Data Science is recognized as the sexiest job of the 21st century. As organizations seek to maintain themselves through data-driven insights, demand for data science professionals is soaring relentlessly. Many reports show that the demand for data scientists is growing year over year and continues to rise sharply. Thus, to harness insights in their data to personalize experiences at scale, companies need to acquire the best data professionals. On the other side, before entering a data science career, candidates need to grasp knowledge into the world of data and analytics.
Here's a look at the top 10 free must-read ebooks to learn data science.
Whether a candidate wants to develop a mobile application, gets a certification for programming knowledge, or learns new skills, he/she must opt for the right programming language to learn. This eBook will provide ten popular and significant programming languages that are in demand today. For each, learners will find a bit about the language and the complexity involved in learning it. They will also learn how it is used, and which language they should start with.
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Python is a first-class tool for many researchers mainly credited to its libraries for storing, manipulating, and gaining insight from data. Authored by Jake VanderPlas, this handbook will provide learner to learn how to use IPython and Jupyter; NumPy, includes the ndarray for efficient storage and manipulation of dense data arrays in Python; Pandas which features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python; Matplotlib; and Scikit-Learn.
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This eBook will offer to learn how to program with Python 3 from beginning to end. Python 101 starts with the fundamentals of Python and then builds onto what learners have learned from there. Authored by Michael Driscoll, the book will be split into five parts. In part one, learners will learn all the basics of Python. Part two will be a curated tour of the Python Standard Library. Part three is all intermediate level material, covering lamda, decorators, properties, debugging, testing, and profiling. Part four will provide how to install 3rd party libraries from the Python Package Index and other locations. The last section of the book will cover how to share your code with your friends and the world.
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Machine Learning Yearning is a free ebook from Andrew Ng. It teaches learners how to structure ML projects. This ebook is also focused on teaching ML algorithms and how to make them work. After reading this book, learners will be able to prioritize the most promising directions for an AI project; diagnose errors in an ML system; build ML in complex settings, such as mismatched training/test sets; set up an ML project to compare to and/or surpass human-level performance; and know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
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As Tableau makes analytics easy and accessible for everyone, it is currently seen to be the market leader when it comes to Self-Service BI with a high degree of execution. This free ebook will be beneficial to those using Tableau, but want to get more out of that powerful data visualization tool. It will make a learner a Tableau power user. With this book, readers will unlock the secrets to powerful features they didn't know existed, for in-depth data analytics and insight.
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This ebook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It is not a technical guide to data science. Rather, it will provide data scientists with common career questions like What separates the work of a data scientist from a statistician, and a software engineer? How can they work together? What should you look for when evaluating data science roles at companies? What does it take to build an effective data science team? What mindsets, techniques and skills distinguish a great data scientist from the merely good? What lies in the future for data science? and more.
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Since the demand for data scientists continues to grow, the best data scientist will be the one who is the most well-informed. This ebook will bring data science enthusiasts up to speed on the relevant aspects of this red-hot field, and give them a competitive advantage when they apply for that coveted position. The ebook will provide the basics of big data and data science; history and developments of data science; prerequisites for becoming a data scientist; preferred educational qualifications; miscellaneous Non-technical skills; study plan and useful resources.
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This hands-on guide demonstrates how the flexibility of the command line can help a learner to become a more efficient and productive data scientist. In this book, users will learn how to coalesce small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model their data. This ebook obtains data from websites, APIs, databases, and spreadsheets that help explore data, compute descriptive statistics, create visualizations, and manage data science workflow.
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This practical guide aimed at explaining how to apply various statistical methods to data science, gives learners how to avoid their misuse, and advises them on what is imperative and what is not. This book will provide learners to learn: Why exploratory data analysis is a key preliminary step in data science? How random sampling can reduce bias and yield a higher quality dataset, even with big data? How the principles of experimental design yield definitive answers to questions? How to use regression to estimate outcomes and detect anomalies? Key classification techniques for predicting which categories a record belongs to and much more.
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This ebook is a key part of the data science process as it allows learners to sharpen their questions and refine their modeling strategies. It significantly teaches how to use R to effectively visualize and explore complex datasets. This book is based on the industry-leading Johns Hopkins Data Science Specialization.
Get your copy here.
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