Modeling Risk and Realities from Coursera

What's inside

Syllabus

Week 1: Modeling Decisions in Low Uncertainty Settings

This module is designed to teach you how to analyze settings with low levels of uncertainty, and how to identify the best decisions in these settings. You'll explore the optimization toolkit, learn how to build an algebraic model using an advertising example, convert the algebraic model to a spreadsheet model, work with Solver to discover the best possible decision, and examine an example that introduces a simple representation of risk to the model. By the end of this module, you'll be able to build an optimization model, use Solver to uncover the optimal decision based on your data, and begin to adjust your model to account for simple elements of risk. These skills will give you the power to deal with large models as long as the actual uncertainty in the input values is not too high.

Week 2: Risk and Reward: Modeling High Uncertainty Settings

What if uncertainty is the key feature of the setting you are trying to model? In this module, you'll learn how to create models for situations with a large number of variables. You'll examine high uncertainty settings, probability distributions, and risk, common scenarios for multiple random variables, how to incorporate risk reduction, how to calculate and interpret correlation values, and how to use scenarios for optimization, including sensitivity analysis and the efficient frontier. By the end of this module, you'll be able to identify and use common models of future uncertainty to build scenarios that help you optimize your business decisions when you have multiple variables and a higher degree of risk.

Week 3: Choosing Distributions that Fit Your Data

When making business decisions, we often look to the past to make predictions for the future. In this module, you'll examine commonly used distributions of random variables to model the future and make predictions. You'll learn how to create meaningful data visualizations in Excel, how to choose the the right distribution for your data, explore the differences between discrete distributions and continuous distributions, and test your choice of model and your hypothesis for goodness of fit. By the end of this module, you'll be able to represent your data using graphs, choose the best distribution model for your data, and test your model and your hypothesis to see if they are the best fit for your data.

Week 4: Balancing Risk and Reward Using Simulation

This module is designed to help you use simulations to enabling compare different alternatives when continuous distributions are used to describe uncertainty. Through an in-depth examination of the simulation toolkit, you'll learn how to make decisions in high uncertainty settings where random inputs are described by continuous probability distributions. You'll also learn how to run a simulation model, analyze simulation output, and compare alternative decisions to decide on the most optimal solution. By the end of this module, you'll be able to make decisions and manage risk using simulation, and more broadly, to make successful business decisions in an increasing complex and rapidly evolving business world.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for students who are returning to study after a break or who are new to quantitative methods

Appropriate for students with a business background who want to improve their decision-making skills

Provides a solid foundation in quantitative modeling techniques

Taught by experienced instructors with strong academic and industry credentials

Offers practical examples and case studies to illustrate the application of quantitative modeling

Requires basic mathematical skills and a willingness to engage with technical concepts

Reviews summary

Informative risk modeling

Learners say this modeling risks course is highly informative and insightful for beginners and experienced learners alike. Learners walk away satisfied from the real-life examples and materials provided to them, helping them to build a foundation in modeling risks and uncertainties. Despite some claims of instructors lacking descriptiveness, many learners say Professors Savin and Veeraraghavan are clear, articulate, and organized, making difficult topics easy to follow. Although some learners mention repetition throughout the course, many say it's well-structured and provides a solid introduction to the concepts of risk and financial modeling.

Suitable for both beginners and experienced learners.

"This course is wonderful and highly recommended for every student who wants to develop skill in mathematical modelling."

"Learned some valuable concepts about modeling risk and uncertainty. "

Delivers a solid foundation in risk and financial modeling concepts.

"This course really broaden your mind about using Excel simulations to examine real life problems"

"Its a good course to start from begining and think beyond what can really be modelled and analysed in the real world, and to what extent."

Course provides valuable real-world examples and case studies to enhance learning.

"Amazing clarity of thought and flawless explanation. Thanks to both the professors for giving such valuable insight."

"This surely was the best course, amongst the last 3. The instructors were great, went in-depth and the tests also had some standard."

Top-notch instructors who deliver valuable concepts in a clear and well-organized manner.

"Both Lecturers(professor), were very Professional, Yet I have to Admit the Excel skills that are required in these Modules, its still make me learn even further."

Some learners find the course repetitive in parts.

"Some of the course repeated other courses in the Business and Financial Modeling Specialization (and they sometimes repeated themselves within the course), but overall I found it very well-organized."

"The course is good. The explanations and material presented by Prof. Savin are excellent. Unfortunately, I can't say the same for the material and the explanations provided for the sessions conducted by Prof. Veeraraghavan."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Modeling Risk and Realities with these activities:

Think Bayes

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Enhance your understanding of probability and Bayesian inference by reviewing the book Think Bayes.

View Probably Overthinking It: How to Use Data to... on Amazon

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Read the book and take notes
Work through the exercises in the book
Discuss the book with others

Monte Carlo Simulation Tutorial

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Expand your understanding of uncertainty modeling by following a tutorial on Monte Carlo simulation.

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Learn the basics of Monte Carlo simulation
Follow a step-by-step tutorial to build a Monte Carlo simulation model
Run the simulation model and analyze the results

Data Visualization with Excel

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Develop your data visualization skills by creating charts and graphs in Excel to represent complex data.

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Learn about different chart types in Excel
Create a chart from a data set
Customize the appearance of a chart
Interpret data from a chart

Six other activities

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Excel Solver Practice

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Reinforce your understanding of optimization techniques by using Excel Solver to solve linear programming problems.

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Review the Excel Solver documentation
Create an Excel model for a simple optimization problem
Use Solver to solve the optimization problem
Analyze the results of the optimization

Peer Review of Modeling Assumptions

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Gain feedback on your modeling assumptions by sharing your work with peers for review.

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Create a model and document your assumptions
Share your model with peers
Receive feedback and revise your model

Risk Assessment Exercises

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Improve your risk assessment skills by completing a series of exercises.

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Identify hazards and assess their risks
Develop risk mitigation strategies
Evaluate the effectiveness of risk mitigation strategies

Data Modeling Workshop

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Deepen your understanding of data modeling by attending a workshop.

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Register for a data modeling workshop
Attend the workshop and participate in exercises
Apply what you learned to a real-world data modeling project

Risk Management Case Study

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Apply your knowledge of risk management by developing a case study that analyzes a real-world scenario.

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Identify a business scenario with risk factors
Analyze the risks associated with the scenario
Develop a risk management plan
Evaluate the effectiveness of the risk management plan

Contribute to an Open-Source Decision-Making Project

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Apply your knowledge of decision-making theory by contributing to an open-source project.

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Find an open-source project related to decision-making
Identify an area where you can contribute
Make a contribution to the project
Review the feedback on your contribution

Career center

Learners who complete Modeling Risk and Realities will develop knowledge and skills that may be useful to these careers:

Risk Manager

Risk managers help companies identify and manage risks. They use a variety of quantitative and qualitative techniques to assess risks and develop mitigation strategies. This course would help someone interested in becoming a risk manager by teaching them how to build quantitative models and incorporate risk and uncertainty into their models. It would also give them a foundation in the measurement and management of risk.

See salaries and explore the career path for Risk Manager

Insurance Actuary

Insurance actuaries use mathematical and statistical models to assess the risks of insurance policies. They develop pricing strategies and make recommendations on policy coverage. This course would help someone interested in becoming an insurance actuary by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Insurance Actuary

Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data. They develop trading strategies and make investment recommendations. This course would help someone interested in becoming a quantitative analyst by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data scientists use mathematical and statistical models to analyze large datasets. They develop algorithms to identify patterns and trends, and they build models to predict future outcomes. This course would help someone interested in becoming a data scientist by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

Operations research analysts use mathematical and statistical models to solve business problems. They develop strategies to improve efficiency and productivity. This course would help someone interested in becoming an operations research analyst by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. They develop models to predict future outcomes and make recommendations on how to improve processes. This course would help someone interested in becoming a statistician by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine learning engineers use mathematical and statistical models to develop machine learning algorithms. These algorithms can be used to identify patterns and trends in data, and to make predictions about future outcomes. This course would help someone interested in becoming a machine learning engineer by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

See salaries and explore the career path for Machine Learning Engineer

Market Researcher

Market researchers use mathematical and statistical models to analyze market data. They develop strategies to improve marketing campaigns and increase sales. This course would help someone interested in becoming a market researcher by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

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Economist

Economists use mathematical and statistical models to analyze economic data. They develop theories to explain economic behavior and make predictions about future economic trends. This course would help someone interested in becoming an economist by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

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Business Analyst

Business analysts use mathematical and statistical models to analyze business data. They develop strategies to improve business processes and increase profits. This course would help someone interested in becoming a business analyst by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

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Data Analyst

Data analysts help companies make informed decisions by analyzing data. They use a variety of statistical techniques to identify trends and patterns in data, and they build models to predict future outcomes. This course would help someone interested in becoming a data analyst by teaching them how to build quantitative models and incorporate risk and uncertainty into their models. It would also give them a foundation in the measurement and management of risk.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial analysts help companies understand their financial situation. They build financial models to estimate a company's worth and predict its future cash flows. Many financial analysts use specialized software to build these models. This course would help someone interested in becoming a financial analyst by teaching them how to build quantitative models and incorporate risk and uncertainty into their models. It would also give them a foundation in the measurement and management of risk.

See salaries and explore the career path for Financial Analyst

Financial Advisor

Financial advisors help individuals and businesses manage their finances. They develop investment plans and make recommendations on how to save for retirement, buy a home, or start a business. This course would help someone interested in becoming a financial advisor by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

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Software Engineer

Software engineers design, develop, and maintain software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course would help someone interested in becoming a software engineer by teaching them how to build quantitative models and incorporate risk and uncertainty into their models.

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Management Consultant

Management consultants help companies improve their performance. They use a variety of analytical techniques to identify problems and develop solutions. This course would help someone interested in becoming a management consultant by teaching them how to build quantitative models and incorporate risk and uncertainty into their models. It would also give them a foundation in the measurement and management of risk.

See salaries and explore the career path for Management Consultant