by Sinan Ozdemir
Learn the techniques and math you need to start making sense of your data.
About This Book
-Enhance your knowledge of coding with data science theory for practical insight into data science and analysis -More than just a math class, learn how to perform real-world data science tasks with R and Python -Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo-code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you.
Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
by Joel Grus
All Indian reprints of O'Reilly are printed in grayscale.
to really learn data science, you should not only master the tools-data Science libraries, frameworks, modules, and toolkits-but 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.
- Learn the basics of linear algebra, statistics, and Probability 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, naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, produce, and databases
by Graham J. Williams
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open-source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data.
Building on over thirty years' experience in teaching and practicing data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practice of data science while building their data skills. Proven frameworks are provided as reusable templates. Real-world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets.
The book begins by introducing data science. It then reviews R's capabilities for analyzing data by writing computer programs. These programs are developed and explained step by step. From analyzing and visualizing data, the framework moves on to tried and tested machine learning techniques for predictive modeling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
by Field Cady
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just a rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist.
The book also features: Extensive sample code and tutorials using Python along with its technical libraries Core technologies of Big Data, including their strengths and limitations and how they can be used to solve real-world problems Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity A wide variety of case studies from industry Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools.
Source: HOB