A Practical Guide to Artificial Intelligence and Data Analytics

by Rayan Wali

Overview

Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of
Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a
different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics
touches on all of the fundamental topics you will need to understand deeper into machine learning and
artificial intelligence research, literature, and practical applications with its four parts:

Part I: A Conceptual (and Visual) Illustration [topics including, but not limited to, are listed below]
Fundamentals of Data Science
The Data and Machine Learning Pipelines
Data Preprocessing + Worked Data Preprocessing Strategy
Data Visualization
Python for Data Analysis
Calculus & Linear Algebra Fundamentals
Data Structures and Algorithms Exercises
Machine Learning Models & Algorithms (kNN, Neural Networks, Hidden Markov Models, Ensemble Methods, etc.)
Deep Learning for Computer Vision & NLP (CNNs, RNNs, etc.)
Data Mining
Model Deployment
Time Series Data Analysis
AI Systems in the Real-World
Applications of Data Analysis Exercises
Database Systems & Cloud Computing (with practical example)
Functional Programming for Data Analytics

Part II: 10 Full-Length Case Studies
Case Study I: Sports Web Scraping
Case Study II: NLP Textual Analysis
Case Study III: Emergency Response Duration Analysis
Case Study IV: MNIST Image Classification
Case Study V: COVID-19 Statistical Data Analysis
Case Study VI: COVID-19 Chest X-Ray Screening
Case Study VII: Signal Strength Geospatial Analysis
Case Study VIII: NYC Crash Accidents Data Analysis
Case Study IX: Sales Forecasting
Case Study X: Meteorite Landings Analysis

Part III: Mixed Exercises
This section consists of 60+ exercises designed to reinforce the content from the knowledge gained in
Part I and the practical case studies in Part II. These exercises are strategically constructed to cover a wide
range of topics, from statistics to machine learning to cloud computing, and help you prepare for AI and Data
Analytics interviews.

Part IV: A Full-Length Data Science and Analytics Skills Assessment (DSSA)
With exercises that span a wide range of AI problems from different domains, from the economics and
finance to transportation and medical industries, the DSSA aims to provide a comprehensive assessment to
measure your understanding through cleverly-designed AI reasoning, problem-solving, and scenario-based
exercises, whether you use it to enhance your understanding in the AI and Data Analytics field or use it to
prepare for your AI/Data Analytics problem solving and system design interviews.

Section I: 60 Multiple-Choice and Short-Answer Exercises
Section II: 5 AI & Data Analytics Problem Solving and Coding Exercises
Solutions to Sections I and II are included

With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book
simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design
skills.

Product Details

ISBN-13:	9798403640718
Publisher:	Rayan S. Wali
Publication date:	01/17/2022
Edition description:	2nd ed.
Pages:	602
Product dimensions:	8.50(w) x 11.00(h) x 1.22(d)

About the Author

Rayan S. Wali is an AWS software engineer at Amazon Inc. He holds a Bachelor’s Degree of Computer
Science and Applied Economics from Cornell University. At his time at Cornell, he served as an
Educational Facilitator for Data Structures and Multivariable Calculus and a Teaching Assistant for
Operating Systems. He has interest in Artificial Intelligence research and developing Machine Learning
systems, and is constantly working on new projects and designing novel algorithms with the hope to
improve current AI technologies.

He launched A Practical Guide to AI and Data Analytics in 2021, with the goal of improving the data
science education. The ten practical case studies and the full-length assessment at the end of the book
are designed to further strengthen data science and analytics concepts by giving diverse approaches to
problems. Alongside this book, he launched Functionally Pragmatic Programming in 2021.