Professional Course

Understanding the World Through Data

edX, Online
9 weeks
9 weeks
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Course description

Understanding the World Through Data

Speech recognition, drones, and self-driving cars – things that once seemed like pure science fiction – are now widely available technologies, and just a few examples of how humans have taught machines to analyze data and make decisions. In this hands-on, introductory course, you will examine all the forms in which data exists, learn tools that uncover relationships between data, and leverage basic algorithms to understand the world from a new perspective.

Whether you're a high school student or someone switching careers, all you need to get started in this course is a curiosity about the topic of machine learning and a willingness to tinker around with your computer.

The course is taught by modules. Within each module, you'll have access to videos, short exercises, and a final capstone project. In Module 1, you'll begin by looking at different kinds of data. To help you explore the data, you'll dive right into some programming with the Python programming language. You don't need to have any programming background, we will guide you on how to leverage Python to explore and visualize any data.

Who should attend?


High school (grade 8) math

  • equations of lines and polynomial curves
  • finding average and standard deviation

Training content

  • How to represent and manipulate data
  • Reverse engineering nature
  • Distributions and Latent Variables
  • How machines think

Course delivery details

This course is offered through Massachusetts Institute of Technology, a partner institute of EdX.

3-6 hours per week


  • Verified Track -$49
  • Audit Track - Free

Certification / Credits

What you'll learn

  • Python programming and the Colab notebook programming environment
  • Dependent and independent variables
  • Coming up with relationships between data using linear and polynomial regression models
  • Recognizing how data is distributed
  • How to observe noise in distributions and when to ignore it
  • Categorize data into groups with classification models

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141 Portland Street
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