Data Mining Algorithms in SSAS, Excel, and R
Don't use data mining as a black box. Get a deep understanding of how the data mining algorithms work. This knowledge is not only theoretical; it helps you developing better models in production.
What you'll learn
Data mining is gaining popularity as the most advanced data analysis technique. With modern data mining engines, products, and packages, like SQL Server Analysis Services (SSAS), Excel, and R, data mining has become a black box. It is possible to use data mining without knowing how it works. However, not knowing how the algorithms work might lead to many problems, including using the wrong algorithm for a task, misinterpretation of the results, and more. This course explains how the most popular data mining algorithms work, when to use which algorithm, and advantages and drawbacks of each algorithm as well. Demonstrations show the algorithms usage in SSAS, Excel, and R.
Table of contents
- Introduction and Modules 2m
- Assumptions and Overview 1m
- What Is Data Mining? 2m
- Data Mining Types and Tasks 2m
- Demo: SQL Server Queries 2m
- Demo: SSRS Report 2m
- Virtuous Cycle and CRISP Model 2m
- Data Flow 1m
- Types of Analyses and SQL Server Tools 2m
- Demo: SSAS Cube 7m
- Demo: Data Mining Model 5m
- Excel Tools and R 2m
- Summary 1m
- Introduction 1m
- Naive Bayes Prior 2m
- Naive Bayes Posterior 2m
- Example and Usage 2m
- Demo: Naive Bayes in SSAS 5m
- Demo: Naive Bayes in Excel 4m
- Demo: Naive Bayes in R 6m
- Decision Trees 2m
- Decision Trees Example 2m
- Decision Trees Example and Usage 3m
- Demo: Decision Trees in SSAS 3m
- Demo: Decision Trees in Excel 2m
- Demo: Decision Trees in R 2m
- Summary 1m
- Introduction 1m
- Neural Network 2m
- Backpropagation and Logistic Regression 2m
- Usage 1m
- Demo: Neural Network and Logistic Regression in SSAS 3m
- Demo: Logistic Regression in Excel 3m
- Demo: Neural Network and Logistic Regression in R 4m
- Training and Test Sets 1m
- Lift and Profit Chart 2m
- Classification Matrix and Cross Validation 3m
- Demo: Evaluating Predictive Models 5m
- Summary 1m
- Introduction 1m
- Clustering and Hierarchical Methods 2m
- Hierarchical Methods and Dendrogram 1m
- Demo: Hierarchical Clustering in R 2m
- K-Means Clustering 3m
- Expectation-Maximization Clustering 2m
- Clustering Usage 1m
- Demo: Expectation-Maximization Clustering in SSAS 3m
- Demo: Finding Outliers in Excel 2m
- Summary 1m
About the author
Dejan Sarka, MCT and SQL Server MVP, is an independent consultant, trainer, and developer focusing on database & business intelligence applications. His specialties are advanced topics like data modeling, data mining, and data quality. On these toughest topics, he works and researches together with SolidQ and The Data Quality Institute. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or coauthor of eleven books about databases and SQL Server, wi... more