Computing for Data Analysis: Theory and Practices
Title | Computing for Data Analysis: Theory and Practices PDF eBook |
Author | Sanjay Chakraborty |
Publisher | Springer Nature |
Total Pages | 230 |
Release | 2023-02-04 |
Genre | Technology & Engineering |
ISBN | 9811980047 |
This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.
Computer-Aided Qualitative Data Analysis
Title | Computer-Aided Qualitative Data Analysis PDF eBook |
Author | Udo Kelle |
Publisher | SAGE |
Total Pages | 246 |
Release | 1995-07-20 |
Genre | Mathematics |
ISBN | 9781446224908 |
Computer-Aided Qualitative Data Analysis offers an invaluable guide to the rapidly developing arena of research practice. It will be essential reading for researchers and graduate students across the social sciences who are engaged in qualitative research.
Data Science in Theory and Practice
Title | Data Science in Theory and Practice PDF eBook |
Author | Maria Cristina Mariani |
Publisher | John Wiley & Sons |
Total Pages | 404 |
Release | 2021-10-12 |
Genre | Mathematics |
ISBN | 1119674689 |
DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.
Data Analysis for the Life Sciences with R
Title | Data Analysis for the Life Sciences with R PDF eBook |
Author | Rafael A. Irizarry |
Publisher | CRC Press |
Total Pages | 461 |
Release | 2016-10-04 |
Genre | Mathematics |
ISBN | 1498775861 |
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
Classification and Data Analysis
Title | Classification and Data Analysis PDF eBook |
Author | Krzysztof Jajuga |
Publisher | Springer Nature |
Total Pages | 334 |
Release | 2020-08-28 |
Genre | Business & Economics |
ISBN | 3030523489 |
This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2019, held in Szczecin, Poland, on September 18–20, 2019. Providing a balance between theoretical and methodological contributions and empirical papers, it covers a broad variety of topics, ranging from multivariate data analysis, classification and regression, symbolic (and other) data analysis, visualization, data mining, and computer methods to composite measures, and numerous applications of data analysis methods in economics, finance and other social sciences. The book is intended for a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.
Information-Theoretic Methods in Data Science
Title | Information-Theoretic Methods in Data Science PDF eBook |
Author | Miguel R. D. Rodrigues |
Publisher | Cambridge University Press |
Total Pages | 561 |
Release | 2021-04-08 |
Genre | Computers |
ISBN | 1108427138 |
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Introduction to Data Science
Title | Introduction to Data Science PDF eBook |
Author | Rafael A. Irizarry |
Publisher | CRC Press |
Total Pages | 794 |
Release | 2019-11-20 |
Genre | Mathematics |
ISBN | 1000708039 |
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.