps of the data science journey. " Read the original post
Week 1: Business and Data Understanding
Week 2: Data Preparation
Week 3: Modeling (1)
Week 4: Modeling (2)
Week 5: Evaluating Model Performance
Week 6: Deployment and Maintenance
Week 7: Final Exam
Anybody with a basic understanding of data, data analysis, and simple mathematics.
openSAP course Introduction to Statistics for Data Science
This course was rated with 4.44 stars in average from 1436 votes.
Find out more in the certificate guidelines.
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.