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Enabling Technologies for Data Science and Analytics: The Internet of Things

Provided by Columbia University (ColumbiaX)
See prerequisites
7–10 hours
per week, for 5 weeks

$149 USD for graded exams and assignments, plus a certificate

Discover the relationship between Big Data and the Internet of Things (IoT).

Before you start

  • High school math
  • Some exposure to computer programming

Choose your pace

Self-Paced courses contain assignments without due dates. You can progress at your own speed.

Steady Learners
80% complete in less than 17 weeks
Accelerated Learners
50% complete in less than 9 weeks
Course opens: May 28, 2019
Course ends: May 1, 2020

What you will learn

  • Networks, protocols and basic software for the Internet of Things (IoT)
  • How automated decision and control can be done with IoT technologies
  • Discuss devices including sensors, low power processors, hubs/gateways and cloud computing platforms
  • Learn about the relationship between data science and natural language and audio-visual content processing
  • Study research projects drawn from scientific journals, online media, and novels
  • Review fundamental techniques for visual feature extraction, content classification and high-dimensional indexing
  • Techniques that can be applied to solve problems in web-scale image search engines, face recognition, copy detection, mobile product search, and security surveillance
  • Examine data collection, processing and analysis


The Internet of Things is rapidly growing. It is predicted that more than 25 billion devices will be connected by 2020.

In this data science course, you will learn about the major components of the Internet of Things and how data is acquired from sensors. You will also examine ways of analyzing event data, sentiment analysis, facial recognition software and how data generated from devices can be used to make decisions.

Meet your instructors

Fred Jiang
Assistant Professor in the Electrical Engineering Department
Columbia University
Julia Hirschberg
Percy K. and Vida LW Hudson Professor of Computer Science
Columbia University
Michael Collins
Vikram S. Pandit Professor of Computer Science
Columbia University
Shih-Fu Chang
Richard Dicker Chair Professor
Columbia University
Zoran Kostic
Associate Professor of Professional Practice & Director of the MS EE Program
Columbia University
Kathy McKeown
Henry and Gertrude Rothschild Professor of Computer Science
Columbia University
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This course is part of:

Earn a Professional Certificate in 2-4 months if courses are taken one at a time.

View the program
  1. 35–50 hours of effort

    Learn how statistics plays a central role in the data science approach.

  2. 35–50 hours of effort

    Learn the principles of machine learning and the importance of algorithms.

  3. Enabling Technologies for Data Science and Analytics: The Internet of Things

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