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.

Companies that hire Data Mining 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.

Apply for Data Mining and Warehousing Certification

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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

Certified Data Mining and Warehousing Professional

How It Works

Test Details

Benefits of Certification