About the Program

Want to learn Data Science? We recommend that you start with this learning path.

Dust off your lab-coat and stretch out your fingers and get ready for the journey of a lifetime that will have you see the everyday through a new lens. Looking at mundane events becomes interesting from the speed of your windshield wipers wiping off the rain to the rate of plant growth in ditches along highways under different conditions. As the study that leads into all things pertinent to humans in present, this path is a must for all who have even the slightest interest in this field.

This learning path currently consists of one course that introduces you to Data Science from a practitioner point of view, to courses that discuss topics such as data compilation, preparation and modeling throughout the life-cycle of data science from basic concepts and methodologies to advanced algorithms. It also discusses how to get some practical knowledge with open source tools.

Data Science Foundations - Course Outline

Course 1: Introduction to Data Science

Effort: 3 hours Level: Beginner

Data Science is the hottest field of the century. Learn more about why data science, artificial intelligence (AI) and machine learning are revolutionizing the way people do business and research around the world. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Course 2: Data Science Tools

Effort: 4 hours Level: Beginner

Learn and try out the most popular data science tools like Jupyter Notebooks, RStudio IDE, Apache Zeppelin, IBM Watson Studio, and more. Tools are available to use directly on the cloud.

Course 3: Data Science Methodology

Effort: 5 hours Level: Beginner

Grab your lab coat, beakers, and pocket calculator ... wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed.

Learning Path Courses

  • Data Science 101

    • Details
  • Data Science Hands-On with Open Source Tools

    • Details
  • Data Science Methodology

    • Details
-