Vskills Data Mining and Warehousing Professional assesses the candidate for a company’s data mining and warehousing needs. The certification tests the candidates on various areas in data mining and warehousing which include knowledge of planning, managing, designing, implementing, supporting, maintaining and analyzing the organization’s data warehouse and covering data mining and On-Line Analytical Processing (OLAP). http://www.vskills.in/certification/Certified-Data-Mining-and-Warehousing-Professional

1. Certified Data Mining and
Warehousing Professional
VS-1068

2. Certified Data Mining and Warehousing Professional
www.vskills.in
CCCCertifiedertifiedertifiedertified DataDataDataData Mining andMining andMining andMining and WarehousingWarehousingWarehousingWarehousing
ProfessionalProfessionalProfessionalProfessional
Certification CodeCertification CodeCertification CodeCertification Code VS-1068
VskillsVskillsVskillsVskills DataDataDataData Mining andMining andMining andMining and Warehousing ProfessionalWarehousing ProfessionalWarehousing ProfessionalWarehousing Professional assesses the candidate for a company’s
data mining and warehousing needs. The certification tests the candidates on various areas
in data mining and warehousing which include knowledge of planning, managing,
designing, implementing, supporting, maintaining and analyzing the organization’s data
warehouse and covering data mining and On-Line Analytical Processing (OLAP).
Why should one take this certification?Why should one take this certification?Why should one take this certification?Why should one take this certification?
This Course is intended for professionals and graduates wanting to excel in their chosen
areas. It is also well suited for those who are already working and would like to take
certification for further career progression.
Earning Vskills Data Mining and Warehousing Professional Certification can help
candidate differentiate in today's competitive job market, broaden their employment
opportunities by displaying their advanced skills, and result in higher earning potential.
Who will benefit from taking this certification?Who will benefit from taking this certification?Who will benefit from taking this certification?Who will benefit from taking this certification?
Job seekers looking to find employment in IT department of various companies, students
generally wanting to improve their skill set and make their CV stronger and existing
employees looking for a better role can prove their employers the value of their skills
through this certification
Test DetailsTest DetailsTest DetailsTest Details
• Duration:Duration:Duration:Duration: 60 minutes
• No. of questions:No. of questions:No. of questions:No. of questions: 50
• Maximum marks:Maximum marks:Maximum marks:Maximum marks: 50, Passing marks: 25 (50%)
There is no negative marking in this module.
Fee StruFee StruFee StruFee Structurecturecturecture
Rs. 4,500/- (Includes all taxes)
Companies that hire Vskills CertifiedCompanies that hire Vskills CertifiedCompanies that hire Vskills CertifiedCompanies that hire Vskills Certified Data Mining and WarehousingData Mining and WarehousingData Mining and WarehousingData Mining and Warehousing
ProfessionalProfessionalProfessionalProfessional
Data Mining and Warehousing professional are in great demand. Companies specializing
in Integration Services are constantly hiring knowledgeable professionals. Various banks,
telecom and IT companies also need data mining and warehousing professionals for data
management and analysis.

3. Certified Data Mining and Warehousing Professional
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Table of Contents
1.1.1.1. DATA WAREHOUSING INTRODUCTIONDATA WAREHOUSING INTRODUCTIONDATA WAREHOUSING INTRODUCTIONDATA WAREHOUSING INTRODUCTION
1.1 Introduction
1.2 Meaning of Data Warehousing
1.3 History of Data Warehousing
2.2.2.2. DATA WAREHOUSING CHARACTERISTICSDATA WAREHOUSING CHARACTERISTICSDATA WAREHOUSING CHARACTERISTICSDATA WAREHOUSING CHARACTERISTICS
2.1 Introduction
2.2 Data Warehousing
2.3 Operational vs. Informational Systems
2.4 Characteristics of Data Warehousing
3.3.3.3. ONLINE TRANSACTION PROCESSINGONLINE TRANSACTION PROCESSINGONLINE TRANSACTION PROCESSINGONLINE TRANSACTION PROCESSING
3.1 Introduction
3.2 Data Warehousing and OLTP Systems
3.3 Processes in Data Warehousing OLTP
3.4 What is OLAP?
3.5 Who uses OLAP and WHY?
3.6 Multi-Dimensional View s
3.7 Benefits of OLAP
4.4.4.4. DATA WAREHOUSING MODELSDATA WAREHOUSING MODELSDATA WAREHOUSING MODELSDATA WAREHOUSING MODELS
4.1 Introduction
4.2 The Data Warehouse Model
4.3 Data Modeling for Data Warehouses
5.5.5.5. DATA WAREHOUSING ARCHITECTUREDATA WAREHOUSING ARCHITECTUREDATA WAREHOUSING ARCHITECTUREDATA WAREHOUSING ARCHITECTURE
5.1 Introduction
5.2 Structure of a Data Warehouse
5.3 Data Warehouse Physical Architectures
5.4 Principles of a Data Warehousing
6.6.6.6. DATA WAREHOUSING AND OPERATIONAL SYSTEMSDATA WAREHOUSING AND OPERATIONAL SYSTEMSDATA WAREHOUSING AND OPERATIONAL SYSTEMSDATA WAREHOUSING AND OPERATIONAL SYSTEMS
6.1 Introduction
6.2 Operational Systems
6.3 “ Warehousing” Data outside the Operational Systems
6.4 Integrating Data from more than one Operational System
6.5 Differences between Transaction and Analysis Processes
6.6 Data is mostly Non-volatile
6.7 Data saved for longer periods than in transaction systems

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6.8 Logical Transformation of Operational Data
6.9 Structured Extensible Data Model
6.10 Data Warehouse model aligns with the business structure
6.11 Transformation of the Operational State Information
6.12 De-normalization of Data
6.13 Static Relationships in Historical Data
6.14 Physical Transformation of Operational Data
6.15 Operational terms transformed into uniform business terms
7.7.7.7. DATA WAREHOUSING CONDATA WAREHOUSING CONDATA WAREHOUSING CONDATA WAREHOUSING CONSIDERATIONSSIDERATIONSSIDERATIONSSIDERATIONS
7.1 Introduction
7.2 Building a Data Warehouse
7.3 Nine Decisions in the design of a Data Warehouse
8.8.8.8. DATA WAREHOUSINGDATA WAREHOUSINGDATA WAREHOUSINGDATA WAREHOUSING ---- APPLICATIONSAPPLICATIONSAPPLICATIONSAPPLICATIONS
8.1 Introduction
8.2 Data Warehouse Application
9.9.9.9. ROI AND DESIGN CONSIDERATIONSROI AND DESIGN CONSIDERATIONSROI AND DESIGN CONSIDERATIONSROI AND DESIGN CONSIDERATIONS
9.1 Introduction
9.2 Need of a Data Warehouse
9.3 Business Considerations: Return on Investment
9.4 Organizational Issues
9.5 Design Considerations
9.6 Data content
9.7 Metadata
9.8 Data Distribution
9.9 Tools
9.10 Performance Considerations
10.10.10.10. TECHNICAL AND IMPLEMENTATION CONSIDERATIONSTECHNICAL AND IMPLEMENTATION CONSIDERATIONSTECHNICAL AND IMPLEMENTATION CONSIDERATIONSTECHNICAL AND IMPLEMENTATION CONSIDERATIONS
10.1 Introduction
10.2 Technical Considerations
10.3 Hardware Platforms
10.4 Balanced Approach
10.5 Optimal hardware architecture for parallel queryscalability
10.6 Data warehouse and DBMS Specialization
10.7 Communications Infrastructure
10.8 Implementation Considerations
10.9 Access Tools
11.11.11.11. DATA WAREHOUSING BENEFITSDATA WAREHOUSING BENEFITSDATA WAREHOUSING BENEFITSDATA WAREHOUSING BENEFITS
11.1 Introduction
11.2 Benefits of Data Warehousing
11.3 Problems with Data Warehousing
11.4 Criteria for a Data Warehouse

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12.12.12.12. PROJECT MANAGEMENT PROCESSPROJECT MANAGEMENT PROCESSPROJECT MANAGEMENT PROCESSPROJECT MANAGEMENT PROCESS
12.1 Introduction
12.2 Project Management Process
12.3 The Scope Statement
12.4 Project Planning
12.5 Project Scheduling
12.6 Software Project Planning
12.7 Critical Path Method
12.8 Decision Making
13.13.13.13. WORK BREWORK BREWORK BREWORK BREAKDOWN STRUCTUREAKDOWN STRUCTUREAKDOWN STRUCTUREAKDOWN STRUCTURE
13.1 Introduction
13.2 Work Breakdow n Structure (WBS)
13.3 How to build a WBS (a serving suggestion)
13.4 To Create Work Breakdown Structure
13.5 From WBS to Activity Plan
13.6 Estimating Time
14.14.14.14. PROJECT ESTIMATION AND RISKPROJECT ESTIMATION AND RISKPROJECT ESTIMATION AND RISKPROJECT ESTIMATION AND RISK
14.1 Introduction
14.2 Project Estimation
14.3 Analyzing Probability & Risk
15.15.15.15. MANAGING RISKMANAGING RISKMANAGING RISKMANAGING RISK
15.1 Introduction
15.2 Risk Analysis
15.3 Risk Management
15.4 Risk Analysis
15.5 Managing Risks: Internal & External
15.6 Internal and External Risks
15.7 Critical Path Analysis
16.16.16.16. DATA MINING CONCEPTSDATA MINING CONCEPTSDATA MINING CONCEPTSDATA MINING CONCEPTS
16.1 Introduction
16.2 Data Mining
16.3 Data Mining Background
16.4 Inductive Learning
16.5 Statistics
16.6 Machine Learning
17.17.17.17. DATA MINING AND KDDDATA MINING AND KDDDATA MINING AND KDDDATA MINING AND KDD
17.1 Introduction
17.2 What is Data Mining?
17.3 Data Mining: Definitions
17.4 KDD vs. Data Mining

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17.5 Stages OF KDD
17.6 Machine Learning vs. Data Mining
17.7 Data Mining vs. DBMS
17.8 Data Warehouse
17.9 Statistical Analysis
18.18.18.18. ELEMENTS OF DATELEMENTS OF DATELEMENTS OF DATELEMENTS OF DATA MININGA MININGA MININGA MINING
18.1 Introduction
18.2 Elements and uses of Data Mining
18.3 Relationships & Patterns
18.4 Data Mining Problems/Issues
18.5 Goals of Data Mining and Know ledge Discovery
19.19.19.19. DATA INFORMATION AND KNOWLEDGEDATA INFORMATION AND KNOWLEDGEDATA INFORMATION AND KNOWLEDGEDATA INFORMATION AND KNOWLEDGE
19.1 Data, Information and Knowledge
19.2 What can Data Mining do?
19.3 How does Data Mining Work?
19.4 Data Mining in a Nutshell
20.20.20.20. DATA MINING MODELSDATA MINING MODELSDATA MINING MODELSDATA MINING MODELS
20.1 Data Mining
20.2 Data Mining Models
20.3 Discovery of Association Rules
20.4 Discovery of Classification Rules
21.21.21.21. DATA MINING ISSUES AND CHALLENGESDATA MINING ISSUES AND CHALLENGESDATA MINING ISSUES AND CHALLENGESDATA MINING ISSUES AND CHALLENGES
21.1 Data Mining Problems/Issues
21.2 Other Mining Problems
21.3 Data mining Application Areas
21.4 Data Mining Applications-Case Studies
21.5 Housing Loan Prepayment Prediction
21.6 Mortgage Loan Delinquency Prediction
21.7 Crime Detection
21.8 Store-Level Fruits Purchasing Prediction
21.9 Other Application Area
22.22.22.22. DM NEAREST NEIGHBOR AND CLUSTERING TECHNIQUESDM NEAREST NEIGHBOR AND CLUSTERING TECHNIQUESDM NEAREST NEIGHBOR AND CLUSTERING TECHNIQUESDM NEAREST NEIGHBOR AND CLUSTERING TECHNIQUES
22.1 Types of Knowledge Discovered during Data Mining
22.2 Comparing the Technologies
22.3 Clustering And Nearest-Neighbor Prediction Technique
23.23.23.23. DECISION TREESDECISION TREESDECISION TREESDECISION TREES
23.1 What is a Decision Tree?
23.2 Decision Trees
23.3 Where to use Decision Trees?
23.4 Tree Construction Principle

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23.5 The Generic Algorithm
23.6 Guillotine Cut
23.7 Over Fit
24.24.24.24. DECISION TREESDECISION TREESDECISION TREESDECISION TREES ---- ADVANCEDADVANCEDADVANCEDADVANCED
24.1 Best Split
24.2 Decision Tree Construction Algorithms
24.3 Cart
24.4 ID3
24.5 C4.5
24.6 Chaid
24.7 How the Decision Tree Works
24.8 State of the Industry
25.25.25.25. NEURAL NETWORKSNEURAL NETWORKSNEURAL NETWORKSNEURAL NETWORKS
25.1 Basics of Neural Networks
25.2 Are neural networks easy to use?
25.3 Business Scorecard
25.4 Where to use Neural Networks
25.5 Neural Networks for Clustering
25.6 Neural Networks for Feature Extraction
25.7 Applications Score Card
25.8 The General Idea
26.26.26.26. NEURAL NETWORKSNEURAL NETWORKSNEURAL NETWORKSNEURAL NETWORKS ---- ADVANCEDADVANCEDADVANCEDADVANCED
26.1 What is a Neural Network Pparadigm?
26.2 Design decisions in architecting a neural network
26.3 Different types of Neural Networks
26.4 Kohonen feature Maps
26.5 Applications of Neural Networks
26.6 Knowledge Extraction Through Data Mining
27.27.27.27. ASSOCIATION RULES AND GENETIC ALGORITHMASSOCIATION RULES AND GENETIC ALGORITHMASSOCIATION RULES AND GENETIC ALGORITHMASSOCIATION RULES AND GENETIC ALGORITHM
27.1 Association Rules
27.2 Basic Algorithms for Finding Association Rules
27.3 Association Rules among Hierarchies
27.4 Negative Associations
27.5 Additional Considerations for Association Rules
27.6 Genetic Algorithm
27.7 Crossover
27.8 Genetic Algorithms In detail
27.9 Mutation
27.10 Problem-Dependent Parameters
27.11 Encoding
27.12 The Evaluation Step
27.13 Data Mining using GA

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28.28.28.28. OLAP MULTIDOLAP MULTIDOLAP MULTIDOLAP MULTIDIMENSIONAL DATA MODELIMENSIONAL DATA MODELIMENSIONAL DATA MODELIMENSIONAL DATA MODEL
28.1 On Line Analytical Processing
28.2 OLAP Example
28.3 What is OLAP?
28.4 Who uses OLAP and WHY?
28.5 Multi-Dimensional Views
28.6 Complex Calculations
28.7 Time Intelligence
29.29.29.29. OLAP CHARACTERISTICSOLAP CHARACTERISTICSOLAP CHARACTERISTICSOLAP CHARACTERISTICS
29.1 Definitions of OLAP
29.2 Comparison of OLAP and OLTP
29.3 Characteristics of OLAP: FASMI
29.4 Basic Features of OLAP
29.5 Special features
30.30.30.30. MULTIDIMENSIONAL AND MULTIRELATIONAL OLAPMULTIDIMENSIONAL AND MULTIRELATIONAL OLAPMULTIDIMENSIONAL AND MULTIRELATIONAL OLAPMULTIDIMENSIONAL AND MULTIRELATIONAL OLAP
30.1 Introduction
30.2 Multidimensional Data Model
30.3 Multidimensional versus Multi-relational OLAP
30.4 OLAP Guidelines
31.31.31.31. OLAP OPERATIONSOLAP OPERATIONSOLAP OPERATIONSOLAP OPERATIONS
31.1 Introduction
31.2 OLAP Operations
31.3 Lattice of Cubes, Slice and Dice Operations
31.4 Relational Representation of the Data Cube
31.5 DBMS
31.6 Data Mining
31.7 Mining Association Rules
31.8 Classification by Decision Trees and Rules
31.9 Prediction Methods
32.32.32.32. MOLAP/ ROLAP TOOLSMOLAP/ ROLAP TOOLSMOLAP/ ROLAP TOOLSMOLAP/ ROLAP TOOLS
32.1 Categorization of OLAP Tools
32.2 MOLAP
32.3 ROLAP
32.4 Managed Query Environment (MQE)
32.5 Cognos PowerPlay

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Sample QuestionsSample QuestionsSample QuestionsSample Questions
1.1.1.1. TheTheTheThe termtermtermterm DSSDSSDSSDSS refer torefer torefer torefer to _________________._________________._________________._________________.
A. Data Supply System
B. Decision Support System
C. Deducted Services System
D. None of the above
2222.... Partition elimination is used inPartition elimination is used inPartition elimination is used inPartition elimination is used in _________________._________________._________________._________________.
A. De-duplication
B. Range Partitioning
C. Round Robin Partitioning
D. None of the above
3333. The. The. The. The termtermtermterm BPRBPRBPRBPR expands toexpands toexpands toexpands to _________________._________________._________________._________________.
A. Business Process Research
B. Business Practice Research
C. Business Process Re-engineering
D. None of the above
4444. The. The. The. The decision node of a decision tree tests how many attribute valuesdecision node of a decision tree tests how many attribute valuesdecision node of a decision tree tests how many attribute valuesdecision node of a decision tree tests how many attribute values
_________________._________________._________________._________________.
A. Single
B. Double
C. Triple
D. None of the above
5555.... BackBackBackBack propagationpropagationpropagationpropagation neural network usesneural network usesneural network usesneural network uses _________________._________________._________________._________________.
A. Feed-forward topology
B. Feed-backward topology
C. Feed-either topology
D. None of the above
Answers: 1 (B), 2 (B), 3 (C), 4 (A), 5 (A)