Exploratory Data Analysis a Complete Guide - 2019 Edition
Exploratory Data Analysis a Complete Guide - 2019 Edition

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2018-12-20

Total Pages: 294

ISBN-13: 9780655515661

DOWNLOAD EBOOK

How will you minimise dirty data? Do you have explanations for patterns that you see? How can you use your data to convince your audience? Is it possible to speed up the algorithm significantly in all situations? When is it considered completed? This breakthrough exploratory data analysis self-assessment will make you the reliable exploratory data analysis domain standout by revealing just what you need to know to be fluent and ready for any exploratory data analysis challenge. How do I reduce the effort in the exploratory data analysis work to be done to get problems solved? How can I ensure that plans of action include every exploratory data analysis task and that every exploratory data analysis outcome is in place? How will I save time investigating strategic and tactical options and ensuring exploratory data analysis costs are low? How can I deliver tailored exploratory data analysis advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all exploratory data analysis essentials are covered, from every angle: the exploratory data analysis self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that exploratory data analysis outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced exploratory data analysis practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in exploratory data analysis are maximized with professional results. Your purchase includes access details to the exploratory data analysis self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific exploratory data analysis Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Exploratory Data Analysis with Python Cookbook
Exploratory Data Analysis with Python Cookbook

Author: Ayodele Oluleye

Publisher: Packt Publishing Ltd

Published: 2023-06-30

Total Pages: 383

ISBN-13: 1803246138

DOWNLOAD EBOOK

Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.

Making Sense of Data I
Making Sense of Data I

Author: Glenn J. Myatt

Publisher: John Wiley & Sons

Published: 2014-07-02

Total Pages: 262

ISBN-13: 1118422104

DOWNLOAD EBOOK

Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Exploratory Data Analysis
Exploratory Data Analysis

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2018-01-16

Total Pages: 126

ISBN-13: 9780655152057

DOWNLOAD EBOOK

What are the Key enablers to make this Exploratory data analysis move? Where do ideas that reach policy makers and planners as proposals for Exploratory data analysis strengthening and reform actually originate? What threat is Exploratory data analysis addressing? Is the impact that Exploratory data analysis has shown? Does the Exploratory data analysis performance meet the customer's requirements? This exclusive Exploratory data analysis self-assessment will make you the trusted Exploratory data analysis domain veteran by revealing just what you need to know to be fluent and ready for any Exploratory data analysis challenge. How do I reduce the effort in the Exploratory data analysis work to be done to get problems solved? How can I ensure that plans of action include every Exploratory data analysis task and that every Exploratory data analysis outcome is in place? How will I save time investigating strategic and tactical options and ensuring Exploratory data analysis opportunity costs are low? How can I deliver tailored Exploratory data analysis advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Exploratory data analysis essentials are covered, from every angle: the Exploratory data analysis self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Exploratory data analysis outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Exploratory data analysis practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Exploratory data analysis are maximized with professional results. Your purchase includes access details to the Exploratory data analysis self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.

Exploratory Data Analysis
Exploratory Data Analysis

Author: Frederick Hartwig

Publisher: SAGE

Published: 1979

Total Pages: 88

ISBN-13: 9780803913707

DOWNLOAD EBOOK

An introduction to the underlying principles, central concepts, and basic techniques for conducting and understanding exploratory data analysis - with numerous social science examples.

Hands-On Exploratory Data Analysis with R
Hands-On Exploratory Data Analysis with R

Author: Radhika Datar

Publisher: Packt Publishing Ltd

Published: 2019-05-31

Total Pages: 254

ISBN-13: 1789802083

DOWNLOAD EBOOK

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Data Analysis A Complete Guide - 2019 Edition
Data Analysis A Complete Guide - 2019 Edition

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2019-06-13

Total Pages: 310

ISBN-13: 9780655544159

DOWNLOAD EBOOK

What are some real time data analysis frameworks? How are the current generation of managers and auditors placed to interpret the result of Big Data analysis? Does a test systematically cover all parts of the construct? What resources are needed for data analysis, management, recordkeeping, and organization communication? What data analysis software will you use? This premium Data analysis self-assessment will make you the credible Data analysis domain auditor by revealing just what you need to know to be fluent and ready for any Data analysis challenge. How do I reduce the effort in the Data analysis work to be done to get problems solved? How can I ensure that plans of action include every Data analysis task and that every Data analysis outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data analysis costs are low? How can I deliver tailored Data analysis advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data analysis essentials are covered, from every angle: the Data analysis self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Data analysis outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data analysis practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data analysis are maximized with professional results. Your purchase includes access details to the Data analysis self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data analysis Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Think Stats
Think Stats

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

Published: 2014-10-16

Total Pages: 284

ISBN-13: 1491907363

DOWNLOAD EBOOK

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 thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

Hands-On Exploratory Data Analysis with Python
Hands-On Exploratory Data Analysis with Python

Author: Suresh Kumar Mukhiya

Publisher: Packt Publishing Ltd

Published: 2020-03-27

Total Pages: 342

ISBN-13: 178953562X

DOWNLOAD EBOOK

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.