Analyzing Tabular Data
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Analyzing Tabular Data
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Author : Nigel Gilbert
language : en
Publisher: Routledge
Release Date : 2022-02-10
Analyzing Tabular Data written by Nigel Gilbert and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-10 with Social Science categories.
First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. Empirical social research almost invariably requires the presentation and analysis of tables, and this book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data. The book begins with an introduction to the process of data analysis and the basic structure of cross-tabulations. At the core of the methods described in the text is the loglinear model. This and the logistic model, are explained and their application to causal modelling, to event history analysis, and to social mobility research are described in detail. Each chapter concludes with sample programs to show how analysis on typical datasets can be carried out using either the popular computer packages, SPSS, or the statistical programme, GLIM. The book is packed with examples which apply the methods to social science research. Sociologists, geographers, psychologists, economists, market researchers and those involved in survey research in the fields of planning, evaluation and policy will find the book to be a clear and thorough exposition of methods for the analysis of tabular data.
Analyzing Tabular Data
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Author : G. Nigel Gilbert
language : en
Publisher:
Release Date : 1993
Analyzing Tabular Data written by G. Nigel Gilbert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Social sciences categories.
Analyzing Tabular Data
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Author : Nigel Gilbert
language : en
Publisher: Taylor & Francis
Release Date : 2022-02-10
Analyzing Tabular Data written by Nigel Gilbert and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-10 with Social Science categories.
First published in 1993, Analyzing Tabular Data is an accessible text introducing a powerful range of analytical methods. Empirical social research almost invariably requires the presentation and analysis of tables, and this book is for those who have little prior knowledge of quantitative analysis or statistics, but who have a practical need to extract the most from their data. The book begins with an introduction to the process of data analysis and the basic structure of cross-tabulations. At the core of the methods described in the text is the loglinear model. This and the logistic model, are explained and their application to causal modelling, to event history analysis, and to social mobility research are described in detail. Each chapter concludes with sample programs to show how analysis on typical datasets can be carried out using either the popular computer packages, SPSS, or the statistical programme, GLIM. The book is packed with examples which apply the methods to social science research. Sociologists, geographers, psychologists, economists, market researchers and those involved in survey research in the fields of planning, evaluation and policy will find the book to be a clear and thorough exposition of methods for the analysis of tabular data.
New Markov Chain Monte Carlo Algorithms For Analyzing Tabular Data
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Author : Philip J. O'Neil
language : en
Publisher:
Release Date : 2000
New Markov Chain Monte Carlo Algorithms For Analyzing Tabular Data written by Philip J. O'Neil and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Algorithms categories.
Exploratory Data Analysis With Python Cookbook
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Author : Ayodele Oluleye
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30
Exploratory Data Analysis With Python Cookbook written by Ayodele Oluleye and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
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.
Bayesian Database
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Author : Jay Baxter
language : en
Publisher:
Release Date : 2014
Bayesian Database written by Jay Baxter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
BayesDB, a Bayesian database table, lets users query the probable implications of their tabular data as easily as an SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with little statistics knowledge can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries. BayesDB is suitable for analyzing complex, heterogeneous data tables with no preprocessing or parameter adjustment required. This generality rests on the model independence provided by BQL, analogous to the physical data independence provided by the relational model. SQL enables data filtering and aggregation tasks to be described independently of the physical layout of data in memory and on disk. Non-experts rely on generic indexing strategies for good-enough performance, while experts customize schemes and indices for performance-sensitive applications. Analogously, BQL enables analysis tasks to be described independently of the models used to solve them. Non-statisticians can rely on a general-purpose modeling method called CrossCat to build models that are good enough for a broad class of applications, while experts can customize the schemes and models when needed. This thesis defines BQL, describes an implementation of BayesDB, quantitatively characterizes its scalability and performance, and illustrates its efficacy on real-world data analysis problems in the areas of healthcare economics, statistical survey data analysis, web analytics, and predictive policing.
Excel Data Analysis
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Author : Jinjer L. Simon
language : en
Publisher: Visual
Release Date : 2003
Excel Data Analysis written by Jinjer L. Simon and has been published by Visual this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
* Essential for those who know basic Excel and want to explore the full potential of the program * Teaches how to manipulate data to suit specific needs and achieve more by doing less work * Self-contained two-page lessons, featuring high-resolution screen shots and minimal text, show how to create custom functions, retrieve data from databases, use value chains, and slice and pivot information from the Web with Excel's PivotTable utility * Covers data analyzing techniques for statistical functions, financial functions, data sharing, PivotTables and PivotCharts, Solver, and BackSolver
Data Science And Big Data Analytics
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Author : EMC Education Services
language : en
Publisher: John Wiley & Sons
Release Date : 2015-01-05
Data Science And Big Data Analytics written by EMC Education Services and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-05 with Computers categories.
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Analyzing Data With Power Bi And Power Pivot For Excel
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Author : Alberto Ferrari
language : en
Publisher: Microsoft Press
Release Date : 2017-04-28
Analyzing Data With Power Bi And Power Pivot For Excel written by Alberto Ferrari and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-28 with Business & Economics categories.
Renowned DAX experts Alberto Ferrari and Marco Russo teach you how to design data models for maximum efficiency and effectiveness. How can you use Excel and Power BI to gain real insights into your information? As you examine your data, how do you write a formula that provides the numbers you need? The answers to both of these questions lie with the data model. This book introduces the basic techniques for shaping data models in Excel and Power BI. It’s meant for readers who are new to data modeling as well as for experienced data modelers looking for tips from the experts. If you want to use Power BI or Excel to analyze data, the many real-world examples in this book will help you look at your reports in a different way–like experienced data modelers do. As you’ll soon see, with the right data model, the correct answer is always a simple one! By reading this book, you will: • Gain an understanding of the basics of data modeling, including tables, relationships, and keys • Familiarize yourself with star schemas, snowflakes, and common modeling techniques • Learn the importance of granularity • Discover how to use multiple fact tables, like sales and purchases, in a complex data model • Manage calendar-related calculations by using date tables • Track historical attributes, like previous addresses of customers or manager assignments • Use snapshots to compute quantity on hand • Work with multiple currencies in the most efficient way • Analyze events that have durations, including overlapping durations • Learn what data model you need to answer your specific business questions About This Book • For Excel and Power BI users who want to exploit the full power of their favorite tools • For BI professionals seeking new ideas for modeling data
Synthesizing Tabular Data Using Conditional Gan
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Author : Lei Xu (S.M.)
language : en
Publisher:
Release Date : 2020
Synthesizing Tabular Data Using Conditional Gan written by Lei Xu (S.M.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of discrete and continuous columns, building such a model is a non-trivial task. Continuous columns may have multiple modes, while discrete columns are sometimes imbalanced, making modeling difficult. To address this problem, I took two major steps. (1) I designed SDGym, a thorough benchmark, to compare existing models, identify different properties of tabular data and analyze how these properties challenge different models. Our experimental results show that statistical models, such as Bayesian networks, that are constrained to a fixed family of available distributions cannot model tabular data effectively, especially when both continuous and discrete columns are included. Recently proposed deep generative models are capable of modeling more sophisticated distributions, but cannot outperform Bayesian network models in practice, because the network structure and learning procedure are not optimized for tabular data which may contain non-Gaussian continuous columns and imbalanced discrete columns. (2) To address these problems, I designed CTGAN, which uses a conditional generative adversarial network to address the challenges in modeling tabular data. Because CTGAN uses reversible data transformations and is trained by re-sampling the data, it can address common challenges in synthetic data generation. I evaluated CTGAN on the benchmark and showed that it consistently and significantly outperforms existing statistical and deep learning models.