Books: 155

Data Analysis

CoverTitleYear
Many professional, high-quality surveys collect data on people's behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, more » in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to: * Create a robust research question and design that suits secondary analysis * Locate, access and explore data online * Understand data documentation * Check and 'clean' secondary data * Manage and analyse your data to produce meaningful results * Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you'll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book's companion website give you an opportunity to practice, check your understanding and work hands on with real data as you're learning. « less
2017
A Practical Guide to Self-Service Data Analytics with Excel 2016 and Power BI Desktop
Analyze your company’s data quickly and easily using Microsoft’s latest tools. Build scalable and robust data models to work from. Learn to clean and combine different data sources effectively. Create compelling visualizations and share them with your colleagues. Author Dan Clark takes you through more » each topic using step-by-step activities and plenty of screen shots to help familiarize you with the tools. This second edition includes new material on advanced uses of Power Query, along with the latest user guidance on the evolving Power BI platform.Beginning Power BIis your hands-on guide to quick, reliable, and valuable data insight. What You Will Learn * Simplify data discovery, association, and cleansing * Create solid analytical data models * Create robust interactive data presentations * Combine analytical and geographic data in map-based visualizations * Publish and share dashboards and reports Who This Book Is For Business analysts, database administrators, developers, and other professionals looking to better understand and communicate with data. « less
2017
Methodologies and Applications
This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential more » new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers. « less
2017
Beginners Guide to Learn Data Analytics, Predictive Analytics and Data Science with Linux Operating System
THIS IS A 2 BOOK BUNDLE RELATED TO DATA ANALYTICS AND BEGINNING YOUR QUEST TO UNDERSTAND THE LINUX COMMAND LINE OPERATING SYSTEM TWO MANUSCRIPTS FOR THE PRICE OF ONE! WHATS INCLUDED IN THIS 2 BOOK BUNDLE MANUSCRIPT: Data Analytics: Practical Data Analysis and Statistical Guide to Transform more » and Evolve Any Business, Leveraging the power of Data Analytics, Data Science, and Predictive Analytics for Beginners Hacking University: Senior EditionOptimal beginners guide to precisely learn and conquer the Linux operating system. A complete step-by-step guide in how the Linux command line works IN DATA ANALYTICS, YOU WILL LEARN: * Why your business should be using data analytics * Issues with using big data * Effective data management * Examples of data management in the real-world * The different kinds of data analytics and their definitions * How data management, data mining, data integration and data warehousing work together * A step-by-step guide for conducting data analysis for your business * An organizational guide to data analytics * Tools for data visualization IN HACKING UNIVERSITY SENIOR EDITION, YOU WILL LEARN: * What is Linux * History and Benefits of Linux * Ubuntu Basics and Installing Linux * Managing Software and Hardware * The Command Line Terminal * Useful Applications * Security Protocols * Scripting, I/O Redirection, Managing Directories * And a bunch more! Get your copy today! Scroll up and hit the buy button to download now! « less
2017
There are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitioner’s viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors more » as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies. « less
2017
KEY FEATURES * Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide * Gain in-depth understanding of the text mining process with lucid implementation in the R language * Example-rich guide that lets you gain high-quality information from text data BOOK more » DESCRIPTION Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. WHAT YOU WILL LEARN * Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process * Access and manipulate data from different sources such as JSON and HTTP * Process text using regular expressions * Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis * Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R * Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) * Build a baseline sentence completing application * Perform entity extraction and named entity recognition using R ABOUT THE AUTHOR Ashish Kumar is an IIM alumnus and an engineer at heart. He has extensive experience in data science, machine learning, and natural language processing having worked at organizations, such as McAfee-Intel, an ambitious data science startup Volt consulting), and presently associated to the software and research lab of a leading MNC. Apart from work, Ashish also participates in data science competitions at Kaggle in his spare time. Avinash Paul is a programming language enthusiast, loves exploring open sources technologies and programmer by choice. He has over nine years of programming experience. He has worked in Sabre Holdings , McAfee , Mindtree and has experience in data-driven product development, He was intrigued by data science and data mining while developing niche product in education space for a ambitious data science start-up. He believes data science can solve lot of societal challenges. In his spare time he loves to read technical books and teach underprivileged children back home. TABLE OF CONTENTS 1. Statistical Linguistics with R 2. Processing Text 3. Categorizing and Tagging Text 4. Dimensionality Reduction 5. Text Summarization and Clustering 6. Text Classification 7. Entity Recognition « less
2017
A Step-by-Step Guide
Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro more » Tableau demonstrates the power of visual analytics and teaches you how to: * Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. * Write your own custom SQL, etc. * Perform statistical analysis in Tableau using R * Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you’ll learn * Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. * Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. * Integrate Tableau with R * Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert. « less
2017
A Random Matrix Theory Approach
This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.
2017
A Practical Real-World Approach to Gaining Actionable Insights from your Data
Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Pythonteaches you both basic and advanced concepts, including more » text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book: * Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics * Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews * Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern What you will learn: • Natural Language concepts • Analyzing Text syntax and structure • Text Classification • Text Clustering and Similarity analysis • Text Summarization • Semantic and Sentiment analysisReadership The book is for IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data. « less
2017
Scene Classification and Geometric Labeling
This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and more » synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks. « less
2016