Introduction to Data Mining and Analytics

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Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this text comprises a complete how-to and is an excellent resource for anyone considering the field. Case studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industry-standard applications. After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of “big data” and of text and image data mining. The text concludes by describing an analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace.

Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets (7版)

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Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets 作 / 譯 者 ： Frederick S. Hillier,Mark S. Hillier,Kari Schmedders,Molly Stephens I S B N - 13 ： 9781265040055 I S B N - 10 ： 1265040052 類 別： 管理科學 版 次： 7 版 年 份： 2023 規 格： 742 頁 出 版 商： McGraw Hill Education 目錄 PART I: THE ESSENCE OF MANAGEMENT SCIENCE AND BUSINESS ANALYTICS Ch 1 Introduction Ch 2 Overview of the Analysis Process PART II: MODELS FOR PREDICTIVE ANALYTICS Ch 3 Classification and Prediction Models for Predictive Analytics CH 4 Predictive Analytics Based on Traditional Forecasting Methods PART III: USING LINEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS Ch 5 Linear Programming: Basic Concepts Ch 6 Linear Programming: Formulation and Applications Ch 7 The Art of Modeling with Spreadsheets Ch 8 What-If Analysis for Linear Programming Ch 9 Network Optimization Problems PART IV: USING INTEGER OR NONLIEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS Ch10 Integer Programming Ch11 Nonlinear Programming PART V: TRADITIONAL UNCERTAINTY MODELS FOR PERFORMING PREDICTIVE OR PRESCRIPTIVE ANALYTICS Ch12 Decision Analysis Ch13 Queueing Models Ch14 Computer Simulation: Basic Concepts Ch15 Computer Simulation with Analytic Solver