Certified Business Intelligence Professional

Course details

Vskills certification for Business Intelligence assesses the candidate for a company’s data analytics and business intelligence needs. The certification tests the candidates on various areas in business intelligence which includes knowledge of planning, designing, implementing and maintaining the organization’s data warehouse, data mining, data analytics and data intelligence for better decision making.

Why should one take this certification?
The 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 Business Intelligence 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 MIS or analytics 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 Business Intelligence Professional
Business Intelligence professional are in great demand. Companies specializing in Integration Services are constantly hiring knowledgeable professionals. Various banks, telecom and IT companies also need data analytics and data intelligence professionals for data management and analysis.

Table of Contents

Introduction

• Evolution
• Need and benefits
• Technical terms

Integration

• BI life cycle and management systems functions
• ERP and BI
• SCM and BI
• E-commerce and BI

Data Management

• Data Management
• Reporting and Querying
• BI and MDM
• Knowledge Management

OLAP (Online analytical processing)

• Evolution, Features and functions
• Multidimensional analysis
• Data drill-in and drill-up
• OLAP Models (ROLAP and MOLAP) and applications

Dashboards

• EIS
• KPI
• BI Dashboard

Data Warehousing

• Dimensional modeling and metadata
• ETL

Data Analytics

• Concepts and terminologies
• Techniques used (neural network, statistics, fuzzy logic, genetic algorithms

Value Proposition

• Business intelligence economics
• Cost Matrix, SLA and ROI
• Risk Mitigation

Requirement Assessment

• Business problem assessment
• Focusing pertinent information
• Desired outcome specification

Design

• Data and architecture design
• Hardware and Software Selection
• Generate data warehouse matrix
• Dimensional modeling and ETL

Implementation

• Physical Design
• Physical Storage (SAN, RAID, etc.)
• Indexing (B-Tree, Clustered, etc.)
• Data partitioning and clustering for performance
• Analytics criteria selection
• OLAP tools and data slicing or dicing

Post-Implementation

• Security Policy, user privileges and security tools
• Backup and Recovery
• Monitoring and managing data growth

Performance Measurement

• Observing dashboards
• Assessing KPI and scorecard

Advanced BI

• Future Trends
• Case Studies

Updated on 01 February, 2019