Principles of Data Science

by Sinan Özdemir

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. What You Will Learn - Get to know the five most important steps of data science - Use your data intelligently and learn how to handle it with care - Bridge the gap between mathematics and programming - Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results - Build and evaluate baseline machine learning models - Explore the most effective metrics to determine the success of your machine learning models - Create data visualizations that communicate actionable insights - Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking-and answering-complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. 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.

Genres: Nonfiction, Programming

490 pages, Kindle Edition

Published December 16, 2016

Reviews

[Song · Rating 4 out of 5]

Great introduction book to cover the important concepts and algorithms in Data Science and Machine Learning at the entry level, with the hands-on examples and practices. The descriptions and explanations are easy to understand and follow. Not too much "mathematic", but of course it requires the basic ideas of possibility and statistics. But in general the book is suitable and friendly for the beginners.

The only problem is the example code was written by Python2. It requires the reader/learner has a lot of Python knowledge and programming skills to fix the problems made by the differences between Python2 and Python3 before running the code.

[kurp · Rating 4 out of 5]

Excellent introduction - not perfect for sure, but explainations are simple, clear, engaging and often funny. Covers types of data and some very basics of: mathematics, probability, statistics, data visualization, machine learning and prediction methods.

[Ana Uzelac · Rating 4 out of 5]

A great book! The only downside is the code which is written in Python 2 and sometimes needs a lot of modifications in order to run. Additionally, some libraries used in the book are outdated. A must read if you want a great introduction to the data science field…

[Stein Karlsen · Rating 4 out of 5]

Good book with excellent descriptions of different algorithms