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Instructor

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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.

Analytics for the Internet of Things

Instructor: Dr. Vishnu Nanduri

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. 

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

Curriculum

Module 1: Intro to Analytics for the Internet of Things
Lecture 1 Intro to Analytics for the Internet of Things
19:37

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
19:19

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
16:28

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
11:59

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
19:35

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
20:37

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
18:14

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.

Reviews

5 Reviews

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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

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Eveline T

December, 2016

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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.

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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.

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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.

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