Learn IT - Books tags exploratory

Books tags: exploratory

Python Data Analysis

Packt Publishing, 2017

Key Features

Find, manipulate, and analyze your data using the Python 3.5 libraries

Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code

An easy-to-follow guide with realistic examples that are frequently used in real-world data...

Practical Statistics for Data Scientists: 50 Essential Concepts

O'Reilly, 2017

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...

Practical Data Science Cookbook - Second Edition

Packt Publishing, 2017

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...

Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences (Use R!)

Springer, 2011

This accessible primer on multivariate statistics in the life sciences gives a full description of the general data analysis paradigm, from exploratory analysis to modeling to validation; covers other aspects of chemometrics; and supplies the relevant R code.

...

Data Mining Using SAS Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Springer, 2002

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods...

An Introduction to Bootstrap Methods with Applications to R

John Wiley & Sons, 2011

A comprehensive introduction to bootstrap methods in the R programming environment

Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the...

Practical Data Analysis

Packt Publishing, 2013

Transform, model, and visualize your data through hands-on projects, developed in open source tools

Overview

Explore how to analyze your data in various innovative ways and turn them into insight

Learn to use the D3.js visualization tool for exploratory data analysis

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

O'Reilly, 2017

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas,...

Elementary Mechanics Using Python: A Modern Course Combining Analytical and Numerical Techniques (Undergraduate Lecture Notes in Physics)

Springer, 2015

This book – specifically developed as a novel textbook on elementary classical mechanics – shows how analytical and numerical methods can be seamlessly integrated to solve physics problems. This approach allows students to solve more advanced and applied problems at an earlier stage and equips them to deal with real-world examples...

Think Stats

O'Reilly, 2014

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this...

Visual Six Sigma: Making Data Analysis Lean (Wiley and SAS Business Series)

John Wiley & Sons, 2016

Streamline data analysis with an intuitive, visual Six Sigma strategy

Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual...

Exploratory Multivariate Analysis by Example Using R (Chapman & Hall/CRC Computer Science & Data Analysis)

CRC Press, 2010

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis...

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