Business Intelligence And Analytics - Notes, Syllabus & Materials | YNetStudyHub
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Business Intelligence And Analytics

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Topics & Syllabus 38

#1 Introduction to Business Intelligence and Analytics
This topic will cover the fundamental concepts of business intelligence and analytics...
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#2 Data Warehousing and Data Modeling
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#3 Data Extraction, Transformation, and Loading (ETL)
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#4 Data Visualization and Reporting
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#5 Predictive Analytics and Machine Learning
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#6 Business Intelligence Tools and Technologies
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#7 Implementing Business Intelligence Solutions
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#8 Data Governance and Security in BI
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#9 Introduction to Business Intelligence and Analytics
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#10 Data Warehousing and Data Modeling
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#11 Data Extraction, Transformation, and Loading (ETL)
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#12 Data Visualization and Dashboard Design
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#13 Predictive Analytics and Forecasting
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#14 Machine Learning for Business Intelligence
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#15 Big Data Analytics
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#16 Business Intelligence Tools and Platforms
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#17 Ethical and Legal Considerations in Business Intelligence
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#18 Implementing Business Intelligence Solutions
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#19 Introduction to Business Intelligence and Analytics
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#20 Data Collection and Integration
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#21 Data Warehousing and Data Mining
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#22 Business Intelligence Tools and Technologies
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#23 Predictive Analytics and Forecasting
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#24 Performance Management and KPIs
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#25 Data Visualization and Dashboards
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#26 Big Data Analytics
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#27 Data Governance and Ethics
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#28 Business Intelligence Implementation and Strategy
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#29 Introduction to Business Intelligence and Analytics
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#30 Data Warehousing and ETL Processes
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#31 Data Visualization and Reporting
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#32 Business Performance Management
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#33 Predictive Analytics and Data Mining
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#34 Big Data Analytics
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#35 Business Intelligence Tools and Platforms
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#36 Data Governance and Ethics
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#37 Business Intelligence Implementation Strategies
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#38 Business Intelligence Trends and Future Developments
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Unit Outline 60h

Learning Objectives

5 objectives
  • Understand fundamental concepts and importance of Business Intelligence (BI) and Analytics in organizational decision-making.
  • Gain knowledge of data warehousing, data modeling, and ETL processes for effective data management.
  • Develop skills in data visualization, reporting, and the use of popular BI tools and platforms.
  • Explore predictive analytics, machine learning, and big data analytics techniques for forecasting and insight generation.
  • Learn best practices in BI implementation, data governance, security, and ethical considerations.

Content Outline

Preview

1. Introduction to Business Intelligence and Analytics

  • Definition and key concepts
  • Importance of data-driven decision-making
  • Role of BI and Analytics in modern organizations
  • Applications and benefits across industries

2. Data Collection, Integration, and Quality

  • Methods of data collection
  • Integrating data from multiple sources
  • Ensuring data quality and consistency

3. Data Warehousing and Data Modeling

  • Purpose and architecture of data warehouses
  • Data warehousing vs operational databases
  • Data modeling techniques: conceptual, logical, and physical models
  • Centralized data repository benefits

4. Data Extraction, Transformation, and Loading (ETL)

  • Overview of ETL process
  • Data extraction from heterogeneous sources
  • Data transformation techniques and cleaning
  • Loading data into data warehouses
  • ETL best practices and common tools

5. Data Mining and Predictive Analytics

  • Concepts of data mining and knowledge discovery
  • Predictive analytics overview
  • Regression analysis
  • Time series forecasting
  • Introduction to machine learning algorithms: clustering, classification, anomaly detection

6. Big Data Analytics

  • Understanding big data characteristics (volume, velocity, variety, veracity)
  • Challenges and opportunities of big data
  • Technologies: Hadoop ecosystem, Spark
  • Analytics methods for large-scale structured and unstructured data

7. Data Visualization and Reporting

  • Importance of data visualization in BI
  • Visualization techniques and principles
  • Designing effective dashboards and reports
  • Tools overview: Tableau, Power BI, QlikView
  • Best practices for interactive and informative visualizations

8. Business Intelligence Tools and Platforms

  • Survey of popular BI tools: Tableau, Power BI, QlikView, SAS
  • Features, capabilities, and industry use cases
  • Criteria for selecting BI tools

9. Business Performance Management and KPIs

  • Defining Key Performance Indicators (KPIs)
  • Monitoring and analyzing organizational performance
  • Role of BI in performance management

10. Data Governance, Security, and Ethics

  • Importance of data governance in BI
  • Ensuring data quality, integrity, and privacy
  • Compliance with regulations: GDPR, HIPAA
  • Ethical considerations in data usage and analysis

11. Implementing Business Intelligence Solutions

  • Planning and designing BI solutions
  • Development and deployment processes
  • Data integration and user training
  • Maintenance, support, and continuous improvement

12. Business Intelligence Strategy and Future Trends

  • Aligning BI initiatives with organizational goals
  • Measuring BI impact on business performance
  • Emerging trends and technologies in BI and analytics
  • The future of BI: AI integration, augmented analytics, and automation
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Parent Course
Bachelor of Science in Business Information Technology

This unit is an integral part of the Bachelor of Science in Business Information Technology syllabus.

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