| Topics for practical |
1. Data warehouse design:
Design dimension tables.
Design fact tables.
Create an indexed view and rebuild columnstore indexes. |
2. Data Warehouse with Azure:
Create an Azure SQL Data Warehouse Project.
Develop tables in Azure SQL Data Warehouse.
Migrate Data Warehouse to Azure.
Pause and remove Azure data warehouse. |
3. Data Warehouse implementation and use:
Cleanse data with SQL Server Data Quality Services.
Create custom knowledge base.
Install Master Data Services and IIS.
Configure MDS and deploy sample MDS model.
Install MDS excel add-in and Update master data in excel.
Consume the data from the warehouse. |
4. Working with Data and Data Preprocessing:
Demonstrate the use of ARFF files taking input and display the output of the files.
Create your own excel file. Convert the excel file to .csv format and prepare it as ARFF files.
Preprocess and classify Customer dataset.
http://archive.ics.uci.edu/ml/
Perform Preprocessing, Classification techniques onAgriculture dataset.
(http://archive.ics.uci.edu/ml/)
Preprocess and classify Weather dataset.
http://archive.ics.uci.edu/ml/
Perform data Cleansing of customer dataset.
http://archive.ics.uci.edu/ml/
www.kdnuggets.com/datasets/ |
5. Performing classification on data sets:
Building a Decision Tree Classifier in Weka
Applying Naïve Bayes on Dataset for classification
Creating the Testing Dataset
Decision Tree Operation with R
Naïve Bayes Operation using R
Classify the dataset using decision tree.
www.kdnuggets.com/datasets/ |
6. Simple Clustering:
Perform Clustering technique on Customer dataset.
http://archive.ics.uci.edu/ml/
Perform Clustering technique on Agriculture dataset.
http://archive.ics.uci.edu/ml/
Perform Clustering technique on Weather dataset.
http://archive.ics.uci.edu/ml/ |
7. Implementing Clustering with Weka and R:
Clustering Fisher‘s Iris Dataset with the Simple k-Means Algorithm
Handling Missing Values
Results Analysis after Applying Clustering
Classification of Unlabeled Data
Clustering in R using Simple k-Means |
8. Implementing Apriori Algorithm with Weka and R:
Applying Predictive Apriori in Weka
Applying the Apriori Algorithm in Weka on a Real World Dataset
Applying the Apriori Algorithm in Weka on a Real World Larger Dataset
Applying the Apriori Algorithm on a Numeric Dataset |
9. Implementing Association Mining with R:
Applying Association Mining in R
Application of Association Mining on Numeric Data in R
Perform Association technique on Agriculture dataset.
http://archive.ics.uci.edu/ml/,
www.kdnuggets.com/datasets/
Perform Association technique on Agriculture dataset.
http://archive.ics.uci.edu/ml/ ,
www.kdnuggets.com/datasets/
Perform Association technique on Weather dataset. |
10. Web Mining:
Implement Hyperlink Induced Topic Search (HITS) algorithm
Implement PageRank Algorithm |