Smarter Decisions – The Intersection of Internet of Things and Decision Science电子书

Smarter Decisions – The Intersection of Internet of Things and Decision Science

Smarter Decisions – The Intersection of Internet of Things and Decision Science

Credits

About the Author

About the Reviewer

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. IoT and Decision Science

Understanding the IoT

IoT in a real-life scenario

Demystifying M2M, IoT, IIoT, and IoE

Digging deeper into the logical stack of IoT

People

Processes

Technology

Software

Protocol

Infrastructure

Business processes

Things

Data

The problem life cycle

The problem landscape

The art of problem solving

The interdisciplinary approach

The problem universe

The problem solving framework

Summary

2. Studying the IoT Problem Universe and Designing a Use Case

Connected assets & connected operations

The journey of connected things to smart things

Connected assets - A real life scenario

Connected operations – The next revolution

What is Industry 4.0?

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

How is detergent manufactured?

What are the common issues that arise in the detergent manufacturing process?

What kind of machinery is used for the detergent manufacturing process?

What do we need to know more about the company, its production environment, and operations?

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)

Summary

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

End product related information

Manufacturing environment information

Raw material data

Operational data

Summarizing the data surfacing activity

Feature exploration

Understanding the data landscape

Domain context for the data

Exploring each dimension of the IoT Ecosystem through data (Univariates)

What does the data say?

Exploring Previous Product...

Summarizing this section

Studying relationships

So what is correlation?

Exploring Stage 1 dimensions

Revisiting the DDH matrix

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

What does a Type 1 error mean?

So what is ANOVA?

Validating hypotheses - category 3

So what is regression?

Hypotheses - category 3

Summarizing Exploratory Data Analysis phase

Root Cause Analysis

Synthesizing results

Visualizing insights

Stitching the Story together

Conclusion

Production Quantity

Raw material quality parameters

Resources/Machinery used in Stage 3

Assembly Line

Summary

4. Experimenting Predictive Analytics for IoT

Resurfacing the problem - What's next?

Linear regression - predicting a continuous outcome

Prelude

Solving the prediction problem

So what is linear regression?

Interpreting the regression outputs

F statistic

Estimate/coefficients

Standard error, t-value, and p value

Residuals, multiple R squared, residual standard error and adjusted R squared

What is the adjusted R-squared value?

Improving the predictive model

Let's define our approach

How will we go about it?

Let's being modeling

So how do we move ahead?

The important points to ponder are as follows:

What should we take care of?

So what next?

Decision trees

Understanding decision trees

So what is a decision tree?

How does a decision tree work?

What are different types of decision trees?

So how is a decision tree built and how does it work?

How to select the root node?

How are the decision nodes ordered/chosen?

How different is the process for classification and regression?

Predictive modeling with decision trees

So how do we approach?

So what do we do to improve the results?

So, what next? Do we try another modeling technique that could give us more powerful results?

Logistic Regression - Predicting a categorical outcome

So what is logistic regression?

So how does the logistic regression work?

How do we assess the goodness of fit or accuracy of the model?

Too many new terms?

Recap to the model interpretation

Improving the classification model

Let's define our approach

How do we go about it?

Let's begin modeling

So how do we move ahead?

Adding interaction terms

What can be done to improve this?

What just happened?

What can be done to improve the TNR and overall accuracy while keeping the TPR intact?

Summary

5. Enhancing Predictive Analytics with Machine Learning for IoT

A Brief Introduction to Machine Learning

What exactly is ensemble modeling?

Why should we choose ensemble models?

So how does an ensemble model actually work?

What are the different ensemble learning techniques?

Quick Recap - Where were we previously?

Ensemble modeling - random forest

What is random forest?

How do we build random forests in R?

What are these new parameters?

Mtry

Building a more tuned version of the random forest model

How?

Can we improve this further?

What can we do to achieve this?

Ensemble modeling - XGBoost

What is different in XgBoost?

Are we really getting good results?

What next?

A cautionary note

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?

Neurons

Edges

Activation function

Learning

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?

Packaging our results

A quick recap

Results from our predictive modeling exercise

Few points to note

Summary

6. Fast track Decision Science with IoT

Setting context for the problem

The real problem

What next?

Defining the problem and designing the approach

Building the SCQ: Situation - Complication - Question

Research

How does a solar panel ecosystem work?

Functioning

What are the different kinds of solar panel installations?

What challenges are faced in operations supported by solar panels?

Domain context

Designing the approach

Studying the data landscape

Exploratory Data Analysis and Feature Engineering

So how does the consumption fare in comparison with the generation?

Battery

Load

Inverter

Assimilate learnings from the data exploration exercise

Let's assimilate all our findings and learnings in brief

Solving the problem

Feature engineering

Building predictive model for the use case

Building a random forest model

Packaging the solution

Summary

7. Prescriptive Science and Decision Making

Using a layered approach and test control methods to outlive business disasters

What is prescriptive analytics?

What happened?

Why and how did it happen?

When will it happen (again)?

So what, now what?

Solving a prescriptive analytics use case

Context for the use case

Descriptive analytics - what happened?

Inquisitive analytics - why and how did it happen?

Predictive analytics – when will it happen?

The inception of prescriptive analytics

Getting deeper with prescriptive analytics

Solving the use case the prescriptive way

Test and control analysis

Implementing Test & Control Analysis in Prescriptive Analytics

Improving IVR operations to increase the call completion rate

Reducing the repeat calls

Staff training for increasing first call resolution rate

Tying back results to data-driven and heuristic-driven hypotheses

Connecting the dots in the problem universe

Story boarding - Making sense of the interconnected problems in the problem universe

Step 1 - Immediate

Step 2 - Future

Implementing the solution

Summary

8. Disruptions in IoT

Edge/fog computing

Exploring the fog computing model

Cognitive Computing - Disrupting intelligence from unstructured data

So how does cognitive computing work?

Where do we see the use of cognitive computing?

The story

The bigger question is, how does all of this happen?

Next generation robotics and genomics

Robotics – A bright future with IoT, Machine Learning, Edge & Cognitive Computing

Genomics

So how does genomics relate to IoT?

Autonomous cars

Vision and inspiration

So how does an autonomous car work?

Wait, what are we missing?

Vehicle - to - environment

Vehicle - to - vehicle

Vehicle - to - infrastructure

The future of autonomous cars

Privacy and security in IoT

Vulnerability

Integrity

Privacy

Software infrastructure

Hardware infrastructure

The protocol infrastructure

Summary

9. A Promising Future with IoT

The IoT Business model - Asset or Device as a Service

The motivation

Real life use case for Asset as a Service model

How does it help business?

Best case scenario

Worst case scenario

Neutral case

Conclusion

Leveraging Decision Science to empower the Asset as a Service model

Smartwatch – A booster to Healthcare IoT

Decision science in health data

Conclusion

Smart healthcare - Connected Humans to Smart Humans

Evolving from connected cars to smart cars

Smart refuel assistant

Predictive maintenance

Autonomous transport

Concluding thoughts

Summary