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Python Data Analysis
Python Data Analysis

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...
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute...

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

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,...

Fashion Supply Chain Management in Asia: Concepts, Models, and Cases (Springer Series in Fashion Business)
Fashion Supply Chain Management in Asia: Concepts, Models, and Cases (Springer Series in Fashion Business)
This book serves as a comprehensive guide to understanding the theories and applications in managing the Asian fashion supply chain, presenting both quantitative and exploratory studies. Providing academicians and practitioners insights into the latest developments and models, it also offers diverse perspectives on areas like strategic...
Statistical Methods in Analytical Chemistry (Chemical Analysis: A Series of Monographs on Analytical Chemistry and Its Applications)
Statistical Methods in Analytical Chemistry (Chemical Analysis: A Series of Monographs on Analytical Chemistry and Its Applications)

This new edition of a successful, bestselling book continues to provide you with practical information on the use of statistical methods for solving real-world problems in complex industrial environments. Complete with examples from the chemical and pharmaceutical laboratory and manufacturing areas, this thoroughly updated book clearly...

Practical Data Analysis
Practical Data Analysis

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
  • ...
Data Mining for Geoinformatics: Methods and Applications
Data Mining for Geoinformatics: Methods and Applications

The rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems. This unique volume contains a collection of...

Network Security Through Data Analysis: Building Situational Awareness
Network Security Through Data Analysis: Building Situational Awareness

Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In this practical guide, security researcher Michael Collins shows you several techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what...

Practical Statistics for Data Scientists: 50 Essential Concepts
Practical Statistics for Data Scientists: 50 Essential Concepts

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...

Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data
Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.

Key Features

  • Get up and running with the Jupyter ecosystem and some example datasets
  • ...
Developing and Validating Rapid Assessment Instruments (Pocket Guides to Social Work Research Methods)
Developing and Validating Rapid Assessment Instruments (Pocket Guides to Social Work Research Methods)

Despite increases in their application and improvements in their structure, there is a paucity of reliable and valid scales compared to the complex range of problems that social workers and other health professionals confront daily. They need to be able to design rapid assessment instruments (RAIs) to fit their specific situations, and with this...

Unsupervised Learning with R
Unsupervised Learning with R

Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data

About This Book

  • Unlock and discover how to tackle clusters of raw data through practical examples in R
  • Explore your data and create your own models from scratch
  • ...
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