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Quantitative
Analysis
Make decisions backed by data. Probability, statistics, and modeling techniques for business problems that resist gut-feel answers.
What You'll Learn
A full semester of quantitative analysis — completed at your own pace, with no deadlines or fixed class times.
What You'll Be Able to Do
- Acquire essential knowledge and skills for strategic decision-making and risk analysis in the business field, emphasizing the use of quantitative methods
- Develop proficiency in key quantitative tools, including decision trees, payback analysis, and simulations, for effective managerial decision-making
- Gain a comprehensive understanding of the quantitative approach to decision-making, focusing on its advantages, objectives, and application under conditions of certainty, risk, and uncertainty
- Master business risk management through in-depth analysis and the application of risk management strategies, integrating concepts like probability distributions and Monte Carlo simulation
- Prepare for data-driven, strategic decision-making, equipping students to navigate the challenges of today's dynamic business environment
Major Course Topics
Course chapters and lessons
Quantitative Analysis has 13 chapters across 34 instructional hours, as listed in the course syllabus. Expand any chapter to see the lessons inside it.
- Chapters
- 13
- Lessons
- 55
- Instructional hours
- 34 hrs
- Semester hours
- 3
Chapter 1
Strategic Management and Organizational Decision-Making
- 1.1 Thinking Ahead: How the Strategic Management Process Guides Organizational Direction
- 1.2 How Organizations Make Choices: Structures and Procedures Behind Organizational Decision-Making
- 1.3 A Toolkit for Uncertainty: Applying Tree Analysis, Payback Methods, and Simulation to Business Choices
- 1.4 Crunching Numbers for Better Outcomes: The Advantages of a Quantitative Decision-Making Approach
Chapter 2
Decision Models, Risk, and Uncertainty
- 2.1 Choosing the Right Framework: Types and Development of Decision-Making Models
- 2.2 Decision-Making Under Certainty and Risk: Structured Approaches When Data Is Available
- 2.3 Decision-Making Under Uncertainty: Strategies When Outcomes and Probabilities Are Unknown
- 2.4 Knowing What Could Go Wrong: Business Risk Analysis and the Goals of Risk Management
Chapter 3
Simulation in Business Analysis
- 3.1 Running the Scenario Before It Happens: Simulation as a Tool for Business Problem-Solving
- 3.2 Building Simulation Models: Random Numbers, Probability Distributions, and the Difference Between Static and Dynamic Approaches
- 3.3 Rolling the Dice Deliberately: The Scope and Business Applications of Monte Carlo Simulation
Chapter 4
Inventory Management and Queuing Theory
- 4.1 What Goes on the Shelf: Understanding the Components of Merchandise Inventory
- 4.2 The True Cost of Keeping Stock: Definitions, Methods, and Types of Inventory Costs
- 4.3 Supply Meets Demand: Balancing Inventory Needs Against Cost Constraints
- 4.4 Keeping Track of What You Have: Types and Objectives of Inventory Control Systems
- 4.5 Recording What Comes In: How Inventory Purchases Are Accounted For
- 4.6 Downstream Consequences: Why Inventory Choices Reverberate Across the Entire Supply Chain
- 4.7 Nobody Likes to Wait: Understanding Where Queuing Problems Arise and What They Cost Businesses
- 4.8 Queue Design and Economics: How Waiting-Line Theory Helps Businesses Build Efficient Systems and Manage Costs
Chapter 5
Decision Analysis: Frameworks and Tools
- 5.1 Making Decisions on Purpose: The Discipline and Structure of Business Decision Analysis
- 5.2 Data That Informs Choices: Using Probability and Economic Metrics in Decision-Making
- 5.3 Choosing in the Dark: Three Frameworks for Rational Decisions When No Probabilities Exist
- 5.4 Probability in Action: Applying Decision Analysis When Likelihood Data Is Available
- 5.5 Branches and Boxes: How Decision Trees and Payoff Tables Clarify Complex Business Choices
- 5.6 What Is Certainty Worth? Calculating the Expected Value of Perfect Information
Chapter 6
Decision Analysis: Bayesian Methods, Strategy, and Utility
- 6.1 Revising the Estimate: How New Evidence and Updated Probabilities Sharpen Business Decisions
- 6.2 Playing It Smart: Understanding and Applying Business Decision Strategies
- 6.3 Reasoning Backward From Evidence: Bayes' Theorem Defined, Derived, and Applied
- 6.4 Is More Data Worth It? Evaluating the Expected Value of Sample Information
- 6.5 Measuring More Than Money: Applying Utility Theory to Complex Business Choices
Chapter 7
Project Planning, Scheduling, and Cost Management
- 7.1 Mapping the Work: Using PERT to Analyze Projects and Plan for Completion
- 7.2 A Visual Timeline for Every Project: The Legacy of Henry Gantt and the Power of Bar Charts
- 7.3 From Vision to Execution: The Phases, Steps, and Deliverables of Project Planning
- 7.4 Assigning Responsibility: How Project Teams Are Structured and Work Is Broken Down
Chapter 8
Project Risk, Critical Path, and Global Complexity
- 8.1 Sources of Uncertainty in Project Timelines: Why Activity Durations Are Rarely Fixed
- 8.2 Estimating Under Uncertainty: Statistical Methods for Predicting Project Completion Times
- 8.3 Finding the Bottleneck: How the Critical Path Method Identifies What Cannot Be Delayed
- 8.4 Every Expense Counted: Distinguishing Direct and Indirect Costs in Project Budgeting
- 8.5 Across Cultures and Borders: Addressing Diversity and Global Complexity in Project Teams
Chapter 9
Linear Programming: Modeling and Solving
- 9.1 Constraints as Equations: Constructing Linear Programming Models From Real Business Problems
- 9.2 Indivisible by Design: Using Integer Programming to Solve Budgeting and Distribution Challenges
- 9.3 Solving for the Best Outcome: Putting Linear Programming to Work on Business Constraints
- 9.4 Where the Optimal Lives: Why Extreme Points Are Central to Linear Programming Solutions
- 9.5 Beyond the Printout: How to Interpret and Act on Linear Programming Computer Results
Chapter 10
Sensitivity Analysis and Solution Robustness
- 10.1 What Sensitivity Analysis Is and Why Every LP Solution Needs It
- 10.2 How Changing Coefficients, Constraints, and Parameters Affect the Optimal Solution
- 10.3 Testing the Boundaries: Sensitivity Analysis and What It Reveals About LP Optimal Solutions
Chapter 11
LP Applications in Marketing and Finance
- 11.1 Where the Budget Goes: Applying LP to Audience Targeting and Marketing Research Allocation
- 11.2 LP in Financial Planning: Optimizing Budgets, Cash Flow, and Resource Allocation
- 11.3 LP in Portfolio Management: Balancing Risk, Return, and Investment Strategy
Chapter 12
Transportation Problems: Models and Methods
- 12.1 Getting Goods Where They Need to Go: The Characteristics, Variants, and Solutions of Transportation Problems
- 12.2 Edge Cases and Exceptions: Tackling Non-Standard Scenarios in Transportation Problem-Solving
- 12.3 Starting Simple and Improving: Solving Transportation Problems from Initial Feasible Solution to Optimal Plan
Chapter 13
Assignment Problems and Optimization
- 13.1 The Assignment Problem: What It Is, Where It Appears, and Why It Matters
- 13.2 Step by Step Through the Hungarian Algorithm: Solving Assignment Problems Optimally
These chapters come straight from the official course syllabus, which also sets out the graded assignment and the proctored final exam.
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Build analytical strength with quantitative analysis.
Make decisions backed by data. Probability, statistics, and modeling techniques for business problems that resist gut-feel answers.
🎯 Why It Matters
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HD video lectures broken into short, focused modules. Watch at your own pace — pause, rewind, replay any time.
Quizzes & Final Exam
Evaluate your understanding after every section with quizzes and a comprehensive final exam that mirrors real college assessment.
Structured College-Level Course
Structured lessons covering every topic — designed as a standard Quantitative Analysis college-level course.
Official Transcript
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