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Titlebook: Essentials of Business Analytics; An Introduction to t Bhimasankaram Pochiraju,Sridhar Seshadri Textbook 2019 Springer Nature Switzerland A

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發(fā)表于 2025-3-21 17:58:15 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Essentials of Business Analytics
副標題An Introduction to t
編輯Bhimasankaram Pochiraju,Sridhar Seshadri
視頻videohttp://file.papertrans.cn/316/315611/315611.mp4
概述Offers a comprehensive introductory approach to business analytics that includes an emphasis on big data handling, applications in different verticals and case studies.Highlights big data handling, ap
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Essentials of Business Analytics; An Introduction to t Bhimasankaram Pochiraju,Sridhar Seshadri Textbook 2019 Springer Nature Switzerland A
描述.This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters..The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text.. .Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business soluti
出版日期Textbook 2019
關鍵詞Business Analtyics; Big Data; Data Analysis; Data Visualization; Forecasting Analytics; Machine Learning;
版次1
doihttps://doi.org/10.1007/978-3-319-68837-4
isbn_ebook978-3-319-68837-4Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Data Collectionf the characteristics of the data in question. How do we collect data? What kinds of data exist? Where is it coming from? Before beginning to analyze data, analysts must know how to answer these questions. In doing so, we build the base upon which the rest of our examination follows. This chapter ai
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Data Management—Relational Database Systems (RDBMS)at can be stored and processed by computers. In order to process and manipulate data efficiently, it is very important that data is stored in an appropriate form. Data comes in many shapes and forms, and some of the most commonly known forms of data are numbers, text, images, and videos. Depending o
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Statistical Methods: Basic Inferencesrder to do this, we must be able to summarize and lay out datasets in a manner that allows us to use more advanced methods of examination. This chapter introduces fundamental methods of statistics, such as the central limit theorem, confidence intervals, hypothesis testing and analysis of variance (
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Statistical Methods: Regression Analysis systematically develop linear regression modeling of data. Chapter 6 on Basic inference is all the prerequisite that is required for this chapter. We start with motivating examples (Sect.?2). Section 3 deals with the methods and diagnostics for linear regression. We start with a discussion on what
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Text Analyticserstanding and examining data in word formats, which tend to be more unstructured and therefore more complex. Text analytics uses tools such as those embedded in R in order to extract meaning from large amounts of word-based data. Two methods are described in this chapter: bag-of-words and natural l
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Simulationg. Our focus will be on applications and to understand the steps in building a simulation model and interpreting the results of the model; the theoretical background can be found in the reference textbooks described at the end of the chapter. Simulation is a practical approach to decision making und
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