Certified Data Mining and Warehousing Professional

Course details

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).

Why should one take this certification?
This certification 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?
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.

Companies that hire Vskills Certified Data Mining and Warehousing Professional
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.

Table of Contents

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
• 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

Updated on 01 February, 2019