Certified Data Mining and Warehousing Professional

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive Online e-Learning Access (LMS)
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification


$49.00 /-
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Why should I take this Data Mining certification?

Data mining is the process of extracting patterns from large sets of data by using statistical methods & artificial intelligence with database management. Data mining analyze large sets of data & convert it into useful information. This certification on Data Mining will teach you to apply algorithms and techniques to solve interesting real-world data mining challenges.

Study & Learn

  • Data Warehousing Models & Architecture
  • Data Warehousing Operational Systems
  • Clustering And Nearest-Neighbor Prediction
  • Machine Learning
  • Decision Tress
  • Neural Networks
  • Data Mining Algorithms
  • On Line Analytical Processing (OLAP)

How will I benefit from this Data Mining Certification?

Data Mining is one of the hottest trend in information technology today. There is a huge demand for Data Mining professionals. Data Scientists enjoy one of the top-paying jobs and work in many areas like big data, analytics, robotics and artificial intelligence. Hence Data Mining professionals has a large job demand growth & this growth has been increasing ever since.

Data Mining and Warehousing Table of Contents

https://www.vskills.in/certification/certified-data-Mining-and-warehousing-professional-table-of-contents

Data Mining and Warehousing Tutorial

http://vskills.in/certification/tutorial/information-technology/data-mining-and-warehousing-professional/

Data Mining and Warehousing Sample Questions

https://www.vskills.in/certification/certified-data-mining-and-warehousing-professional-sample-questions

Data Mining and Warehousing Mock Test

https://www.vskills.in/practice/data-mining-and-warehousing

Data Mining and Warehousing Interview Questions

https://www.vskills.in/interview-questions/data-mining-and-warehousing-interview-questions

Companies that hire Data Mining & Warehousing Professionals

Vskills Certified candidates will find employment opportunities in Big Companies like Google India, Microsft India, HCL, Tech Mahindra, Veritas, Citrix, Novartis and Bosch etc.

Data Mining & Warehousing Blogs

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Data Mining & Warehousing Jobs

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Data Mining & Warehousing Internships

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TABLE OF CONTENT


DATA WAREHOUSING INTRODUCTION

  • Introduction
  • Meaning of Data Warehousing
  • History of Data Warehousing

DATA WAREHOUSING CHARACTERISTICS

  • Introduction
  • Data Warehousing
  • Operational vs. Informational Systems
  • Characteristics of Data Warehousing

ONLINE TRANSACTION PROCESSING

  • Introduction
  • Data Warehousing and OLTP Systems
  • Processes in Data Warehousing OLTP
  • What is OLAP?
  • Who uses OLAP and WHY?
  • Multi-Dimensional View s
  • Benefits of OLAP

DATA WAREHOUSING MODELS

  • Introduction
  • The Data Warehouse Model
  • Data Modeling for Data Warehouses

DATA WAREHOUSING ARCHITECTURE

  • Introduction
  • Structure of a Data Warehouse
  • Data Warehouse Physical Architectures
  • Principles of a Data Warehousing

DATA WAREHOUSING AND OPERATIONAL SYSTEMS

  • Introduction
  • Operational Systems
  • “ Warehousing” Data outside the Operational Systems
  • Integrating Data from more than one Operational System
  • Differences between Transaction and Analysis Processes
  • Data is mostly Non-volatile
  • Data saved for longer periods than in transaction systems
  • Logical Transformation of Operational Data
  • Structured Extensible Data Model
  • Data Warehouse model aligns with the business structure
  • Transformation of the Operational State Information
  • De-normalization of Data
  • Static Relationships in Historical Data
  • Physical Transformation of Operational Data
  • Operational terms transformed into uniform business terms

DATA WAREHOUSING CONSIDERATIONS

  • Introduction
  • Building a Data Warehouse
  • Nine Decisions in the design of a Data Warehouse

DATA WAREHOUSING - APPLICATIONS

  • Introduction
  • Data Warehouse Application

ROI AND DESIGN CONSIDERATIONS

  • Introduction
  • Need of a Data Warehouse
  • Business Considerations: Return on Investment
  • Organizational Issues
  • Design Considerations
  • Data content
  • Metadata
  • Data Distribution
  • Tools
  • Performance Considerations

TECHNICAL AND IMPLEMENTATION CONSIDERATIONS

  • Introduction
  • Technical Considerations
  • Hardware Platforms
  • Balanced Approach
  • Optimal hardware architecture for parallel queryscalability
  • Data warehouse and DBMS Specialization
  • Communications Infrastructure
  • Implementation Considerations
  • Access Tools

DATA WAREHOUSING BENEFITS

  • Introduction
  • Benefits of Data Warehousing
  • Problems with Data Warehousing
  • Criteria for a Data Warehouse

PROJECT MANAGEMENT PROCESS

  • Introduction
  • Project Management Process
  • The Scope Statement
  • Project Planning
  • Project Scheduling
  • Software Project Planning
  • Critical Path Method
  • Decision Making

WORK BREAKDOWN STRUCTURE

  • Introduction
  • Work Breakdow n Structure (WBS)
  • How to build a WBS (a serving suggestion)
  • To Create Work Breakdown Structure
  • From WBS to Activity Plan
  • Estimating Time

PROJECT ESTIMATION AND RISK

  • Introduction
  • Project Estimation
  • Analyzing Probability & Risk

MANAGING RISK

  • Introduction
  • Risk Analysis
  • Risk Management
  • Risk Analysis
  • Managing Risks: Internal & External
  • Internal and External Risks
  • Critical Path Analysis

DATA MINING CONCEPTS

  • Introduction
  • Data Mining
  • Data Mining Background
  • Inductive Learning
  • Statistics
  • Machine Learning

DATA MINING AND KDD

  • Introduction
  • What is Data Mining?
  • Data Mining: Definitions
  • KDD vs. Data Mining
  • Stages OF KDD
  • Machine Learning vs. Data Mining
  • Data Mining vs. DBMS
  • Data Warehouse
  • Statistical Analysis

ELEMENTS OF DATA MINING

  • Introduction
  • Elements and uses of Data Mining
  • Relationships & Patterns
  • Data Mining Problems/Issues
  • Goals of Data Mining and Know ledge Discovery

DATA INFORMATION AND KNOWLEDGE

  • Data, Information and Knowledge
  • What can Data Mining do?
  • How does Data Mining Work?
  • Data Mining in a Nutshell

DATA MINING MODELS

  • Data Mining
  • Data Mining Models
  • Discovery of Association Rules
  • Discovery of Classification Rules

DATA MINING ISSUES AND CHALLENGES

  • Data Mining Problems/Issues
  • Other Mining Problems
  • Data mining Application Areas
  • Data Mining Applications-Case Studies
  • Housing Loan Prepayment Prediction
  • Mortgage Loan Delinquency Prediction
  • Crime Detection
  • Store-Level Fruits Purchasing Prediction
  • Other Application Area

DM NEAREST NEIGHBOR AND CLUSTERING TECHNIQUES

  • Types of Knowledge Discovered during Data Mining
  • Comparing the Technologies
  • Clustering And Nearest-Neighbor Prediction Technique

DECISION TREES

  • What is a Decision Tree?
  • Decision Trees
  • Where to use Decision Trees?
  • Tree Construction Principle
  • The Generic Algorithm
  • Guillotine Cut
  • Over Fit

DECISION TREES - ADVANCED

  • Best Split
  • Decision Tree Construction Algorithms
  • Cart
  • ID3
  • C4.5
  • Chaid
  • How the Decision Tree Works
  • State of the Industry

NEURAL NETWORKS

  • Basics of Neural Networks
  • Are neural networks easy to use?
  • Business Scorecard
  • Where to use Neural Networks
  • Neural Networks for Clustering
  • Neural Networks for Feature Extraction
  • Applications Score Card
  • The General Idea

NEURAL NETWORKS - ADVANCED

  • What is a Neural Network Pparadigm?
  • Design decisions in architecting a neural network
  • Different types of Neural Networks
  • Kohonen feature Maps
  • Applications of Neural Networks
  • Knowledge Extraction Through Data Mining

ASSOCIATION RULES AND GENETIC ALGORITHM

  • Association Rules
  • Basic Algorithms for Finding Association Rules
  • Association Rules among Hierarchies
  • Negative Associations
  • Additional Considerations for Association Rules
  • Genetic Algorithm
  • Crossover
  • Genetic Algorithms In detail
  • Mutation
  • Problem-Dependent Parameters
  • Encoding
  • The Evaluation Step
  • Data Mining using GA

OLAP MULTIDIMENSIONAL DATA MODEL

  • On Line Analytical Processing
  • OLAP Example
  • What is OLAP?
  • Who uses OLAP and WHY?
  • Multi-Dimensional Views
  • Complex Calculations
  • Time Intelligence

OLAP CHARACTERISTICS

  • Definitions of OLAP
  • Comparison of OLAP and OLTP
  • Characteristics of OLAP: FASMI
  • Basic Features of OLAP
  • Special features

MULTIDIMENSIONAL AND MULTIRELATIONAL OLAP

  • Introduction
  • Multidimensional Data Model
  • Multidimensional versus Multi-relational OLAP
  • OLAP Guidelines

OLAP OPERATIONS

  • Introduction
  • OLAP Operations
  • Lattice of Cubes, Slice and Dice Operations
  • Relational Representation of the Data Cube
  • DBMS
  • Data Mining
  • Mining Association Rules
  • Classification by Decision Trees and Rules
  • Prediction Methods

MOLAP/ ROLAP TOOLS

  • Categorization of OLAP Tools
  • MOLAP
  • ROLAP
  • Managed Query Environment (MQE)
  • Cognos PowerPlay

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