An AI Afternoon: The bare essentials of AI & Data Science – a one sitting read

A short but serious introduction to the field of AI and Data Science aimed to help business leaders keen on jumpstarting their AI journeys. In this short, one sitting afternoon read, you’ll find just enough material to help you take those initial steps towards adopting AI in your organization. This isn’t a treatise on AI, it is a compendium, to help you comfortably and confidently start off.

Seasoned practitioners would find the narrative of the book useful in explaining AI related concepts to their business stakeholders which we all understand is so very critical for success.

The table of contents for the book includes:

1.       To AI or not to AI

1.1.     That’s no longer a question

1.2.     AI Drivers

1.3.     Why is AI challenging

1.3.1.  Developing a vision is not easy

1.3.2.  Impact on organization’s projects portfolio

1.3.3.  Architectural Implications

1.3.4.  Change management

1.3.5.  Change the engine while it’s humming

1.3.6.  Talent

1.3.7.  Ethics, Trust and Transparency

2.       Demystifying AI

2.1.     Why so many buzzwords

2.2.     Taming the terminology

2.2.1.  Business Intelligence

2.2.2.  Business Analytics

2.2.3.  Descriptive, Inferential, Predictive and Prescriptive Analytics

2.2.4.  Data Science

2.2.5.  Machine Learning

2.2.6.  Artificial Intelligence

3.       Practical business applications

3.1.     Illustrative Use Cases

3.1.1.  Retail

3.1.2.  Manufacturing

3.1.3.  Healthcare

3.1.4.  Banking and Finance

3.1.5.  Telecom

4.       The AI Process

4.1.     AI process flow essentials

4.1.1.  Use case identification

4.1.2.  Cross-industry process for data mining (CRISP-DM)

5.       Concepts & algorithms overview

5.1.     Structured & Unstructured Data

5.2.     Statistics Overview

5.2.1.  On data and measurements

5.2.2.  On preliminary analysis

5.2.3.  On probability

5.2.4.  Estimation, Prediction and Forecast

5.2.5.  Taking out subjectivity – Hypothesis Testing

5.2.6.  Regression analysis

5.3.     Machine Learning Overview

5.4.     Deep Learning Overview

6.       Technology Overview

6.1.     Technology as an enabler

6.1.1.  Storage

6.1.2.  Compute

6.1.3.  Visualization

6.1.4.  Analytics toolset