Model Validation: 1.1 Why mathematical modelling? | Saylor Academy

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Read this material on mathematical modeling. Try to complete the exercise at the end of the chapters. Don't worry if you have not yet learned the math necessary to complete the exercises. Rather, notice how many physical systems can be represented by mathematical models. We can then implement these models using programming languages like Python and R. We will discuss those later. Think about the types of systems you interact with, personally or in business situations. How might you use a mathematical model to help you understand this system?

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