Data Science in Action - Building a Predictive Churn Model
Join this free online course to get hands-on practice with data science. You’ll learn about the entire process of collecting, processing, and evaluating data, and have access to a system for hands-on practice – just like a data scientist!
Mode autodidacte
Langue: English

Informations sur le cours

Course Summary

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.

Here is what some participants are saying about the course:

  • "Thanks to whole OpenSAP team for such great course. Needless to say that this course helps many to change their current role to be a future DATA Scientist/DATA Analyst…" read the original post

  • "Thanks a lot for offering again another excellent course. I learned a lot about the whole topic and how to use the tool." read the original post

  • "Excellent course, hoping to see more on Data Science!!!!!" read the original post

  • "Another solid Data Science course - thank you!" read the original post

  • "This is such a great initiative to help others learn more and in a short amount of time. I found it very interesting and insightful. I like that the course was easy to follow and well structured." read the original post

Course Characteristics

Course Content

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

Target Audience

Anybody with a basic understanding of data, data analysis, and simple mathematics.

Course Requirements

The following openSAP course is strongly recommended:

Getting Started with Data Science

Development Systems

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.

About Further Content Experts

Antoine Chabert

enter image description here

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.

Contenu du cours

  • 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
  • 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 reactivate now!

Inscrivez-moi à ce cours

Le cours est en accès libre. Créez votre compte et suivez le cours sur openSAP.
Inscrivez-moi maintenant

Ce cours a eu lieu du 8 nov. 2017 au 14 déc. 2017.

5863 apprenants étaient inscrits le jour 1 du cours.

Lorsque l'examen final était terminé, ce nombre avait augmenté à 8680.

13534 apprenants sont actuellement inscrits.


This course was rated with 4.43 stars in average from 612 votes.

Certificate Requirements

  • Obtenez un certificat de réussite en gagnant plus de 50% du nombre maximal de points pour la somme de toutes les tâches hebdomadaires.
  • Obtenez une attestation de participation en complétant au moins 50% du matériel du cours.

En savoir plus lisez les lignes directrices pour le certificat.

Ce cours est offert par

Stuart Clarke

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.

Votre demande a bien été envoyé à notre service d'assistance, et sera répondu dès que possible.


Oops quelque chose allait mal.