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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!
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!
Lesson 1: Introduction to Analytics
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
Lesson 2: Introduction to Data Mining
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)
Lesson 3: The Data Mining Process
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)
Lesson 4: Data and Methods in Data Mining
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
Lesson 5: Data Mining Algorithms
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
Lesson 6: Text Analytics and Text Mining
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)
Lesson 7: Big Data Analytics
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?