Principles of Data Science Paperback - 13 Dec 2016
by Sinan Ozdemir
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?
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.
Data Science From Scratch: First Principles with Python, Second Edition Paperback - 5 May 2019
by Joel Grus
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, nave Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, produce, and databases
An Introduction to Data Science First Edition
by Jeffrey S. Saltz
An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.
Data Science from Scratch: The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Tree Kindle Edition
by Steven Cooper
- If you are looking to start a new career that is in high demand, then you need to continue reading.
- Data scientists are changing the way big data is used in different institutions.
- Big data is everywhere, but without the right person to interpret it, it means nothing.
- So where do business find these people to help change their business?
The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.
As businesses and the internet change, so will data science. This means it's important to be flexible.
When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in?