Data Mining and Predictive Analytics for Business Decisions : A Case Study Approach

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using m...

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Bibliographic Details
Main Author: Fortino, Andres.
Format: eBook
Language: English
Published: Dulles, VA : Mercury Learning & Information, [2023]
Subjects:
Data mining.
Electronic data processing.
elektronické knihy
electronic books
ISBN: 9781683926740
1683926749
1683926730
9781683926733
Physical Description: 1 online resource (291 p.)

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Table of contents

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041 |a eng 
100 1 |a Fortino, Andres. 
245 1 0 |a Data Mining and Predictive Analytics for Business Decisions :  |b A Case Study Approach /  |c Andres Fortino. 
264 1 |a Dulles, VA :  |b Mercury Learning & Information,  |c [2023] 
300 |a 1 online resource (291 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Description based upon print version of record. 
505 0 |a Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Data Mining and Business -- Data Mining Algorithms and Activities -- Data is the New Oil -- Data-Driven Decision-Making -- Business Analytics and Business Intelligence -- Algorithmic Technologies Associated with Data Mining -- Data Mining and Data Warehousing -- Case Study 1.1: Business Applications of Data Mining -- Case A -- Classification -- Case B -- Regression -- Case C -- Anomaly Detection -- Case D -- Time Series -- Case E -- Clustering -- Reference -- Chapter 2: The Data Mining Process 
505 8 |a Data Mining as a Process -- Exploration -- Analysis -- Interpretation -- Exploitation -- Selecting a Data Mining Process -- The CRISP-DM Process Model -- Business Understanding -- Data Understanding -- Data Preparation -- Modeling -- Evaluation -- Deployment -- Selecting Data Analytics Languages -- The Choices for Languages -- References -- Chapter 3: Framing Analytical Questions -- How Does CRISP-DM Define the Business and Data Understanding Step? -- The World of the Business Data Analyst -- How Does Data Analysis Relate to Business Decision-Making? -- How Do We Frame Analytical Questions? 
505 8 |a What Are the Characteristics of Well-framed Analytical Questions? -- Exercise 3.1 -- Framed Questions About the Titanic Disaster -- Case Study 3.1 -- The San Francisco Airport Survey -- Case Study 3.2 -- Small Business Administration Loans -- References -- Chapter 4: Data Preparation -- How Does CRISP-DM Define Data Preparation? -- Steps in Preparing the Data Set for Analysis -- Data Sources and Formats -- What is Data Shaping? -- The Flat-File Format -- Application of Tools for Data Acquisition and Preparation -- Exercise 4.1 -- Shaping the Data File -- Exercise 4.2 -- Cleaning the Data File 
505 8 |a Ensuring the Right Variables are Included -- Using SQL to Extract the Right Data Set from Data Warehouses -- Case Study 4.1: Cleaning and Shaping the SFO Survey Data Set -- Case Study 4.2: Shaping the SBA Loans Data Set -- Case Study 4.3: Additional SQL Queries -- Reference -- Chapter 5: Descriptive Analysis -- Getting a Sense of the Data Set -- Describe the Data Set -- Explore the Data Set -- Verify the Quality of the Data Set -- Analysis Techniques to Describe the Variables -- Exercise 5.1 -- Descriptive Statistics -- Distributions of Numeric Variables -- Correlation 
505 8 |a Exercise 5.2 -- Descriptive Analysis of the Titanic Disaster Data -- Case Study 5.1: Describing the SFO Survey Data Set -- Solution Using R -- Solution Using Python -- Case Study 5.2: Describing the SBA Loans Data Set -- Solution Using R -- Solution Using Python -- Reference -- Chapter 6: Modeling -- What is a Model? -- How Does CRISP-DM Define Modeling? -- Selecting the Modeling Technique -- Modeling Assumptions -- Generate Test Design -- Design of Model Testing -- Build the Model -- Parameter Setting -- Models -- Model Assessment -- Where Do Models Reside in a Computer? -- The Data Mining Engine 
500 |a The Model 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Data mining. 
650 0 |a Electronic data processing. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Fortino, Andres  |t Data Mining and Predictive Analytics for Business Decisions  |d Bloomfield : Mercury Learning & Information,c2023  |z 9781683926757 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpDMPABDA8/data-mining-and?kpromoter=marc  |y Full text 

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