Data Science Fundamentals
ABOUT THIS LEARNING PATH
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. Come along and start your journey to receiving the following badges: Data Science Foundations.
TELL YOUR FRIENDS
Aspiring Data Scientists
LEARNING PATH LEVEL:
Our learning paths are designed to build on the content learned in the first course and then build upon the concepts in courses that follow. We recommend that they are completed in the order outlined in this learning path to ensure you get the most out of your investment of time. If you like what you see here, come and discover other learning paths and browse our course catalog.
How to obtain your badge for the "data-science" learning path
- Step 1: Enrol in every course above.
- Step 2: Successfully pass and receive a certificate for each of the courses.
- Step 3: Return here and click the button below to request your badge.
- Step 4: Visit your email to accept your badge.
Data Science Foundations - Level 2 (V2)
This badge earner has a solid understanding of data science methodologies, and tools. The individual also has a hands-on appreciation of programming languages to use in data science tasks.Liquid error: internal