Enter the world of Internet of Things with the power of data science with this highly practical, engaging book
About This Book
- Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, practical book
- Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense
- This highly practical, example-rich guide fills the gap between your knowledge of data science and IoT
Who This Book Is For
If you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a non-technical manager anchoring IoT projects, you can skip the code and still benefit from the book.
What You Will Learn
- Explore decision science with respect to IoT
- Get to know the end to end analytics stack ? Descriptive + Inquisitive + Predictive + Prescriptive
- Solve problems in IoT connected assets and connected operations
- Design and solve real-life IoT business use cases using cutting edge machine learning techniques
- Synthesize and assimilate results to form the perfect story for a business
- Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R
With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.
The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.
By the end of this book, you'll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Style and approach
This scenario-based tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.
Table of contents
Smarter Decisions – The Intersection of Internet of Things and Decision Science
- Smarter Decisions – The Intersection of Internet of Things and Decision Science
- About the Author
- About the Reviewer
- eBooks, discount offers, and more
1. IoT and Decision Science
- Understanding the IoT
- Demystifying M2M, IoT, IIoT, and IoE
- Digging deeper into the logical stack of IoT
- The problem life cycle
- The problem landscape
- The art of problem solving
- The problem solving framework
2. Studying the IoT Problem Universe and Designing a Use Case
- Connected assets & connected operations
Defining the business use case
- Defining the problem
Researching and gathering context
- Gathering context - examining the type of problem
- Gathering context - research and gather context
- Research outcome
- Prioritize and structure hypotheses based on the availability of data
- Validating and Improving the hypotheses (iterate over #2 and #3)
- Assimilate results and render the story
- Sensing the associated latent problems
- Designing the heuristic driven hypotheses matrix (HDH)
3. The What and Why - Using Exploratory Decision Science for IoT
Identifying gold mines in data for decision making
- Examining data sources for the hypotheses
- Data surfacing for problem solving
- Feature exploration
- Understanding the data landscape
- Exploring each dimension of the IoT Ecosystem through data (Univariates)
- Studying relationships
Exploratory data analysis
- So how do we validate our findings?
- So how does hypothesis testing work?
- Validating hypotheses - category 1
- How does the chi-squared test work in a nutshell?
- Validating hypotheses - category 2
- Validating hypotheses - category 3
- Hypotheses - category 3
- Summarizing Exploratory Data Analysis phase
- Root Cause Analysis
- Identifying gold mines in data for decision making
4. Experimenting Predictive Analytics for IoT
- Resurfacing the problem - What's next?
Linear regression - predicting a continuous outcome
- Solving the prediction problem
- Interpreting the regression outputs
- Residuals, multiple R squared, residual standard error and adjusted R squared
- Improving the predictive model
- Understanding decision trees
- Predictive modeling with decision trees
Logistic Regression - Predicting a categorical outcome
- So what is logistic regression?
- So how does the logistic regression work?
- Recap to the model interpretation
- Improving the classification model
5. Enhancing Predictive Analytics with Machine Learning for IoT
- A Brief Introduction to Machine Learning
- Ensemble modeling - random forest
- Ensemble modeling - XGBoost
Neural Networks and Deep Learning
So what is so cool about neural networks and deep learning?
- What is a neural network?
- So what is deep learning?
- So what problems can neural networks and deep learning solve?
- So how does a neural network work?
- Activation function
- So what are the different types of neural networks?
- How do we go about modeling using a neural network or deep learning technique?
- What next?
- What have we achieved till now?
- So what is so cool about neural networks and deep learning?
- Packaging our results
6. Fast track Decision Science with IoT
- Setting context for the problem
Defining the problem and designing the approach
- Building the SCQ: Situation - Complication - Question
- Domain context
- Designing the approach
- Studying the data landscape
- Exploratory Data Analysis and Feature Engineering
- Building predictive model for the use case
- Packaging the solution
7. Prescriptive Science and Decision Making
Using a layered approach and test control methods to outlive business disasters
- What is prescriptive analytics?
- Solving a prescriptive analytics use case
- Solving the use case the prescriptive way
- Connecting the dots in the problem universe
- Story boarding - Making sense of the interconnected problems in the problem universe
- Implementing the solution
- Using a layered approach and test control methods to outlive business disasters
8. Disruptions in IoT
- Edge/fog computing
- Cognitive Computing - Disrupting intelligence from unstructured data
- Next generation robotics and genomics
- Autonomous cars
- Privacy and security in IoT
9. A Promising Future with IoT
- The IoT Business model - Asset or Device as a Service
- Smartwatch – A booster to Healthcare IoT
- Smart healthcare - Connected Humans to Smart Humans
- Evolving from connected cars to smart cars
- Title: Smarter Decisions – The Intersection of Internet of Things and Decision Science
- Release date: July 2016
- Publisher(s): Packt Publishing
- ISBN: 9781785884191
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