Certified Data Mining and Warehousing Professional
- Offered byVSkills
Certified Data Mining and Warehousing Professional at VSkills Overview
Duration | 14 hours |
Total fee | ₹3,499 |
Mode of learning | Online |
Credential | Certificate |
Future job roles | Director - Global Analytics Operations , VBA Developer, Senior Software Engineer , Commercial Banking |
Certified Data Mining and Warehousing Professional at VSkills Highlights
- Earn a certificate of completion from Vskills
- Get Lifelong e-learning access
Certified Data Mining and Warehousing Professional at VSkills Course details
- For Data Scientists
- For Data Mining professionals
- 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)
- Data mining is the process of extracting patterns from large sets of data by using statistical methods & artificial intelligence with database management
- This certification on Data Mining will teach you to apply algorithms and techniques to solve interesting real-world data mining challenges
Certified Data Mining and Warehousing Professional at VSkills Curriculum
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
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 Application
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 query scalability
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 Breakdown 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 and Risk
Managing Risk
Introduction
Risk Analysis
Risk Management
Risk Analysis
Managing Risks: Internal and 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 and 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
ID
C.
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 P paradigm?
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 OLAP
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 Multi relational 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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