Business Analytics DeepDive – Data Mining Algorithms in SSAS, Excel, R and Azure ML

BI-BADDDMA-EN Don’t just use data mining and machine learning without understanding how it works. Get the insights in the most popular algorithms.


Duration: 1 day

Level: 400

What does this course cover?

Data mining as the most advanced data analysis technique is gaining popularity. With modern data mining engines, products and packages, like SQL Server Analysis Services (SSAS), Excel, R, and Azure ML, 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 SQL Server Analysis Services, Excel 2013 using the SQL Server algorithms, R, Azure ML native algorithms, and using the R algorithms in Azure ML. You will also learn how to evaluate different predictive models. Algorithms explained include Naïve Bayes, Decision Trees, Neural Networks, Logistic Regression, Perceptron Model, Linear Regression, Regression Trees, Ordinal Regression, Poisson Regression, Principal Component Analysis, Support Vector Machines, Hierarchical Clustering, K-Means Clustering, Expectation-Maximization Clustering, Association Rules, Sequence Clustering, Auto-Regressive Trees with Cross-Prediction (ARTXP), Auto-Regressive Integrated Moving Average (ARIMA), and Time Series. Algorithms usage is explained through real life use case as well, on a fraud detection example.

“Very profound knowledge – though fully understandable”

WHEN and WHERE is this course running?

Course outline

  • Introduction to data mining and / or machine learning
  • Classification, prediction and estimation algorithms
  • Forecasting and unsupervised algorithms
  • Data mining project: fraud detection

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