Data Science Algorithms in SSAS, Excel, R, and Azure ML
Don’t just use statistics, data mining, and machine learning without understanding how it works. Get the insights in the most popular algorithms.
Duration: 24 hours
What does this course cover?
Advanced data analysis techniques are gaining popularity. With modern statistics / data mining / machine learning 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 and labs show the algorithms usage in SQL Server Analysis Services, Excel using the SSAS algorithms, R language and SQL Server R Services, Azure ML native algorithms, and using the R algorithms in Azure ML. The attendees also learn how to evaluate different predictive and unsupervised 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.
The course also includes the explanation of the introductory statistics, including descriptive statistics, correlations and linear associations. Even the information theory is touched briefly. All of these methods are useful for gathering understanding of the data used for later analysis and advanced data profiling. Mining unstructured data, specifically texts, is covered in the course as well. Finally, a practical real life example, namely anomaly detection, concludes the course.
This class contains about 65% theory and demos explained by the Trainer. About 35% of the time attendees perform practical exercise. After every module the group discuss the results of the lab to make sure that the concept and the practical scenarios are well understood.
What do you need to know?
- At least moderate experience with data warehousing, reporting and On-Line Analytical Processing
- Familiarity with the Transact-SQL language
- Knowledge of a .NET language like C# or VB.NET is welcome as well
WHEN and WHERE is this course running?
”Several members of the class were impressed that he could answer any question without having to consult reference material”
The focus of the training is the theoretical concepts of advanced analytics. The importance for the attendees to fully understand how the algorithms work, how to correctly use them, how to prepare the data, and how to interpret the results is the first training goal. The software part is used just for showing the concepts and enriching the concept with examples. It helps a lot in understanding how to work with data, how to prepare useful derived variables, or to smooth values of a variable appropriately, or to discretize them correctly, etc. Attendees can and should be able to use different tools in the future.
Instructor-led training in class, with maximum number of attendees 12, 24 training hours spread in 3 days.
Online training: 8 sessions á 2.5 hours
Every attendee gets a .PDF printout of all slides and detailed lab instructions. In addition, attendees are welcome to copy the demo and lab solutions for further reference.
To evaluate the knowledge of the attendees we developed 60 different questions. The questions can be split into two halves to assess the knowledge before and after the training.
All our courses can be offered as a private delivery and tailored for your team's specific needs
Module 01. Introduction to data mining, machine learning, and statistics
Module 02: Introducing advanced analytics in SSAS, Excel, Azure ML and R
Lab: Getting familiar with the tools
Module 03: Statistics for data profiling and understanding
Lab: Data profiling and introductory statistics
Module 04: Data preparation
Lab 04: Using SSIS to split the data into training and test set and checking the split with Decision Trees
Module 05: Classification and prediction algorithms
Lab: Using the Naïve Bayes, Decision Trees, Logistic Regression, and Neural Network algorithms, and evaluating predictive models
Module 06: Estimation Algorithms
Lab: Using the Linear Regression and Regression Trees algorithms
Module 07: Unsupervised algorithms
Lab: Using the Association Rules, Clustering, and Sequence Clustering Algorithms
Module 08: Forecasting Algorithms
Lab 08: Using the Time Series, ARIMA and ARTXP algorithms
Module 09: Personal analysis of geographical and temporal data
Lab: Using Excel with Power Map and Power View, and Power BI Desktop
Module 10: Advanced personal analytics
Lab: Using Excel for data mining, using R in Power BI Desktop
Module 11: Analyzing texts with SSIS, Transact-SQL, SSAS, R, and Azure ML
Lab: Text mining