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Dr. Vishnu  Nanduri, Instructor - Analytics for the Internet of Things

Dr. Vishnu Nanduri

Dr. Vishnu brings more than a 13 years of experience in machine learning and predictive analytics. He holds a MS and a PhD in Industrial Engineering from the University of South Florida, and has held positions in both Industry and Academia. He has worked in University of Wisconsin -Milwaukee, Impetus, and IBM.

Learn how to apply basic analytical methods to IoT Data

  • Learn how to manipulate and analyze IoT Data and explore different use cases in IoT.
  • Instructor is an advanced analytics expert who has held various positions as a researcher and professor at University of Wisconsin-Milwaukee and as a Senior data scientist at Impetus and a Practice leader for Data Science in IT Operations Analytics at IBM. 

Duration: 2h 6m

Course Description

According to the Internet of Things Global Standards Initiative, IoT is defined as the network of physical objects or things embedded with electronics, software, sensors, and network connectivity, which enable these objects to collect and exchange data. As a recent report by McKinsey pegs the potential economic impact of IoT at $11 trillion by 2025, the most important ingredient of the IoT boom is considered to be the data generated by the collection of sensors. This course will first give an introduction to the concept of IoT and then provide students with the knowledge of how to handle the data deluge related to IoT. It’s geared towards engineers who would like to understand how to utilize the data generated by IoT technologies, or executives who would like to gain some insights to different applications this data can be used for.

What am I going to get from this course?

  • Apply the fundamentals of machine learning and statistics to extract value from IoT data
  • Understand different business use-cases for IoT data 
  • Understand different types of IoT data

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Basic understanding of Statistics 
  • Basic programming knowledge
  • Basic understanding of R programming language

Who should take this course? Who should not?

  • Engineers who would like to understand the methods used to analyze IoT data in large scale
  • Executives who would like to understand how to utilize IoT data to create business value


Module 1: Intro to Analytics for the Internet of Things

Lecture 1 Intro to Analytics for the Internet of Things

This lecture will touch upon the common use cases in IoT, the type of data sensors generate & the analytical techniques used to generate insights from this data, and how to drive business value using analytics.

Module 2: Clustering

Lecture 2 Clustering

Clustering is one of the most commonly-used unsupervised learning methods. This lecture gives the audience some explanation of the most common algorithms in clustering, and solves some example problems.

Module 3: Decision Trees

Lecture 3 Decision Trees

The concept of decision trees is an integral part of the supervised learning domain. This module first gives an explanation of what a decision tree is, then gives some examples which this concept can be applied to.

Module 4: Random Forests

Lecture 4 Random Forests

This is an introductory lecture about the Random Forests method. It starts off with the basic theory behind the method, gives some explanation about the advantages/disadvantages the method has when compared to Decision Trees, and then dives deep into the algorithm with the help of some real life examples.

Module 5: Regression

Lecture 5 Regression

An overview of how simple multiple regression and multiple linear regression work, and how these two can be used on IoT data.

Module 6: SVD

Lecture 6 SVD

This lecture explains what SVD (Singular Value Decomposition) is, how to interpret it, and gives us an example by implementing the SVD method on a recommender system.

Module 7: Time Series Modeling for IoT Data

Lecture 7 Time Series Modeling for IoT Data

An overview of the three commonly used methods in time series modeling: Moving Averages, Exponential Smoothing and Holt-Winters Exponential smoothing, all backed by real-life examples.


10 Reviews

Matthew J

December, 2016

I took this course because I am hoping to transition to the IoT industry since it is booming. I was looking for a course that started with the basic, but also dived into the advances areas of IOT analytics. This course is exactly what I seeking. Instructor has a well defined curriculum that has a great easy to follow flow. A lot is covered in this course, but the instructor teaching style does not confuse you and is very focused

Eveline T

December, 2016

Charlotte H

May, 2017

This course was nicely structured and helps really imparting excellent knowledge on how to analyze Internet of Things data and manipulate it in various IoTuse cases. Since the instructor is a professional teacher as well, he did not miss any details needed in the analytics for the IoT. With his way of teaching, it was not difficult for me to understand machine learning and types of IoT data, their fundamentals, and statistics to extract values.

Michael S

May, 2017

All in all an excellent course for those who want to pursue their careers in the Internet of Things technologies. It was really exciting to learn about sensors, software, network connectivity and how the physical objects collect and exchange data.

Wishes M

May, 2017

As I was very much interested in IOT, I participated in learning from this course. And I was not disappointed, as I could learn much more than I expected.

wooyoung S

July, 2017

This course of study needs a broad understanding of Electrical Engineering to understand what's going on. Kindly make it a bit interactive, and increase the number of handouts, so that we can refer to them easily, when we need.

Mike G

July, 2017

Carrying on with significant degree of analysis of IOT was a very good plan. better visualization and shorter topics could have been better.

Cesar T

July, 2017

I thank Experfy for paying attention to our feedback, and happy to see some improvements. Learning this was significantly more challening for me. This course is better than I expected

John C

July, 2017

The program is a treasure, incorporating most of IOT and analytics. For me as a working professional, I find it more demanding, when the instructor is carrying on with his presentations. A better collective system is required while explaining IOT network communication systems. For someone away from academic field for long , we need a more sophisticated graphic presentation. Besides this, I believe this study is far better than a traditional data science course available online.

Peter V

October, 2017

This course really introduced me to the fundamentals of analytics techniques that can be used to analyse IoT type data. It provided an overview of potential use cases in IoT. I really think this course is a stepping stone to more advanced IoT courses. While this might not be for everyone, I definitely recommend this to anyone who's looking to build a foundation in IoT analytics.