The recruitment company Glassdoor recently revealed the 50 best jobs in America. The score is determined by three key factors: the number of job openings, the job satisfaction rating, and the median annual base salary. Unsurprisingly, the data science jobs claimed the top spot for the second year in a row. In fact, the data science job score was 4.8 out of 5, with a job satisfaction score of 4.4 out of 5, and a median base salary of $110,000. One of the main reasons for these extraordinary scores is the huge shortage in talented individuals with the right skill sets.
Following the success of the openSAP course Getting Started with Data Science, this follow-on course will provide students with the knowledge and hands-on skills to build, test, and deploy predictive models in a business-like environment.
In the first week, you’ll be introduced to the business case study where you are asked to investigate customer churn for a telecommunications organization. In the second week, you’ll prepare the data and create an analytical data set, conduct an initial data analysis, and learn how to encode the data. In the third week, you’ll develop a churn model, evaluate its performance, and learn how to productionize it. Then, in the fourth week, you’ll learn how to monitor and maintain a model, build a segmentation, and improve model performance. Finally, in week five, you’ll take a short exam.
Data science is a complex subject, but in this course, you’ll revisit the fundamental principles and learn how to do if for real. Remember, you don’t have to be a rocket scientist to be a data scientist.
Week 1: Case Study Introduction
Week 2: Prepare and Encode Data
Week 3: Develop, Evaluate, and Deploy Models
Week 4: Monitor Models and Improve Performance
Week 5: Final Exam
Anybody with a basic understanding of data, data analysis, and simple mathematics.
The following openSAP course is strongly recommended:
To get a deeper understanding of the predictive analytics processes used, we recommend you follow the hands-on exercises. For this purpose we will provide the required systems and installation instructions in week 1 of this course.
Antoine Chabert is a product manager for SAP Predictive Analytics. His current tasks include promoting the solutions within the predictive community and supporting customers on their way to predictive success.
Antoine joined SAP (almost) 10 years ago and has held different engineering and customer-facing roles in the areas of Business Intelligence (semantic layer, data visualization, smart data discovery) and Predictive Analytics.
Prior to joining SAP, Antoine was part of a start-up specializing in predictive analytics for manufacturing, whose intellectual property was acquired by Dassault Systèmes.
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 Nov 08, 2017 through Dec 14, 2017.
9722 learners enrolled.
This course was rated with 4.43 stars in average from 612 votes.
A record of achievement is issued to those who have earned more than 50% of the maximum number of points for the sum of all graded assignments. A confirmation of participation is issued to those who have completed at least 50% of the course material. 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.