Dejan_Sarka.jpg

Dejan Sarka

Dejan Sarka, MCT and SQL Server MVP, is an independent trainer and consultant that focuses on development of database & business intelligence applications.  Besides projects, he spends about half of the time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or coauthor of twelve books about databases and SQL Server. Dejan Sarka also developed many courses and seminars for Microsoft, SolidQ and Pluralsight.

http://sqlblog.com/blogs/dejan_sarka/default.aspx http://sqlblog.com/blogs/dejan_sarka/rss.aspx

In this session, we are going to explain and test different DW features in SQL Server 2012, including star join optimization through bitmap filters, table partitioning, window functions, columnstore indices and more.
Excel is “The” analytical tool in Microsoft suite for advanced analysts. This session introduces Excel 2013 and 2010 business intelligence capabilities.
Writing efficient queries with temporal predicates is finally not a problem anymore.
This session introduces SQL Server 2012 and 2014 text mining capabilities.

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PASS SQL Saturday #460 Slovenia 2015 27 Jun 2015
So we are back. PASS SQL Saturday is coming to Slovenia again on December 12th, 2015. Remember last two years? We had two great events. According to feedback, everybody was satisfied and happy. Let's make another outstanding event! How can you help? First of all, these events are free for ...

Data Mining Algorithms – Support Vector Machines 23 Jun 2015
Support vector machines are both, unsupervised and supervised learning models for classification and regression analysis (supervised) and for anomaly detection (unsupervised). Given a set of training examples, each marked as belonging to one of categories, an SVM training algorithm builds a model ...

Data Mining Algorithms – Principal Component Analysis 02 Jun 2015
Principal component analysis (PCA) is a technique used to emphasize the majority of the variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. It is closely connected to eigenvectors and eigenvalues. A short definition of the algorithm: ...

Data Mining Algorithms – EM Clustering 12 May 2015
With the K-Means algorithm, each object is assigned to exactly one cluster. It is assigned to this cluster with a probability equal to 1.0. It is assigned to all other clusters with a probability equal to 0.0. This is hard clustering. Instead of distance, you can use a probabilistic measure to ...

Data Mining Algorithms – K-Means Clustering 17 Apr 2015
Hierarchical clustering could be very useful because it is easy to see the optimal number of clusters in a dendrogram and because the dendrogram visualizes the clusters and the process of building of that clusters. However, hierarchical methods don’t scale well. Just imagine how cluttered a ...