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!
自学
语言: English

课程信息


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.

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

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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.

课程内容


  • 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!

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该课程自11月08日 到 12月14日 开设.

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评分


本课程已由612位用户进行了五分制评分,平均得分为4.43

该课程提供者


Stuart Clarke

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.

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