Data Mining and Warehousing Certification

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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

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Data Mining and Warehousing Certification

  1. 1. Certified Data Mining and Warehousing Professional VS-1068
  2. 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. 3. Certified Data Mining and Warehousing Professional www.vskills.in 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
  4. 4. Certified Data Mining and Warehousing Professional www.vskills.in 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
  5. 5. Certified Data Mining and Warehousing Professional www.vskills.in 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
  6. 6. Certified Data Mining and Warehousing Professional www.vskills.in 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
  7. 7. Certified Data Mining and Warehousing Professional www.vskills.in 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
  8. 8. Certified Data Mining and Warehousing Professional www.vskills.in 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
  9. 9. Certified Data Mining and Warehousing Professional www.vskills.in 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)

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