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Essential data analytics, data scien...

Attobrah, Maxine.

Essential data analytics, data science, and AIa practical guide for a data-driven world /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essential data analytics, data science, and AIby Maxine Attobrah.
Reminder of title:	a practical guide for a data-driven world /
Author:	Attobrah, Maxine.
Published:	Berkeley, CA :Apress :2024.
Description:	xx, 211 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electronic data processing.
Online resource:	https://doi.org/10.1007/979-8-8688-1070-1
ISBN:	9798868810701$q(electronic bk.)

Essential data analytics, data science, and AIa practical guide for a data-driven world /
Attobrah, Maxine.

Essential data analytics, data science, and AIa practical guide for a data-driven world /[electronic resource] :by Maxine Attobrah. - Berkeley, CA :Apress :2024. - xx, 211 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Obtaining Data -- Chapter 3: ETL Pipeline -- Chapter 4: Exploratory Data Analysis -- Chapter 5: Machine Learning Models -- Chapter 6: Evaluating Models -- Chapter 7: When To Use Machine Learning Models -- Chapter 8: Where Machine Learning Models Live -- Chapter 9: Telemetry -- Chapter 10: Adversaries and Abuse -- Chapter 11: Working With Models.

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.

ISBN: 9798868810701$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1070-1doiSubjects--Topical Terms:

201945
Electronic data processing.

LC Class. No.: QA76

Dewey Class. No.: 004

Essential data analytics, data science, and AIa practical guide for a data-driven world /
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505 0 $a Chapter 1: Introduction -- Chapter 2: Obtaining Data -- Chapter 3: ETL Pipeline -- Chapter 4: Exploratory Data Analysis -- Chapter 5: Machine Learning Models -- Chapter 6: Evaluating Models -- Chapter 7: When To Use Machine Learning Models -- Chapter 8: Where Machine Learning Models Live -- Chapter 9: Telemetry -- Chapter 10: Adversaries and Abuse -- Chapter 11: Working With Models.
520 $a 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.
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