Best Data Processing Books

Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more.

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Author: by Aurélien Géron
O'Reilly Media
English
856 pages

View on Amazon

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworksScikit-Learn and Tensor Flowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.

With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the Tensor Flow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets.


2. Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People

Author: by Aditya Bhargava
Manning Publications
English
256 pages

View on Amazon

SummaryGrokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You’ll start with sorting and searching and, as you build up your skills in thinking algorithmically, you’ll tackle more complex concerns such as data compression and artificial intelligence.

Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn’t have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you’ll find in Grokking Algorithms on Manning Publications’ YouTube channel.

Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.Com (www.Manning. Com/livevideo/algorithms-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the TechnologyAn algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you’ll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you.


3. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: by Wes McKinney
O'Reilly Media
English
550 pages

View on Amazon

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3. 6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.

You’ll learn the latest versions of pandas, NumPy, IPython, and Jupiter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing.

Data files and related material are available on GitHub. Use the IPython shell and Jupiter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas group by facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples.


4. Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability (3rd Edition) (Voices That Matter)

Author: by Steve Krug
0321965515
New Riders
English

View on Amazon

Since Don’t Make Me Think was first published in 2000, hundreds of thousands of Web designers and developers have relied on usability guru Steve Krug’s guide to help them understand the principles of intuitive navigation and information design. Witty, commonsensical, and eminently practical, it’s one of the best-loved and most recommended books on the subject.

Now Steve returns with fresh perspective to reexamine the principles that made Don’t Make Me Think a classicwith updated examples and a new chapter on mobile usability. And it’s still short, profusely illustratedand best of allfun to read. If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world.

If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites. After reading it over a couple of hours and putting its ideas to work for the past five years, I can say it has done more to improve my abilities as a Web designer than any other book.


5. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Author: by Hadley Wickham
O'Reilly Media
English
520 pages

View on Amazon

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.

Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results.

You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.

You’ll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true “signals” in your datasetCommunicatelearn R Markdown for integrating prose, code, and results.


6. Code: The Hidden Language of Computer Hardware and Software

Author: by Charles Petzold
0735611319
Microsoft Press
English

View on Amazon

What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries.

Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines. It’s a cleverly illustrated and eminently comprehensible storyand along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet.

No matter what your level of technical savvy, CODE will charm youand perhaps even awaken the technophile within.


7. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Author: by Cathy O'Neil
Crown
English
288 pages

View on Amazon

NEW YORK TIMES BESTSELLER A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabricwith a new afterword A manual for the twenty-first-century citizen … Relevant and urgent. Financial Times NATIONAL BOOK AWARD LONGLIST NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review The Boston Globe Wired Fortune Kirkus Reviews The Guardian Nature On Point We live in the age of the algorithm.

Increasingly, the decisions that affect our liveswhere we go to school, whether we can get a job or a loan, how much we pay for health insuranceare being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.

But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discriminationpropping up the lucky, punishing the downtrodden, and undermining our democracy in the process.


8. The Algorithm Design Manual (Texts in Computer Science)

Author: by Steven S. Skiena
Springer
English
810 pages

View on Amazon

“My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace graph problems are – they should be part of every working programmer’s toolkit.

The book also covers basic data structures and sorting algorithms, which is a nice bonus. Every 1 pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types.” (Steve Yegge, Get that Job at Google)”Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems.

Every programmer should read this book, and anyone working in the field should keep it close to hand. This is the best investment a programmer or aspiring programmer can make.” (Harold Thimbleby, Times Higher Education)”It is wonderful to open to a random spot and discover an interesting algorithm.


9. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Author: by Peter Bruce
O'Reilly Media
English
368 pages

View on Amazon

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data.

10. Excel 2021: The Key To Becoming an Excel Master in Less Than 30 Minutes a Day | A Complete Step-by-Step Guide from Beginner to Expert Thanks to Unique Smart Method + Practical Examples

Author: by Eugene Gates
B095PYXV3T
English

195 pages

View on Amazon

Do you wish to perfect your Microsoft Excel knowledge to unlock its full range of functions, especially those that are most useful for individual users and businesses? And are you looking for a guide that will take away the guesswork from the whole process and even show you cool shortcuts that will save you your valuable time while making sure that you unlock functions you probably didn’t even think existed yet are very helpful?

If you’ve answered YES,Let This Book Help You Understand Microsoft Excel Inside Out So You Can Make The Most Of What It Was Meant To Do! Microsoft Excel is powerful. That’s why it is a go-to tool for individuals and organizations around the world because it supports functions that are useful for individual users and those that can be used for enterprise level processing.

And if you see what anyone with a strong background in Microsoft Excel can do with the program, you will want to learn about it to streamline so many things in your life. But where do you start? What functions are most important for beginners?

11. Super Founders: What Data Reveals About Billion-Dollar Startups

Author: by Ali Tamaseb
English
320 pages
1541768426

View on Amazon

Super Founders uses a data-driven approach to understand what really differentiates billion-dollar startups from the restrevealing that nearly everything we thought was true about them is false! Ali Tamaseb has spent thousands of hours manually amassing what may be the largest dataset ever collected on startups, comparing billion-dollar startups with those that failed to become one30,000 data points on nearly every factor: number of competitors, market size, the founder’s age, his or her university’s ranking, quality of investors, fundraising time, and many, many more.

And what he found looked far different than expected. Just to mention a few: Most unicorn founders had no industry experience;There’s no disadvantage to being a solo founder or to being a non-technical CEO;Less than 15% went through any kind of accelerator program;Over half had strong competitors when starting-being first to market with an idea does not actually matter.

You will also hear the stories of the early days of billion-dollar startups first-hand. The book includes exclusive interviews with the founders/investors of Zoom, Instacart, PayPal, Nest, Github, Flatiron Health, Kite Pharma, Facebook, Stripe, Airbnb, YouTube, LinkedIn, Lyft, DoorDash, Coinbase, and Square, venture capital investors like Elad Gil, Peter Thiel, Alfred Lin from Sequoia Capital and Keith Rabois of Founders Fund, as well as previously untold stories about the early days of ByteDance (TikTok), WhatsApp, Dropbox, Discord, DiDi, Flipkart, Instagram, Careem, Peloton, and SpaceX.

12. Data Science from Scratch: First Principles with Python

Author: by Joel Grus
O'Reilly Media
English
406 pages

View on Amazon

To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut also understand the ideas and principles underlying them. Updated for Python 3. 6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist.

Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in PythonLearn the basics of linear algebra, statistics, and probabilityand how and when they’re used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest neighbors, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases..

13. Python for Excel: A Modern Environment for Automation and Data Analysis

Author: by Felix Zumstein
O'Reilly Media
English
338 pages

View on Amazon

While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it’s the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein-creator of xlwings, a popular open source package for automating Excel with Python-shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started.

Use Python without extensive programming knowledgeGet started with modern tools, including Jupyter notebooks and Visual Studio codeUse pandas to acquire, clean, and analyze data and replace typical Excel calculationsAutomate tedious tasks like consolidation of Excel workbooks and production of Excel reportsUse xlwings to build interactive Excel tools that use Python as a calculation engineConnect Excel to databases and CSV files and fetch data from the internet using Python codeUse Python as a single tool to replace VBA, Power Query, and Power Pivot

14. Python Data Science Handbook: Essential Tools for Working with Data

Author: by Jake VanderPlas
O'Reilly Media
English
548 pages

View on Amazon

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.

Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use:IPython and Jupyter: provide computational environments for data scientists using PythonNumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in PythonPandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in PythonMatplotlib: includes capabilities for a flexible range of data visualizations in PythonScikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

15. Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Author: by Stefan Jansen
English
820 pages
1839217715

View on Amazon

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML).

This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting.

16. Learning Web Design: A Beginner's Guide to HTML, CSS, JavaScript, and Web Graphics

Author: by Jennifer Robbins
O'Reilly Media
English
808 pages

View on Amazon

Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. You’ll begin at square one, learning how the web and web pages work, and then steadily build from there.

By the end of the book, you’ll have the skills to create a simple site with multicolumn pages that adapt for mobile devices. Each chapter provides exercises to help you learn various techniques and short quizzes to make sure you understand key concepts.

This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels. It is simple and clear enough for beginners, yet thorough enough to be a useful reference for experienced developers keeping their skills up to date.

Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn how JavaScript works and why the language is so important in web designCreate and optimize web images so they’ll download as quickly as possibleNew!