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Principles of Data Wrangling

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something;

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Practical Data Wrangling

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This;

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Data Wrangling with JavaScript

), JavaScript is a terrific all-round environment for all data wrangling needs! Data Wrangling with JavaScript teaches readers core data;

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The Data Wrangling Workshop

fun and interactive way Key Features Explore data wrangling with the help of real-world examples and business use cases Study various;

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Data Wrangling with R

leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly;

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Data Wrangling with Python

wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data;

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Data Analytics

reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling;

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Data Wrangling with Python

datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process;

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Principles of Managerial Statistics and Data Science

Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business;

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The Big RBook

wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that;

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Next Generation Big Data

wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big;

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The Wrangling Lovers

Title: The Wrangling Lovers: or, the Invisible Mistress. A comedy in five acts, and in prose. By E. Ravenscroft].Publisher: British;

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Hands-On Data Analysis with Pandas

library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by;

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A Python Data Analyst s Toolkit

terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like;

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97 Things Every Data Engineer Should Know

, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers;

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Data Analysis for Business, Economics, and Policy

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to;

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Data Analysis for Business, Economics, and Policy

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to;

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Hands-On Data Analysis with Pandas

pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data;

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A Python Data Analyst's Toolkit

terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like;

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Mastering the SAS Ds2 Procedure

Enhance your SAS data wrangling skills with high precision and parallel data manipulation using the new DS2 programming language. This book;

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The Beginner's Guide to Data Science

This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics;

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Practical Machine Learning in R

uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool;

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SciPy Recipes

, statistics, data wrangling, data visualization, and more * A must-have book if you're looking to solve your data-related problems using SciPy, on;

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Principles of Big Data

to reach at the decision-making conclusions. The book Principles of Big Data takes the readers through the various aspects of big data and;

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Fast Data Processing with Spark 2 - Third Edition

Learn how to use Spark to process big data at speed and scale for sharper analytics. Put the principles into practice for faster, slicker;

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Developing High Quality Data Models

Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science;

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