Digi Academy

Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot

Digi Academy
A Milano

Chiedi il prezzo
Chiedi informazioni a un consulente Emagister

Informazioni importanti

Tipologia Corso
Luogo Milano
Inizio Scegli data
  • Corso
  • Milano
  • Inizio:
    Scegli data
Descrizione

Introduction Course Materials Facilities Prerequisites What We'll Be Discussing After completing this module, students will be able to: Successfully log into their virtual machine. Have a full understanding of what the course intends to cover.

Strutture (1)
Dove e quando
Inizio Luogo
Scegli data
Milano
Via Valtellina, 63, 20124, Milano, Italia
Visualizza mappa
Inizio Scegli data
Luogo
Milano
Via Valtellina, 63, 20124, Milano, Italia
Visualizza mappa

Cosa impari in questo corso?

Server
Clustering
SQL
Data Mining

Programma

This module will get students grounded in the terminology and concepts commonly utilized in data mining.


Concepts and Terminology


Data Mining and Results


CRISP-DM


Business Problems for Data Mining


Models, Induction, and Prediction


Data Mining Tasks


Key Concepts


Group discussion of data mining examples


After completing this module, students will be able to:


Have a firm understanding of the concept of data mining.


This module familiarizes the student with the data mining tools in SQL Server Analysis Services.


Introduction to SQL Server Data Tools


Project Walk-Through


Stepping Through the Data Mining Wizard


Testing and Validation of Mining Models


Cross Validation


The Mining Model Prediction Tab


Reports


The User Interface


Offline Mode and Immediate Mode


Data Source


Data View


Exploring Data


Named Calculation


Named Queries


Project Walk-Through to Completion of the Structure Parts 1 and 2


Explore the Models


Compare Mining Structures


Cross Validation


Creating Reports Using Reporting Services


Saving Queries


Saving Results to the Database


Multiple Nested Tables


After completing this module, students will be able to:


Explore the user interface.


Use offline mode and immediate mode.


Create and configure a data source.


Create and configure data view.


Explore data.


Create and configure named calculations.


Create and configure named queries.


Walk-through a project to completion.


Explore the models.


Compare mining structures.


Use cross validation.


Create reports using Reporting Services.


Save queries.


Save results to the database.


Create multiple nested tables off of a case table.


This module explains the Microsoft implementations of the generic types of algorithms uses in data mining. The students will work with each algorithm and implement an example of each.


Types of Data Mining Algorithms


Microsoft Decision Trees Algorithm


Microsoft Linear Regression Algorithm


Microsoft Clustering Algorithm


Microsoft Nave Bayes Algorithm


Microsoft Association Algorithm


Microsoft Sequence Clustering Algorithm


Microsoft Time Series Algorithm


Microsoft Neural Network Algorithm


Microsoft Logistic Regression Algorithm


Microsoft Association Rules Algorithm


Microsoft Sequence Clustering Algorithm


Microsoft Time Series Algorithm


Microsoft Neural Network Algorithm


After completing this module, students will be able to:


Use Microsoft Association Rules Algorithm.


Use Microsoft Sequence Clustering Algorithm.


Use Microsoft Time Series Algorithm.


Use Microsoft Neural Network Algorithm.


This module switches to the use of Excel with PowerPivot and the Data Mining Add-ins. Here the students will see the different capabilities between Excel and SQL Server Analysis Services and learn to use the data mining features of Excel and generate consumable reports from analytics and data mining.


Data Mining Tab


Connection


Data Preparation


Management


Model Usage


Accuracy and Validation


Data Modeling


Visio Data Mining Add-In


Data Preparation


Model Usage—Browse and Document Model


Model Usage—Query


Accuracy and Validation


Decision Trees


Logistic Regression


Nave Bayes


Neural Network


Estimate Tool


Cluster


Associate Tool


Forecast Tool


Table Analysis Tools


Visio Add-In


After completing this module, students will be able to:


Properly prepare data for mining.


Use Model Usage—Browse and Document Model.


Use Model Usage—Query.


Use Accuracy and Validation.


Use Decision Trees.


Use Logistic Regression.


Use Nave Bayes.


Use Neural Network.


Use Estimate Tool.


Use Cluster.


Use Associate Tool.


Use Forecast Tool.


Use Table Analysis Tools.


Use Visio Add-In.


This module consists of five scenarios to help reinforce the concepts covered in this course.


Scenario 1


Scenario 2


Scenario 3


Scenario 4


Scenario 5


n/a



Gli utenti che erano interessati a questo corso si sono informati anche su...
Leggi tutto