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- Title: Data Science: Theories, Models, Algorithms, and Analytics
- Author(s) Sanjiv Ranjan Das
- Publisher: Self-publishing via GitHub; eBook (Apache Licensed)
- License(s): Apache License, Version 2.0
- Hardcover/Paperback: 288 pages
- eBook: HTML and PDF
- Language: English
- ISBN-10/ASIN: N/A
- ISBN-13: N/A
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The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance.
This book provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. Some highlighting contents of the book are Open Source: Modelling in R to Bayes Theorem.
It offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. You'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way.
- Learn the basics of data science and explore its possibilities and limitations
- Manage data science projects and assemble teams effectively even in the most challenging situations
- Understand management principles and approaches for data science projects to streamline the innovation process
- Data Science and Data Engineering
- Data Analysis and Data Mining, Big Data
- The R Programming Language
- Statistics, Mathematical Statistics, and SAS Programming
- Data Science: Theories, Models, Algorithms, and Analytics (Sanjiv Ranjan Das)
- PDF Format (462 pages, 14.3 MB)
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Introduction to Data Science (Rafael A. Irizarry)
Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.
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The Data Science Workshop, 2nd Edition (Anthony So, et al.)
Learn how you can build machine learning models and create your own real-world data science projects. By learning to convert raw data into game-changing insights, you'll open new career paths and opportunities.
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The Data Science Handbook: Advice and Insights
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.
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Introduction to Data Science (Jeffrey Stanton)
This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.
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Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.
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Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)
This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. Learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.
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Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.
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Exploring Data Science (Nina Zumel, et al)
This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.
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Python Data Science Handbook: Essential Tools (Jake VanderPlas)
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.
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Regression Models for Data Science in R (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.
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R for Data Science: Visualize, Model, Transform, Tidy, Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.
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The Ultimate Guide to Effective Data Cleaning
With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Experts share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.
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Elements of Data Science (Allen B. Downey)
This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.
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Statistical Inference: Algorithms, Evidence, and Data Science
A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.
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