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Data Science Methodology Big Data University

Enrollment in this course is by invitation only

About This Course

Learn how data scientists think!
  • Learn the major steps involved in tackling a data science problem.
  • Learn the major steps involved in practicing data science, with interesting real-world examples at each step: from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.

Course Syllabus

Module 1: From Problem to Approach
  • Business Understanding - Concepts & Case Study
  • Analytic Approach - Concepts & Case Study
Module 2: From Requirements to Collection 
  • Data Requirements - Concepts & Case Study
  • Data Collection - Concepts & Case Study
Module 3: From Understanding to Preparation 
  • Data Understanding - Concepts & Case Study
  • Data Preparation - Concepts & Case Study
Module 4: From Modeling to Evaluation
  • Modeling - Concepts & Case Study
  • Evaluation - Concepts & Case Study
Module 5: From Deployment to Feedback
  • Deployment - Concepts & Case Study
  • Feedback - Concepts & Case Study

General Information

  • This course is free.
  • It is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.

Recommended skills prior to taking this course

  • None


  • None

Course Staff

John B. Rollins, Instructor of Data Science Methodology

John B. Rollins

John B. Rollins, Ph.D., P.E., is a Data Scientist at IBM. He is part of the IBM Analytics group and holds a Ph.D. in Petroleum Engineering and Economics from Texas A&M University. With an excellent background of engineering consulting, having been a professor and researcher, he has authored many patents, books, and papers. He achieved honors and awards from IBM as an IBM Second Plateau Inventor. He has great experience in data science methodology.
  1. Course Number

  2. Classes Start

    Any Time, Self-Paced
  3. Estimated Effort

    3 hours