In a world driven by data, data science has become the sixth sense of humanity. Besides the fact that it has become one of the highest-paid and infamous fields in the contemporary market, data science will continue to grow beyond all the challenges in the future. Analyzing recent trends, we can predict that there will be numerous job opportunities that will fetch professionals a handsome salary. Amid this, it is extremely crucial for them to stay updated and upskill their talent to stay ahead in the competition. Educating yourself through data science books is one of the most holistic views to get a hold onto your data-skills. Through following data science books you can learn not only about problem-solving but get a bigger picture of using mathematics, probability, statistics, programming, machine learning and much more in your data science projects & initiatives.
Here are the top 10 data science books you must read to boost your career.
Practical Statistics for Data Scientists
Author: Peter Bruce, Andrew Bruce, Peter Gedeck
Description: 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 practical guide–now including examples in Python as well as R–explains how to apply various 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 scientists use statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With the updated edition, you’ll dive into: Exploratory data analysis; Data and sampling distributions; Statistical experiments and significance testing; Regression and prediction; Classification; Statistical machine learning; and Unsupervised learning.
Python Data Science Handbook
Author: Jake VanderPlas
Description: 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 all—IPython, 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 that provides computational environments for data scientists using Python; NumPy which includes the ndarray for efficient storage and manipulation of dense data arrays in Python; Pandas that features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python; Matplotlib which includes capabilities for a flexible range of data visualizations in Python; and Scikit-Learn for efficient and clean Python implementations of the most important and established machine learning algorithms.
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Author: Seth Stephens-Davidowitz
Description: Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women? Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab.
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data
Author: John Wiley & Sons
Description: Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.
This book will help you become a contributor on a data science team, deploy a structured life-cycle approach to data analytics problems, apply appropriate analytic techniques and tools to analyzing big data, learn how to tell a compelling story with data to drive business action and prepare for EMC Proven Professional Data Science Certification.
Introduction to Statistical Learning
Author: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Description: Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, re-sampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open-source statistical software platform.
Naked Statistics: Stripping The Dread From Data
Author: Charles Wheelan
Description: The best-selling author of Naked Economics defies the odds with a book about statistics that you’ll welcome and enjoy. As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer any questions. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
Big Data – A Revolution that Will Transform How We Live, Work, and Think
Author: Viktor Mayer-Schönberger, and Kenneth Cukier
Description: A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena–from the price of airline tickets to the text of millions of books–into searchable form and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.
In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Author: Cam Davidson-Pilon
Description: Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
Data Science from Scratch: First Principles of Python
Author: Joel Grus
Description: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Learners can get a crash course in Python, learn the basics of linear algebra, statistics, and probability—and understand how and when they’re used in data science, collect, explore, clean, munge, and manipulate data, dive into the fundamentals of machine learning, implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering, and explore recommender systems, natural language processing, network analysis, MapReduce, and databases.
Business Analytics – A Data-Driven Decision-Making Approach for Business
Author: Amar Sahay
Description: This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, the importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses the current state, job outlook, and certifications in analytics.
