Essentials of Data Science and Analytics: Statistical Tools in Data Science and Analytics–an Overview of Machine Learning and R-Statistical Software, Sahay. Amar
Название: Data analytics in project management ISBN: 1138307289 ISBN-13(EAN): 9781138307285 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects.
Название: Data Analytics ISBN: 1138035483 ISBN-13(EAN): 9781138035485 Издательство: Taylor&Francis Рейтинг: Цена: 11176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Effective presentation of analytical results is the key to close a project successfully. With the meteoric rise in the popularity of data science and analytics, business executives focus on analytics` strategic interpretation and managerial implications, as well as value creation from analytics.
Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 110849420X ISBN-13(EAN): 9781108494205 Издательство: Cambridge Academ Рейтинг: Цена: 11563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
Автор: Wainwright Martin J Название: Cambridge Series in Statistical and Probabilistic Mathematic ISBN: 1108498027 ISBN-13(EAN): 9781108498029 Издательство: Cambridge Academ Рейтинг: Цена: 10771.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.
Автор: Washington, Simon Mannering, Fred (university Of S Название: Statistical and econometric methods for transportation data analysis ISBN: 0367199025 ISBN-13(EAN): 9780367199029 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies in various aspects of transportation planning, engineering, safety, and economics.
Описание: Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen provides a holistic approach covering key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies--including lessons from failed projects. It is all designed to help you gain a practical understanding you can apply for profit. * Leverage knowledge extracted via data mining to make smarter decisions * Use standardized processes and workflows to make more trustworthy predictions * Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * Understand predictive algorithms drawn from traditional statistics and advanced machine learning * Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
Описание: This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures.One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS—also helpful in some analytical methods not possible or practical to do in Excel.
Описание: Doubt over the trustworthiness of published empirical results is often a result of statistical mis-specification or invalid probabilistic assumptions. This course in empirical research methods enables the specification and validation of statistical models, facilitating their informed implementation and giving rise to trustworthy evidence.
Описание: Doubt over the trustworthiness of published empirical results is often a result of statistical mis-specification or invalid probabilistic assumptions. This course in empirical research methods enables the specification and validation of statistical models, facilitating their informed implementation and giving rise to trustworthy evidence.
Автор: Gentle, James Название: Statistical Analysis of Financial Data ISBN: 1138599492 ISBN-13(EAN): 9781138599499 Издательство: Taylor&Francis Рейтинг: Цена: 15310.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is about financial data - security prices and prices of derivatives, and the statistical methods for analyzing such data. It covers statistical models of branching processes, linear discrete time series models, and continuous-time stochastic models, all at an intermediate level (advanced undergraduate or beginning graduate).
Описание: The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users.Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.
Описание: This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes.It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together.The first three chapters provide an introduction to BA, importance of analytics, types of BA—descriptive, predictive, and prescriptive—along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics—machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.
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