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Applied Data Mining for Business Analytics LiveLessons (Video Training)

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Description

  • Copyright 2016
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-421138-3
  • ISBN-13: 978-0-13-421138-1

6+ Hours of Video Instruction

The hands-on video guide to using data mining to enable timely, actionable, evidence-based decision-making throughout your organization!

Description

This easy video tutorial is the fastest way to master modern data science best practices and use them to promote timely, evidence-based decision-making! Applied Data Mining LiveLessons demystifies current best practices, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen shows you exactly how analytics and data mining work, why they’ve become so important, and how to apply them to your problems. Delen reviews key concepts, applications, and challenges; introduces advanced tools and technologies, including IBM Watson; and discusses privacy concerns associated with modern data mining. Next, he guides you through the entire data mining process, introducing KDD, CRISP-DM, SEMMA, and Six Sigma for data mining. You’ll watch him demonstrate prediction, classification, decision trees, and cluster analysis...key algorithms such as nearest neighbor...artificial neural networks...regression and time-series forecasting...text analytics and sentiment analysis...big data techniques, technologies, and more. In just hours, you’ll be ready to analyze huge volumes of data, discover crucial new insights, and make better, faster decisions!

Skill Level

  • Beginner to Intermediate

What You Will Learn
  • Core concepts of analytics, big data, and other related concepts
  • The role and the importance of data mining in analytics and evidence-based managerial decision making
  • Critical success factors for data mining
  • Proven processes for carrying out successful data mining projects
  • Basic concepts of these related fields: text mining, web mining, and social media mining
  • Where to find commercial and free/open source data mining software resources

Who Should Take This Course
  • For all professionals on analytics teams; professionals seeking certification; and for BA, MBA, and MS students in analytics programs (degree, certificate, or concentration)
  • For students in online university programs in operations research, MIS, decision sciences, management science, analytics, data mining, and/or data analysis

Course Requirements
  • There are no prerequisites for this course
  • Basic knowledge in data, information technology, or managerial decision-making will be helpful but is not essential
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Table of Contents

Lesson 1: Introduction to Analytics
Understand how data mining and analytics fit together, why analytics has become so popular, key analytics applications and challenges, and today’s cutting edge of analytics: IBM Watson.

Lesson 2: Introduction to Data Mining
Discover what data mining is and isn’t...explore today’s most common data mining applications...see what kind of patterns data mining can discover...explore popular data mining tools...consider privacy issues associated with data mining.

Lesson 3: The Data Mining Process
Explore key data mining-related processes and methodologies, including KDD, CRISP-DM, SEMMA, and Six Sigma for Data Mining, and choose the best options for your own projects.

Lesson 4: Data and Methods in Data Mining
Understand the role of data in data mining...preprocess your data...use prediction, classification, decision trees, cluster analysis, and the k-Means Clustering and Apriori algorithms...replace data mining misconceptions with realities.

Lesson 5: Data Mining Algorithms
Use nearest neighbor and similarity measure algorithms...explore artificial neural networks and support vector machines...use linear and logistic regression...perform time-series forecasting.

Lesson 6: Text Analytics and Text Mining
Explore Natural Language Processing...text mining applications, processes, and tools...and RapidMiner demonstration.

Lesson 7: Big Data Analytics
See where big data comes from...review the Vs that define big data...explore big data concepts, business problems, and technologies...discover the role of data scientists...get started with stream analytics and data stream mining.


About LiveLessons Video Training

The LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at: http://www.informit/livelessons

Sample Content

Table of Contents

Introduction

Lesson 1: Introduction to Analytics
Topics
1.1 What Is Analytics and Where Does Data Mining Fit In?
1.2 Popularity and Application Areas of Analytics
1.3 An Analytics Timeline and a Simple Taxonomy
1.4 Cutting Edge of Analytics: IBM Watson
1.5 Real-world Analytics Applications
Summary

Lesson 2: Introduction to Data Mining
Topics
2.1 What Is Data Mining, and What It Is Not?
2.2 The Most Common Data Mining Applications and Tools
2.3 Demonstration of Data Mining Tools (KNIME)
Summary

Lesson 3: The Data Mining Process
Topics
3.1 The Knowledge Discovery in Databases (KDD) Process
3.2 Cross-Industry Standard Process for Data Mining (CRISP-DM)
3.3 Sample, Explore, Modify, Model and Assess (SEMMA) Process and Six Sigma Process
3.4 Demonstration of Data Mining Tools (IBM SPSS Modeler and R)
Summary

Lesson 4: Data and Methods in Data Mining
Topics
4.1 The Nature of Data in Data Mining
4.2 Data Mining Methods: Predictive versus Descriptive
4.3 Evaluations Methods in Data Mining
4.4 Classification with Decision Trees
4.5 Clustering with k-Means Algorithm
4.6 Association Analysis with Apriori Algorithm
Summary

Lesson 5: Data Mining Algorithms
Topics
5.1 Nearest Neighbor Algorithm for Prediction Modeling
5.2 Artificial Neural Networks (ANN) and Support Vector Machines (SVM)
5.3 Linear Regression and Logistic Regression
Summary

Lesson 6: Text Analytics and Text Mining
Topics
6.1 Introduction to Text Mining and Natural Language Processing
6.2 Text Mining Applications and Text Mining Process
6.3 Text Mining Tools and Demonstration of Text Mining (RapidMiner and KNIME)
Summary

Lesson 7: Big Data Analytics
Topics
7.1 What Is Big Data and Where Does It Come From?
7.2 Fundamental Concepts and Technologies of Big Data
7.3 Demonstration of Big Data Analytics (SAS Visual Analytics)
7.4 Who are Data Scientists and Where Do They Come From?
Summary

Summary

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