| title | excerpt | publishDate | category |
|---|---|---|---|
The Best Data Science Books |
Uncover the top data science books for readers looking to enhance their knowledge in this growing field, featuring in-depth reviews, expert recommendations, and insights into the latest industry trends. |
2024-05-18T09:51:38Z |
Welcome to our book roundup feature, where we delve into the world of data science and share some of the most insightful and practical books available. In this article, we present you with a collection of top data science books that cater to diverse skill levels and interests, ensuring that you find the perfect companion for your data-driven journey. So, grab a cup of coffee and let's dive into the world of data science books!
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Discover the secrets and possibilities of data science with "What Is Data Science? " by Mike Loukides. This concise report delves into the dynamic new field and its rapid growth in recent years. In a time where Hal Varian declared "statistics is the next sexy job" and Tim O'Reilly famously said, "data is the next Intel Inside, " it's essential to understand the significance of data in our modern world.
This book unravels the complexities of data science, exploring its technologies, companies, and unique skill sets. From the birth of "data-driven apps" to the creation of valuable data products, "What Is Data Science? " invites readers to join the revolution of data-driven innovation. Don't miss out on this thought-provoking and intriguing exploration of a rapidly changing landscape.
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Authors: Mike Loukides
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Publisher: "O'Reilly Media, Inc."
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Published Date: April 10, 2011
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Page Count: 25
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Print Type: BOOK
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Categories: Computers
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Average Rating: 4.5
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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What You Need to Know about Data Mining and Data-Analytic Thinking
"Data Science for Business" by Foster Provost and Tom Fawcett offers an in-depth dive into the world of data science without overwhelming readers with technical jargon. This approachable guide covers essential data mining techniques, and more importantly, the data-analytic thinking necessary to derive significant knowledge and business value from data. The 414-page book is a must-read for those looking to employ the powerful principles of data science in solving business problems.
Published by O'Reilly Media, Inc. , the book has an impressive average rating of 4.0 and can be found under the Business & Economics category.
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Authors: Foster Provost, Tom Fawcett
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Publisher: "O'Reilly Media, Inc."
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Published Date: July 27, 2013
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Page Count: 414
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Print Type: BOOK
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Categories: Business & Economics
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Average Rating: 4.0
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Ratings Count: 3.0
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Maturity Rating: NOT_MATURE
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Language: en
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Straight Talk from the Frontline
In an era where data has the power to influence elections and revolutionize business models, "Doing Data Science: Straight Talk from the Frontline" is a must-read for anyone interested in this interdisciplinary field. This informative guide, a collaboration between Columbia University's Introduction to Data Science class and industry experts, provides a detailed overview of the industry and offers valuable insight into how data science can be applied.
Featuring case studies and code examples from leading companies like Google, Microsoft, and eBay, this book covers essential topics such as statistical inference, exploratory data analysis, and data modeling, while also discussing cutting-edge concepts related to data visualization, social networks, and data journalism. Written by experts Catherine O'Neil and Rachel Schutt, "Doing Data Science" is an ideal introduction for those with a background in linear algebra, probability, and statistics who are looking to enter the exciting world of data science.
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Authors: Cathy O'Neil, Rachel Schutt
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Publisher: "O'Reilly Media, Inc."
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Published Date: October 09, 2013
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Page Count: 408
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Print Type: BOOK
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Categories: Computers
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Average Rating: 4.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Using Data Science to Transform Information into Insight
"Data Smart: Using Data Science to Transform Information into Insight" by John Foreman is the perfect guide for those who want to delve into the world of data science without the need for extensive technical knowledge or expensive software. This book demystifies data science by presenting it in a step-by-step format using a familiar tool - the spreadsheet.
In "Data Smart, " readers will gain insight into various data-driven techniques and processes, including mathematical optimization, clustering, data mining in graphs, supervised artificial intelligence, forecasting, and more. Each chapter focuses on a specific technique, allowing readers to follow along and apply their newfound knowledge to real-world situations. With humor and engaging anecdotes, John Foreman makes the concepts accessible to anyone, even those with little to no prior experience in the field.
As the world becomes increasingly data-driven, "Data Smart" empowers readers with the necessary skills to make informed decisions and drive their businesses forward. With its comprehensive approach and easy-to-understand language, this book is a must-read for anyone looking to explore the world of data science and its potential applications.
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Authors: John W. Foreman
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Publisher: John Wiley & Sons
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Published Date: October 31, 2013
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Page Count: 432
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Print Type: BOOK
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Categories: Business & Economics
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Maturity Rating: NOT_MATURE
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Language: en
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Facing the Future with Time-Tested Tools
"Data Science at the Command Line" is a revolutionary guide that showcases the agility and efficiency of the command line for data scientists. Written to empower you with flexible, time-tested tools, this book is a must-read for anyone interested in streamlining their data science workflow.
Beginning with an overview of the Data Science Toolbox, Jeroen Janssens offers insight into an easily installable environment filled with over 80 command-line tools, designed to maximize your data science capabilities regardless of your operating system.
The book is filled with practical examples on how to use the command line to quickly obtain, scrub, explore, and model your data, providing a comprehensive understanding of the potential of this powerful technology. Learn how to work with various data types, including plain text, CSV, HTML/XML, and JSON, as well as how to leverage numerous command-line tools for data manipulation and exploration.
One of the book's standout features is its coverage of Drake, a tool for managing your data science workflow. By providing reusable tools and transforming one-liners into fully functional Python or R code, Drake enhances your productivity and simplifies your daily work.
Moreover, the book explains how to parallelize and distribute data-intensive pipelines using GNU Parallel, providing a clear overview of how to execute model data with dimensionality reduction, clustering, regression, and classification algorithms.
Whether you're new to data science or an experienced professional, "Data Science at the Command Line" opens up a world of possibilities and innovative solutions that will transform the way you handle and analyze your data. Get ready to face the future of data science and experience the powerful capabilities of the command line.
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Authors: Jeroen Janssens
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Publisher: "O'Reilly Media, Inc."
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Published Date: September 25, 2014
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Page Count: 251
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Data Scientists at Work is a comprehensive collection of interviews with sixteen prominent data scientists from diverse industries. This book offers glimpses into the fascinating world of data science, exploring its applications in social media, venture capital, neuroscience, and more. The interviews dive into the interviewees' personal journeys and the strategies they have developed to transform colossal amounts of data into valuable insights for their employers and clients.
This insightful book offers readers a unique perspective on the future of this ever-evolving profession, presented by its most innovative thought leaders.
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Authors: Sebastian Gutierrez
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Publisher: Apress
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Published Date: December 12, 2014
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Page Count: 348
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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First Principles with Python
"Getting Started in Data Science" by Joel Grus is an extensive manual that aids users in comprehending the rudimentary tenets of data science through Python. This publication is tailor-made for those who wish to engage intimately with data science while not solely relying on libraries, frameworks, and tools.
The book commences by elucidating how diverse data science tools and algorithms work by developing them independently. This hands-on technique ensures readers acquire profound knowledge of data science theories. Grus directs you through the fundamentals of linear algebra, statistics, and probability, demonstrating how these crucial components are utilized in data science.
Equipped with a robust mathematical and programming foundation, readers can delve into the domain of data science. They will learn to amass, scrutinize, purge, and manipulate data from various sources. "Getting Started in Data Science" then invites you on an enthralling voyage into the nucleus of machine learning, where you'll learn to develop models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering.
As you advance, you will explore recommender systems, natural language processing, network analysis, MapReduce, and databases. This book serves as a comprehensive course on Python, and it offers a comprehensive initiation to the universe of data science, addressing every facet.
"Getting Started in Data Science" is an captivating read for novices and veterans alike, as it offers an exceptional concatenation of theory and practical application. With this book, you'll be adept to unearth the concealed secrets in today's chaotic sea of data and emerge as a skilled data scientist.
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Authors: Joel Grus
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Publisher: "O'Reilly Media, Inc."
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Published Date: April 14, 2015
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Page Count: 330
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Import, Tidy, Transform, Visualize, and Model Data
Unleash your data's potential and transform it into valuable insights with R for Data Science - the essential guide for novice and experienced data scientists alike! Written by highly respected authors Hadley Wickham and Garrett Grolemund, this comprehensive book takes you on a journey through the power of R, RStudio, and the tidyverse. Designed for those with no prior programming experience, R for Data Science is the perfect launchpad to jumpstart your data science journey.
As you progress through the book, you'll be introduced to the fundamental steps of importing, wrangling, exploring, and modeling your data. This hands-on approach ensures that you'll quickly gain a high-level understanding of the data science process, while also mastering the essential tools to manage the details.
Participate in engaging exercises throughout each section to reinforce your learning and ensure a solid grasp on the concepts. By the end of R for Data Science, you'll have the skills to wrangle your data, program with R, explore your data, model your results, and effectively communicate your findings to others.
Get ready to revolutionize the way you work with data by unlocking the full potential of R for Data Science!
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Authors: Hadley Wickham, Garrett Grolemund
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Publisher: "O'Reilly Media, Inc."
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Published Date: December 12, 2016
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Page Count: 521
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Embark on a comprehensive journey through the world of data science with Principles of Data Science. This accessible and engaging book is your guide to making sense of your data, bridging the gap between mathematics and programming, and transforming raw information into valuable insights.
Designed for readers with basic algebra skills and experience in coding as well as those looking to master data science techniques, this book takes you through the entire data science pipeline. From cleaning and preparing data to building powerful visualizations, you'll learn the skills needed to understand and apply data science principles in real-world scenarios.
Principles of Data Science is divided into several chapters, which follow a step-by-step approach to introduce you to essential concepts, such as data cleaning, data mining, and statistical modeling. The book also covers machine learning techniques and effective metrics to evaluate your machine learning models' performance.
With a focus on hands-on learning, this engaging tutorial includes use cases, examples, and illustrations to help you grasp the fundamental principles of data science. Sinan Ozdemir, the author, takes the time to introduce advanced concepts and demonstrates how to apply them in practice, providing a strong foundation for your data science journey.
Don't miss your chance to unlock the power of data science. With Principles of Data Science as your comprehensive and accessible guide, you'll be well-equipped to navigate complex data analysis and gain a competitive edge in today's data-driven world.
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Authors: Sinan Ozdemir
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Publisher: Packt Publishing Ltd
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Published Date: December 16, 2016
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Page Count: 389
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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The Data Science Handbook offers a comprehensive overview of the discipline, covering essential analytics, programming, and business skills. In this accessible guide, data science expert Field Cady demystifies the field and teaches readers how to combine technical skills into a unified discipline.
Unlike many analytics books, this one delves into computer science and software engineering, explaining classic machine learning algorithms and their applications, as well as the importance of visualization tools. The book also addresses the practical realities of data science and offers real-world case studies, making it a valuable resource for data analysis methodology and big data software tools.
Ideal for data analysis professionals, software professionals, statisticians, and researchers, this handbook serves as an invaluable guide for anyone looking to expand their skill set in data science.
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Authors: Field Cady
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Publisher: John Wiley & Sons
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Published Date: February 28, 2017
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Page Count: 420
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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Unlock the secrets of data science with Data Science For Dummies - the ultimate guide to gain in-depth insight into your business in an easy and accessible way. This book is perfect for IT professionals and students who want a quick primer on all the exciting areas of the rapidly growing data science space.
Written with a focus on business cases, the book takes you on a wild journey through big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. With chapters covering big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL, you'll learn about machine learning and artificial intelligence, and the evolution of the Internet of Things.
Plus, discover data visualization techniques to effectively communicate your valuable data insights. Let Data Science For Dummies be your trusty companion in navigating the broad, sometimes overwhelming field of big data and data science, and gain a competitive edge for your organization.
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Authors: Lillian Pierson
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Publisher: John Wiley & Sons
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Published Date: March 06, 2017
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Page Count: 384
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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"The Data Science Design Manual" by Steven S. Skiena is a must-read for anyone eager to grasp the fundamentals of this rapidly growing interdisciplinary field. This well-crafted textbook/reference offers a comprehensive introduction to data science, focusing on key concepts and skills essential to building successful data systems.
Skiena's engaging writing style provides an intuitive understanding of the core principles within data science and how they can be applied effectively. The book does not limit itself to a particular programming language or suite of data analysis tools, instead offering a high-level discussion of design principles that truly matter.
This easy-to-read book caters perfectly to undergraduate and early graduate students embarking on an "Introduction to Data Science" course, illustrating how the discipline sits at the intersection of statistics, computer science, and machine learning.
In addition to practical insights, "The Data Science Design Manual" also features real-world examples, such as "War Stories, " which give perspectives on how data science applies in the world of business and beyond. The book is further enhanced with homework problems and a complete set of lecture slides available online, designed to assist self-study and enhance understanding.
Practitioners in related fields will likely find this book of immense value as well, highlighting the importance of the big-picture concepts presented in each chapter. Recommended for anyone aiming to delve into the intriguing world of data science, "The Data Science Design Manual" is a comprehensive guide and invaluable resource in this ever-evolving, high-impact field.
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Authors: Steven S. Skiena
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Publisher: Springer
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Published Date: July 01, 2017
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Page Count: 445
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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"Data Science" by John D. Kelleher and Brendan Tierney is a concise introduction to the rapidly evolving field that explores its origins and future. This MIT Press Essential Knowledge volume covers the evolution of data science, its relation to machine learning, and its application in real-world scenarios. Data science has revolutionized digital life, from targeted ads on our screens to personalized recommendations for books and movies, even healthcare costs.
The book provides insights into data infrastructure issues and ethical challenges, showing how data science is transforming modern life. With the rise of big data and social media, data analysis and modeling methods like deep learning have become indispensable. Data science also encompasses the process of extracting valuable patterns from large datasets, with close links to data mining and machine learning.
From a historical standpoint, it discusses the stages in a data science project, from understanding the problem to creating and using machine learning algorithms. The book also delves into the challenges of integrating data from diverse sources, and offers a comprehensive overview of machine learning principles.
Furthermore, "Data Science" reviews the legal and ethical landscape, discussing data regulation and privacy preservation, vital elements in the age of data. The forthcoming impact of data science on our future is also elaborated, along with the key to successful data science projects. Readers will leave this concise volume with a full understanding of data science and its transformative power.
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Authors: John D. Kelleher, Brendan Tierney
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Publisher: MIT Press
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Published Date: April 13, 2018
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Page Count: 280
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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"Data Science" by John D. Kelleher and Brendan Tierney provides a comprehensive yet concise introduction to the rapidly evolving field of data science. This MIT Press publication explores the relationship of data science to machine learning, the various ways data science impacts our lives, with specific examples given of data infrastructure issues and ethical challenges.
The authors delve into the fundamentals of data science, exploring the principles and processes that enable the extraction of valuable insights from huge data sets. They discuss the connection between data mining and machine learning and how it contributes to the broader scope of data science.
"Data Science" offers a brief history of the field, explaining how its principles and methods have evolved over time. It presents key concepts of data and outlines the different stages of a data science project.
Understanding how organizations gather, store and process data has become more accessible than ever. The authors attribute this to the rise of big data and social media, advancements in high-performance computing, and the development of powerful data analysis and modeling techniques such as deep learning.
The book addresses the challenges posed by integrating data from multiple sources, touching on the basics of machine learning, and how to link this expertise with real-world problems. It delves into ethical and legal issues, highlighting developments in data regulation and computational approaches to maintaining privacy.
Looking to the future, the authors discuss the potential impact of data science on our lives and offer principles for success in data science projects.
This book is a must-read for anyone interested in the world of computers, whether you're just starting out and looking for a concise introduction to data science, or you're an experienced professional seeking a deeper understanding of the field.
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Authors: John D. Kelleher, Brendan Tierney
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Publisher: MIT Press
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Published Date: April 13, 2018
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Page Count: 282
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees
Dive headfirst into the exciting world of data science with "Data Science from Scratch" – the ultimate guide for aspiring data scientists! Written by Steven Cooper, this comprehensive guide covers everything you need to know to jumpstart your data science career. Join the ranks of data scientists who are revolutionizing the world of business by leveraging the power of big data.
"Data Science from Scratch" provides in-depth information on what data science is and why it is crucial in today's data-driven world. Learn the prerequisites you need to embark on your data science journey, gain insights into the roles of hacking and coding, and discover the various coding languages essential for success in this field.
One of the crucial aspects of data science is understanding why Python is so important and how to effectively use linear algebra and statistics. With this book, you'll explore the diverse applications of data science and master techniques like working with data through munging and cleaning.
Don't miss out on your chance to be part of the future of data science. Grab your copy of "Data Science from Scratch" and take the first step towards an exciting, ever-growing career!
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Authors: Steven Cooper
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Publisher: Roland Bind
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Published Date: August 10, 2018
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Page Count: 156
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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First Principles with Python
"Data Science from Scratch: First Principles with Python" is your ultimate guide to understanding the core of data science, not just using the tools. This book, authored by Joel Grus, helps you decode the mystery of data science by taking you on a journey of implementing fundamental tools and algorithms from scratch.
It's perfect for those with a mathematical and programming aptitude wanting to dive deep into the discipline without being bogged down by its complexities. With this book, you'll be equipped to pull answers from the chaos of today's data glut, answering questions no one even thought to ask.
So, if you're ready to master data science from the ground up, "Data Science from Scratch" is your go-to manual.
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Authors: Joel Grus
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Publisher: O'Reilly Media
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Published Date: April 12, 2019
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Page Count: 398
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Turning data into information, better decisions, and stronger organizations
The Real Work of Data Science: Turning data into information, better decisions, and stronger organizations, by Ron S. Kenett and Thomas C. Redman, is a must-read guide for data scientists and leaders looking to harness the power of data to drive business success. In a world where data is now considered "the world's most valuable resource, " Kenett and Redman provide a comprehensive understanding of the practical aspects of data science.
This book delves into the challenges of understanding complex problems, addressing quality issues, building trust with decision-makers, organizing data science teams, and ultimately becoming a data-driven company. By exploring these key elements, readers learn how to differentiate themselves from good data scientists to great ones and create teams that truly drive the business.
The Real Work of Data Science covers a range of topics, from addressing long-standing problems in the field and staying updated with today's issues, to understanding the importance of computational Big Data and its impact on the industry. Throughout the book, you'll find valuable insights from top-tier experts in the field, such as Thomas H. Davenport, Sir David Cox, and A. Blanton Godfrey, all of whom highlight the book's pragmatic and understandable nature.
This essential resource should be a staple in any data science curriculum, business school, analytics institute, and for all business managers looking to unlock the full potential of their data and drive the organizations they serve to new heights.
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Authors: Ron S. Kenett, Thomas C. Redman
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Publisher: John Wiley & Sons
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Published Date: May 06, 2019
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Page Count: 136
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Print Type: BOOK
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Categories: Science
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Maturity Rating: NOT_MATURE
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Language: en
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Data Analysis and Prediction Algorithms with R
Dive into the captivating world of data science with 'Introduction to Data Science: Data Analysis and Prediction Algorithms with R', an essential textbook for beginners. Written by expert Rafael A. Irizarry, this comprehensive guide covers the must-know fundamentals of data analysis, predictive algorithms, and R programming.
The book is meticulously structured into six parts, each teeming with real-world case studies that serve as a mirror to a data scientist's experience. From self-reported student heights to the impact of vaccines on infectious disease rates, these case studies challenge readers to solve pressing problems using data analysis.
Learn the intricacies of R programming, data visualization, and data wrangling, among others. The author guides readers through each concept with care, ensuring they grasp every bit of the knowledge needed to become a proficient data scientist. Even if you lack prior R knowledge, the book's clear explanations make it a breeze to catch up.
However, to delve deeper, a background in probability and statistics would be advantageous. The book provides a basic introduction to these concepts, but for a more thorough understanding, it's recommended to supplement your learning with additional texts.
With 'Introduction to Data Science', you'll be ready to face the challenges of the real world, armed with the tools and knowledge of a true data expert. Are you ready to transform data into insight?
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Authors: Rafael A. Irizarry
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Publisher: CRC Press
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Published Date: November 20, 2019
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Page Count: 794
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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"A Hands-On Introduction to Data Science" by Chirag Shah is the ultimate guide for anyone eager to dive into the captivating world of data science. This comprehensive textbook is perfect for students of various disciplines looking for an accessible and engaging introduction to this ever-evolving field.
Published by esteemed Cambrdige University Press in April 2020, the book breaks down complex concepts into actionable, easy-to-understand principles. With its hands-on approach, readers will be empowered to grasp essential data science techniques and make a lasting impact in any industry.
"A Hands-On Introduction to Data Science" is a must-read for those seeking to embark on a journey filled with thought-provoking problems, innovative solutions, and an endless pursuit of knowledge. The engaging content and practical examples make this book an invaluable resource for anyone interested in enhancing their analytical skills and unlocking the power of data.
Experience the excitement and wonder of data science with "A Hands-On Introduction to Data Science" - where groundbreaking ideas and cutting-edge methodologies converge to reshape the future of business and innovation.
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Authors: Chirag Shah
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Publisher: Cambridge University Press
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Published Date: April 02, 2020
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Page Count: 459
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Print Type: BOOK
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Categories: Business & Economics
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Average Rating: 1.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Understand Why Data Science Is the Next
Embark on a journey through the world of Data Science with "Data Science Fundamentals and Practical Approaches" by Dr. Gypsy Nandi and Dr. Rupam Kumar Sharma. This book not only demystifies the intricacies of Data Science, but also equips you with the practical skills needed to excel in this dynamic field.
From fundamentals to advanced techniques, this comprehensive guide covers a wide range of topics such as data preprocessing, plotting, visualization, statistical analysis, machine learning, time-series analysis, and more. It beautifully combines theoretical explanations with Python code and corresponding output, making it easy for readers to grasp the concepts.
Each chapter features numerous examples and illustrative output to solidify your understanding of the essential facts. Plus, with chapter-end exercises that include both short-answer and long-answer questions, you'll have ample opportunity to test your knowledge. The book also provides valuable references to help you dive deeper into each topic.
Whether you're an engineering graduate, a budding data enthusiast, or simply curious about the power of data analysis, this book will serve as an invaluable resource. Unravel the mysteries of Data Science and transform your skills with "Data Science Fundamentals and Practical Approaches".
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Authors: Dr. Gypsy Nandi, Dr. Rupam Kumar Sharma
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Publisher: BPB Publications
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Published Date: June 02, 2020
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Page Count: 572
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Doing the other 80% of the work with Python, R, and command-line tools
Data cleaning might sound like a mundane task, but don't let that fool you. Cleaning data is the groundwork of successful data science, data analysis, and machine learning. This book, Cleaning Data for Effective Data Science, arms you with the tools and techniques you need to tackle it head-on.
Using Python, R, and common command-line tools, this book walks you through the process of data ingestion, data imputation, detection of unreliable data, statistical anomalies, and feature engineering. It's light-hearted, engaging, and packed with real-life examples and exercises that let you put what you've learned to the test. Each chapter ends with an exercise that lets you delve into the skills you've acquired, providing practical experience and a valuable resource for academic courses.
You'll learn to work with various data formats like JSON, CSV, SQL, NoSQL databases, PDF, and binary serialized data structures. You'll also understand the tools used to analyze data, like pandas, SciPy, scikit-learn, Tidyverse, and Bash, and how to apply rules and heuristics for assessing data quality. Identifying and handling unreliable data and outliers based on z-score and statistical properties is another key skill you'll master.
Feature engineering, dimensionality reduction, quantization, and one-hot encoding are also topics you'll explore. Additionally, the book is packed with examples of time series data analysis, de-trending, and interpolation that further reinforce your understanding.
This book is for anyone who works with data—from software developers and aspiring data scientists to teachers and students—and who seeks to improve data hygiene or refresh their skills. Basic familiarity with statistics and machine learning, knowledge of a programming language like Python or R, and some exposure to data science is helpful but not necessary.
By the end of the book, you'll have tackled data cleaning from a deep, practical, and comprehensive perspective. Whether you're a data wrangling veteran or a beginner, Cleaning Data for Effective Data Science will help you tackle data effectively and intelligently.
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Authors: David Mertz
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Publisher: Packt Publishing Ltd
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Published Date: March 31, 2021
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Page Count: 499
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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"Data Science Unmasked: A Journey into Ethical Responsibility" by Peter C. Bruce and Grant Fleming is a riveting exploration into the realm of data science and the ethical concerns that arise with its expanding influence. As the world becomes increasingly reliant on data-driven processes, the potential for bias, injustice, and discrimination also increases. This compelling book is an indispensable compass for data scientists and analytics team leaders navigating this complex landscape.
Bruce and Fleming dissect the intricacies of data science and its impact on society, illuminating the perils of "Black box" algorithms and their frequently concealed repercussions. By examining how these algorithms can be exploited to harm particular groups or exhibit unfairness, the authors provide a comprehensive roadmap for mitigating these risks and implementing data science solutions in a just manner.
Equipped with practical strategies for augmenting model transparency, identifying bias and disparity within models, and auditing projects to ensure impartiality, this 304-page gem becomes an invaluable asset in the era of data-driven decision-making. Targeted towards data science professionals, technical managers, software developers, and statisticians, "Data Science Unmasked" promises to empower those striving to make a positive difference in a rapidly transforming industry.
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Authors: Peter C. Bruce, Grant Fleming
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Publisher: John Wiley & Sons
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Published Date: April 13, 2021
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Page Count: 304
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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A Beginner's Guide for Building Datasets for Analysis
Getting Started in SQL for Data Analytics: An Introductory Guide for Crafting Datasets for Insights, written by seasoned industry expert Renee Teate, paves the way for you to thrive in the exhilarating realm of data science. This comprehensive tutorial, specifically designed by an exceptional professional, opens the door to demystifying the power of SQL, a dynamic language meant for managing databases and mining data.
Take a plunge into crafting datasets for investigation, analysis, and machine learning, as well as designing queries and grasping SQL syntax. This resource stands out among other instructional materials on SQL, as it zeroes in exclusively on the subset of essential SQL skills utilized frequently by analytical professionals and data scientists. You'll learn how to strategize on structuring your dataset and how to apply this knowledge to formulate useful analytical datasets.
SQL for Data Analytics provides an interactive, hands-on approach to understanding reports and machine learning algorithms through carefully curated strategies and techniques. The author ensures you not only comprehend the theory but also hone your skills by practicing with a supplied database and SQL code.
As a 15-year veteran in the data field, Renee Teate has held various roles such as a database developer, a data analyst, and a data scientist, resulting in a wealth of knowledge and experience that she skillfully imparts through engaging anecdotes and sequential instructions. Don't procrastinate any further! Equip yourself with SQL and data modeling expertise to kickstart your career in data science and realize the boundless potential in your data assets.
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Authors: Renee M. P. Teate
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Publisher: John Wiley & Sons
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Published Date: August 17, 2021
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Page Count: 400
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Opportunities and Challenges
"Data Science and Data Analytics: Opportunities and Challenges" by Amit Kumar Tyagi is a comprehensive exploration of the world of data science. This essential guide offers a deep dive into the concepts, tools, and algorithms that drive this evolving field, which is poised to revolutionize artificial intelligence and reshape the future.
Tyagi expertly navigates through complex data landscapes, unraveling the mysteries of turning structured and unstructured data into valuable insights. He emphasizes the importance of handling the escalating volume of unstructured data, which is expected to reach 80% by 2020. The author addresses critical concerns associated with data complexity, scalability, privacy leaks, and trust issues, providing readers with potential solutions and strategies.
Tyagi's book offers a comparative analysis of data science applications across various disciplines, including agriculture, healthcare, graph mining, education, and security. The author's real-world examples and insights present a holistic understanding of data science, making it an invaluable resource for academics, data scientists, and industry professionals seeking to optimize their firm's productivity.
This 482-page book is a must-read for anyone interested in understanding the intricacies of data science and discovering the hidden patterns that drive the future of artificial intelligence. Join Amit Kumar Tyagi as he illuminates the opportunities and challenges that lie ahead in the exciting world of data science.
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Authors: Amit Kumar Tyagi
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Publisher: CRC Press
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Published Date: September 22, 2021
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Page Count: 482
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en