Essential Data Analytics, Data Science, and AI: A Practical Guide for a Data-Driven World - Tapa blanda

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Maxine Attobrah holds a bachelor’s degree in Electrical Engineering from the University of Massachusetts – Amherst. Maxine’s career began as an Electronic Flight Controls Engineer at a leading global security, defense, and aerospace contractor company, where she was responsible for developing and testing control system software to enhance helicopter piloting. Subsequently, Maxine pursued further education, earning master’s degrees in Electrical & Computer Engineering and Engineering & Technology Innovation Management from Carnegie Mellon University. Maxine started her career after graduating at a major global consulting firm as a Data Scientist and has since transitioned to the role of an AI/ML Engineer. Currently, she serves as a Lead AI/ML Engineer at this firm.

This book was prepared by the author in her personal capacity. The views and opinions expressed in this book are those of the author and do not necessarily reflect the official policy, opinion, or position of their present or past employers.

De la contraportada

In today's world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.

The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.

Whether you're a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.

What you will learn:

• What are Synthetic data and Telemetry data
• How to analyze data using programming languages like Python and Tableau.
• What is feature engineering
• What are the practical Implications of Artificial Intelligence