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Practical Statistics for Data Scientists: 50 Essential Concepts

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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll 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
  • Statistical machine learning methods that “learn” from data
  • Unsupervised learning methods for extracting meaning from unlabeled data
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Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!
Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!

Doing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of...

Practical Data Science with R
Practical Data Science with R

Summary

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language...

Data Analysis with R
Data Analysis with R

Key Features

  • Load, manipulate and analyze data from different sources
  • Gain a deeper understanding of fundamentals of applied statistics
  • A practical guide to performing data analysis in practice

Book Description

Frequently the tool of choice for academics, R...


Python for Finance: Analyze Big Financial Data
Python for Finance: Analyze Big Financial Data

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects...

Practical Machine Learning
Practical Machine Learning

About This Book

  • Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
  • Comprehensive practical solutions taking you into the future of machine learning
  • Go a step further and integrate your machine learning projects with...
Practical Data Science Cookbook - Second Edition
Practical Data Science Cookbook - Second Edition

Over 85 recipes to help you complete real-world data science projects in R and Python

About This Book

  • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
  • Get beyond the theory and implement real-world projects in data science using...
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