Feb 20, 2023

Books every data analyst must read!

You can get a head start right now by reading some of the latest data science books on the market. We’ll discuss the best data science books available so you can add them to your reading list and get up to speed on the data science revolution.

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Explore : How to become a Data Analyst in 2023 ?

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3 Books every Data Analyst must Read!

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📌 1. Data Analytics Made Accessible by Dr. Anil Maheshwari

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This book will help you guide concepts of Data Analytics. There are some real-world scenarios & the tools that you can use to learn more about Data Analytics. The chapters in this book are organized for a typical one-semester course. The book includes case studies from real-world stories at the beginning of each chapter.

As an exercise, there is also an ongoing case study for the chapter. The purpose of this book is to provide students with the intuition behind this evolving field, along with a robust toolset of leading data mining techniques and platforms. The 2022 edition includes a summary chapter that summarises the entire book in just 50 key points on a few pages. Finally, an R tutorial and a Python tutorial are included. Includes advanced foundations in big-data, artificial intelligence, careers in data science, data ownership and privacy.

The 2022 edition will be updated in many respects related to Artificial Intelligence. It covers topics such as data lakes and data sharing practices. This ever-evolving book has proven to be very popular around the world.

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Who can Read this :

It can be used as a textbook by students in a variety of academic disciplines, including economics, Computer Science, Statistics, Engineering, and more, who are fascinated by discovering new knowledge and ideas from data. Experts from a wide variety of disciplines, including Executives, Managers, Analysts, Professors, Doctors, and Accountants can use this book to learn how to meaningfully interpret large amounts of data and generate actionable insights in just a few hours.

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📌 2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

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Data Science for Business, authored by eminent data science experts Foster Provost and Tom Fawcett, introduces The Fundamental Principles of Data Science and provides the necessary knowledge and business value to derive useful knowledge and business value from the extracted data you collect.

‍It guides you through the concept of Data Analysis. This guide will also help you understand the many data mining techniques in use today. Building on his MBA course, which Provost has taught at New York University for the past ten years, Data Science for Business provides examples of real business problems to illustrate these principles.

‍It helps you learn not only how to improve communication between business stakeholders and data scientists, but also how to participate intelligently in your company's data science projects. You will also learn how to think analytically about your data and understand how data science techniques can support your business decisions. Understand how data science fits into your business and how to use it to gain a competitive advantage collect great data in the most appropriate way. Learn general concepts for actually extracting knowledge from data.

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Who can Read this :

This book is recommended for beginners and intermediate professionals who would like to learn data analytics without profuse usage of mathematics. This book is not a guide for Hadoop or Big Data concepts and technologies.

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📌3. Data Analytics: Principles, Tools, and Practices by Dr. Gaurav Aroraa, Chitra Lele, Dr. Munish Jindal

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Today, there is a need to solve critical problems related to Data and Data Science. We are looking for professionals who can solve Real-World Data Science Problems using data science tools.

The book "Data Analytics: Principles, Tools, and Practices" can be considered a handbook or guide for professionals who want to start their journey into data science. The journey begins with the introduction of DBMS, RDBMS, NoSQL and Document DB. This book introduces the Fundamentals of Data Science and Modern Ecosystems, including key steps such as Data Ingestion, Data Modification, and Visualization. The book covers different types of analytics, different tools in the Hadoop ecosystem such as Apache Spark, Apache Hive, R, MapReduce, NoSQL Database. It also covers various machine learning techniques that help in Data Analysis and how to visualize data using various graphs and charts.

‍This book describes useful tools and approaches for data analysis, supported by concrete code examples. After reading this book, you will be motivated to explore real-world data analysis and apply what you have learned about databases, BI/DW, data visualization, big data tools, and statistical science.

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Who can Read this :

This book is recommended for Professionals who can use Data Science tools to solve real-world data science problems.

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Bonus!!📘Books you must read!

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📌 4. A Common-Sense Guide to Data Structures and Algorithms, 2nd Edition, by Jay Wengrow

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This hands-on guide to Data Structures and Algorithms goes beyond theory and will help you greatly improve your programming skills. Read this Data Science book to learn how to use hash tables, trees, and charts to improve the efficiency of your code. Each chapter contains hands-on exercises so you can practice what you have learned before proceeding on to the next continuation topic.

Beginners will learn how to use these techniques from scratch, and experienced developers will rediscover forgotten approaches. Algorithms and data structures are more than abstract concepts. Mastering them will enable you to write code that runs faster and more efficiently. This book is especially important for it's web and mobile apps today.

The book provides a hands-on approach to data structures and algorithms using real-world techniques and scenarios that can be used in everyday production code. Graphics and examples make these computer science concepts easy to understand and relevant. Use Big-O Notation, the primary tool for evaluating algorithms, to measure and articulate the efficiency of your code, and modify your algorithms to make them faster. Find out how your choices for arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than alternatives.

Jay Wengrow brings to this book key teaching methods he developed as a web development bootcamp founder and instructor. Use these techniques today to make your code faster and more scalable.

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Who can Read this :

This book is recommended for beginners and intermediates who wants to improve their skills in Data Structures and Algorithms.

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📌 5. Essential Math for Data Science: Calculus, Statistics, Probability Theory, and Linear Algebra by Hadrien Jean

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It's possible to get into data science without a full understanding of the core mathematics, but a truly effective and well-rounded data scientist requires a solid math foundation. Hadrien Jean's Essential Math for Data Science aims to explain the mathematics at the heart of data science, machine learning, and deep learning. Whether you're a data scientist with no math background, or a developer looking to add data analytics to your toolkit, this book will help you master the math and develop your data science skills.

Master the math you need to excel in Data Science and Machine Learning. If you are a data scientist with no background in math or science, or a developer looking to add the data domain to your skillset, this is your book. Author Hadrien Jean explains the mathematical foundations of Data science, Machine learning, and Deep learning.

‍Through this book, you will learn how to use mathematical notation to understand new developments in the field, communicate with colleagues, and solve problems in mathematical terms. It also helps you understand what's behind the algorithms you use. Learn how: Plot data, represent equations, and visualize spatial transformations using Python and Jupyter notebooks. Read and write mathematical notation to communicate data science and machine learning ideas Use machine learning and deep learning libraries such as TensorFlow and Keras Identify the reasons behind flawed models, optimize and fix them prepare to Choosing the Right Tool or Algorithm for the Right Data Problem.

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Who can Read this :

This book is recommended for those who want a solid command on Mathematics . This is applicable for beginners, intermediate as well as those who have no math or science background.

We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.

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