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  • BUS250: Introduction to Business Intelligence and Analytics
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    COURSE INTRODUCTION
    Course Syllabus
    Unit 1: Business Intelligence and Its Role in Organizations
    1.1: Fundamentals of Business Intelligence (BI)
    BI in Modern Organizations
    Evolution of BI Strategies
    Data Sources, Analytics, and Decision Support Systems
    1.2: Practical Applications of BI
    Supporting Decision-Making with BI
    Successful BI Implementations
    BI and Competitive Advantage
    1.3: Fundamentals of Data Management
    BI Systems Architecture
    Data Warehousing and Modeling
    Managing Diverse Data Sources
    1.4: BI Concepts in Action
    Popular BI Tools and Technologies
    Key Performance Indicators (KPIs)
    Unit 1 Assessment
    Unit 1 Assessment
    Unit 2: Sources of Data in BI Systems
    2.1: Selecting Data to Support Business Decision-Making
    Types of Data Sources
    Classifying Data Sources by Structure
    2.2: Evaluating Data Quality and Relevance
    Quality, Accuracy, Completeness, and Timeliness
    Evaluating the Relevance of Data
    Implications of Poor Data Quality
    2.3: Effective Data Integration Strategies and Technologies
    Batch Processing, Real-Time Integration, and Data Virtualization
    Data Integration Technologies and Tools
    Trade-Offs of Integration Approaches
    2.4: Big Data Models and NoSQL Sources
    Role of Big Data Sources in BI Systems
    NoSQL Databases
    Using Technology to Enhance BI Capabilities
    Unit 2 Assessment
    Unit 2 Assessment
    Unit 3: Data Management and Data Warehousing
    3.1: Data Management Principles
    Foundations of Data Management
    Data Quality and Accuracy
    Data Governance Frameworks and Best Practices
    3.2: Data Warehousing Concepts
    Data Warehouses as Centralized Repositories for Data
    Architecture and Components of Data Warehouses
    Designing and Implementing Data Warehouses
    3.3: Data Modeling Techniques
    Data Modeling Techniques
    Dimensional Modeling
    3.4: Integrating Data Management and Data Warehousing
    Integrating Data Management and Data Warehousing
    Selecting and Preparing Data
    Unit 3 Assessment
    Unit 3 Assessment
    Unit 4: Data Analysis and Interpretation
    4.1: Data Analysis Techniques
    Data Analysis Techniques
    Statistical Algorithms and Exploratory Data Analysis
    Data Analysis Tools
    4.2: Interpreting Data Analyses
    Interpreting Analytical Results
    Context and Domain Knowledge
    4.3: The Data Mining Process
    Data Mining Techniques
    Knowledge Discovery
    Text Mining and the Complications of Language
    Exploring Text Data
    Unit 4 Assessment
    Unit 4 Assessment
    Unit 5: Data Visualization and Reporting
    5.1: Data Visualization Techniques
    Communicating Analytical Results
    Visualization Tools and Techniques
    Creating Useful Dashboards and Reports
    Principles of Effective Data Visualization
    5.2 Creating Common Data Visualizations
    Introduction to Tableau
    Charts, Heatmaps, and Treemaps
    Waterfall and Bubble Charts
    More Visualization Techniques
    Word Clouds and Text Tables
    Storyboards and Sentiment Analysis
    Unit 5 Assessment
    Unit 5 Assessment
    Unit 6: Data Analytics
    6.1: Describing and Analyzing Data
    Overview of Data Analysis
    Analytic Techniques
    6.2: Validity, Effectiveness, and Accuracy
    Effectiveness and Accuracy of Analytical Models
    Model Validation
    Refining and Optimizing Analytical Models
    6.3: Descriptive, Predictive, and Cluster Analytics
    Predictive Techniques, Diagnostic Analytics, and Cluster Analysis
    Data Mining in Analytic Systems
    Unit 6 Assessment
    Unit 6 Assessment
    Unit 7: Business Intelligence Tools
    7.1: Data Analysis Techniques and BI
    Descriptive Statistics
    Inferential Statistics
    Inferential versus Descriptive Statistics
    Hypothesis Testing
    7.2: Statistical Software and Programming Languages
    Introduction to R
    Introduction to Python
    7.3: Strengths and Limitations of Analytical Approaches
    Strengths and Limitations of Systems
    Systems for Mobile Development
    Unit 7 Assessment
    Unit 7 Assessment
    Unit 8: Legal and Ethical Considerations
    8.1: Legal Frameworks for BI
    Legal Foundations Governing Data and Security in BI
    International, National, and Industry-Specific Regulations
    Implications of Non-compliance
    8.2: Ethics and BI
    Ethical Challenges in BI
    Ethical Considerations in Algorithms and Models
    8.3: BI and Privacy
    Privacy Concerns
    Balancing BI Insights and Privacy Rights
    Anonymization and Pseudonymization
    8.4: Corporate Culture and Governance BI Practices
    Promoting Ethical Behavior
    The Role of Corporate Culture
    Strategies for Ethical Decision-Making
    Unit 8 Assessment
    Unit 8 Assessment
    Study Guide
    BUS250 Study Guide
    Course Feedback Survey
    Course Feedback Survey
    Certificate Final Exam
    BUS250: Certificate Final Exam
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Saylor Academy © 2010-2025 except as otherwise noted. Excluding course final exams, content authored by Saylor Academy is available under a Creative Commons Attribution 3.0 Unported license. Third-party materials are the copyright of their respective owners and shared under various licenses. See detailed licensing information. Saylor Academy®, Saylor.org®, and Harnessing Technology to Make Education Free® are trade names of the Constitution Foundation, a 501(c)(3) organization through which our educational activities are conducted.