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Data Mining with SQL Server 2005 – 2016 BI-DMNNG12-401-EN-1

3 Days (BI-DMNNG12-401-EN)

In this course attendees learn how to use data mining to find advanced patterns in their data and perform predictions based on the patterns found using SQL Server 2012 Analysis Services. This is a high-end course, enabling students to go beyond simple usage of wizards. In-depth understanding of the data mining algorithms, how they are built and how do they work, is provided.

  • Target Audience
    This course is intended for business intelligence application developers, advanced database administrators, and advanced analysts.

  • Prerequisites
    Before attending this course, it is recommended that students have the following skills:
    • 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.
    The following SolidQ courses are recommended as a learning path to this course:
    • Transact-SQL Fundamentals
    • BI Boot Camp 2008 R2 or BI
    Immersion 2012.

  • Course Objectives
    Upon completion of this course, the student will be able to:
    • Describe what data mining is and what business questions can it answer
    • Explain the process of a data mining project
    • Explore and understand your data using descriptive statistics, OLAP cubes, reports and other tools
    • Prepare the data to make better models
    • Understand the data mining algorithms, and when to use them
    • Create data mining models and browse them
    • Evaluate models to find the one that gives best results
    • Use SQL Server 2012 Integration Services data mining tasks
    • Do text mining with Integration Services and with Full-Text and Semantic Search
    • Understand and use the Data Mining Extensions (DMX) language
    • Deploy data mining models in production using custom application, OLAP cubes or reports developed with SQL Server 2008 Reporting Services.

  • Course Summary Outline
    Day 1
    Module 01: Introduction to Data Mining
    • Introduction
    • Business Questions

    • Data Mining Process • SQL Server Data Mining Tools
    Lab 01: Overview of SQL Server 2012 Data Mining Tools and Samples

    Module 02: Understanding the Data
    • Cases, Variables, and Measures
    • Descriptive Statistics with T-SQL and CLR
    • Data Profiling with SSAS and SSRS
    • Measuring Information
    Lab 02: Using a SSAS Multidimensional Cube and Excel for Data Overview

    Module 03: Data Preparation
    • Missing Values and Outliers
    • Derived Variables
    • Time Series
    • Sampling and Testing the Sampling
    Lab 03: Using SSIS to split the data into a training and test set and checking the split with Decision Trees

    Module 04: Data Mining Algorithms Part 1
    • Naïve Bayes
    • Decision Trees
    • Linear Regression and Regression Trees
    Lab 04: Creating Predictive Models

    Day 2
    Module 05: Data Mining Algorithms Part 2
    • Neural Network and Logistic Regression
    • Predictive Models Evaluation
    • Time Series
    Lab 05: Creating Predictive and Forecasting Models

    Module 06: Data Mining Algorithms Part 3
    • Association Rules
    • Clustering
    • Sequence Clustering
    Lab 06: Creating Undirected Models

    Module 07: Data Mining Extensions (DMX) Language
    • XMLA and Data Mining Objects
    • DDL Statements
    • DML Statements
    • DMX Select
    Lab 07: Using the DMX Language

    Module 08: Integration with SQL Server BI Suite
    • Integration with SSIS
    • Integration with SSRS
    • Integration with SSAS
    Lab 08: Using Data Mining with SSIS, SSRS and SSAS

    Day 3 Module 09: Data Mining with Excel
    • Excel Data Preparation and Data Mining
    • Table Analysis with Excel
    • Combining Data Mining with PowerPivot
    Lab 09: Excel Data Mining and PowerPivot Add-Ins

    Module 10: Analyzing Texts
    • Text Mining with SSIS
    • Using Full-Text Search
    • Semantic Search
    Lab 10: Text Mining with SSIS

    Module 11: Developing Data Mining Applications
    • Advanced DMX
    • Developing Data Mining Models with AMO
    • Using ADOMD.NET Client
    • Server Procedures and ADOMD.NET Server
    Lab 11: Advanced DMX

    Module 12: Administering Data Mining Models
    • Managing SSAS Databases
    • Monitoring SSAS
    • Data Mining Security
    Lab 12: Administering Data Mining Models