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Optimization for Decision Making

4.8 Filled star Filled star Filled star Filled star Half star
Based on 68 ratings
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Soumya Sen

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.

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What's inside

Syllabus

Module 1: Introduction to Linear Programming
Prescriptive analytics is a part of business analytics that is aimed at prescribing solutions to decision problems. The most important modeling technique within prescriptive analytics is optimization. In this module, we will learn how to recognize contexts where it can be applied and get introduced to the basics of linear optimization.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Designed for learners who are looking to enter the field of business analytics, as it will teach skills that are core to entry-level positions
Assumes that learners have little to no knowledge in data analytics or the concepts of prescribed analytics, making the course a suitable starting point
Led by Soumya Sen, a subject-matter expert with years of experience in applied analytics
Emphasizes the practical application of linear optimization models, providing learners with immediately usable skills
Mainly focuses on the concept of linear optimization, limiting its scope in terms of prescriptive analytics techniques
Relies on Excel Solver for problem-solving, which may not be the most effective tool for complex optimization problems

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Reviews summary

Practical linear optimization with excel

Learners say this course offers a fantastic introduction to optimization, particularly highlighting its strong emphasis on practical application for decision-making. Students found the instructor's explanations exceptionally clear, making complex concepts accessible. The course's focus on Excel Solver is consistently praised for providing immediate utility in business contexts. While it serves as a solid foundational course, some more advanced learners found the scope somewhat limited, wishing for deeper coverage of advanced topics or more challenging assignments.
Slight discrepancies in Excel interface, but generally manageable.
"I noticed some slight discrepancies with the Excel interface presented in the videos compared to the newest Excel version, but it was easy enough to figure out."
"Though the course is well-made, I found some of the tools shown were slightly outdated compared to current software versions."
"I didn't encounter major issues, but I had to adjust slightly for minor Excel version differences."
Provides a strong, accessible introduction for new learners.
"A solid foundational course. It truly demystified linear programming for me."
"Good course for beginners in optimization. It explains linear programming and formulation very well."
"Very clear and concise. It does exactly what it promises – introduces linear optimization for decision making using Excel Solver."
Complex concepts are explained in an understandable way.
"The instructor explained complex concepts like formulating objective functions and constraints in a very clear and understandable way."
"Exceptional course for anyone needing to apply optimization in a business context. The instructor's ability to simplify complex topics was outstanding."
"I especially appreciated the clear distinction between variables, objectives, and constraints. Excel Solver is taught brilliantly."
Focus on Excel Solver makes optimization immediately applicable.
"The practical examples using Excel Solver were incredibly helpful. I feel much more confident applying these principles in my work now."
"The Excel Solver demos were exceptionally well-done. It's a great course if you want to apply optimization without getting bogged down in heavy mathematics."
"I learned a lot about how to define decision variables and constraints. The Excel Solver part is very practical."
Some quizzes/assignments could be more challenging or diverse.
"My only minor critique is that some of the quiz questions felt a bit too straightforward. Could use slightly more challenging problems."
"I wish there were more diverse assignments to really test understanding beyond simple models."
"It provides a decent introduction, but I found the assignments somewhat lacking in complexity."
May be too basic for advanced learners seeking deeper topics.
"The course covers the basics well, but as someone with a strong analytics background, I found the pace a bit slow and the content somewhat introductory."
"I was hoping for more advanced topics like integer programming or non-linear optimization. Good for true beginners, though."
"Honestly, I found the course content a bit too simplistic for my needs. I was looking for something that would challenge me more and cover a wider range of optimization techniques."

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 Optimization for Decision Making with these activities:
Identify Mentors in the Field of Optimization
Connecting with experienced professionals will provide you with valuable insights and guidance throughout your learning journey.
Browse courses on Optimization
Show steps
  • Attend industry events or conferences related to optimization.
  • Reach out to professors or researchers in the field.
  • Connect with professionals on LinkedIn or other networking platforms.
Review Basic Algebra and Calculus Concepts
Refreshing your understanding of these concepts will provide a stronger foundation for comprehending linear optimization.
Browse courses on Algebra
Show steps
  • Review notes or online resources on basic algebra topics such as linear equations and inequalities.
  • Practice solving simple algebra problems.
  • Review basic calculus concepts such as derivatives and integrals.
Compile a Glossary of Linear Optimization Terms
Creating a glossary will provide you with a quick reference for understanding and using linear optimization terminology.
Browse courses on Optimization
Show steps
  • Gather a list of key terms related to linear optimization.
  • Define each term clearly and concisely.
  • Organize the glossary alphabetically or by category.
  • Review the glossary regularly to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Tutorials on Linear Optimization Applications
Exploring how linear optimization is used in practical applications will broaden your understanding of its real-world relevance.
Browse courses on Optimization
Show steps
  • Search for online tutorials or courses on linear optimization applications.
  • Choose a specific domain or industry, such as manufacturing, finance, or logistics.
  • Follow the tutorials to learn how linear optimization is used to solve problems in that domain.
  • Take notes and document the key concepts and techniques used.
Join a Study Group or Online Forum for Linear Optimization
Engaging with peers will provide opportunities for discussion, idea sharing, and collaborative problem-solving.
Browse courses on Optimization
Show steps
  • Find or create a study group with classmates or online peers.
  • Meet regularly to discuss concepts, work on problems, and share insights.
  • Participate in online forums or discussion boards dedicated to linear optimization.
Practice Excel Solver
Applying what you've learned to solve optimization problems will reinforce your understanding of the concepts.
Browse courses on Excel Solver
Show steps
  • Download the Excel Solver add-in if you haven't already.
  • Load an example optimization problem into Excel.
  • Set up the decision variables, objective function, and constraints in Excel Solver.
  • Use Excel Solver to solve the problem and find the optimal solution.
  • Repeat these steps for various optimization problems to gain proficiency.
Develop a Linear Optimization Model for a Business Problem
Applying linear optimization to a real-world problem will deepen your understanding and demonstrate your ability to use it effectively.
Browse courses on Optimization
Show steps
  • Identify a business problem that can be addressed using linear optimization.
  • Gather data and define the decision variables, objective function, and constraints.
  • Formulate the optimization problem using a linear programming solver, such as Excel Solver or Gurobi.
  • Solve the problem to obtain the optimal solution.
  • Analyze the results and write a report summarizing your findings and recommendations.

Career center

Learners who complete Optimization for Decision Making will develop knowledge and skills that may be useful to these careers:
Operations Research Analyst
An Operations Research Analyst applies analytical methods to help organizations make better decisions. This course, Optimization for Decision Making, can be a valuable tool for Operations Research Analysts, as it provides a strong foundation in the principles of linear optimization. This course can help Operations Research Analysts develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as manufacturing, logistics, and healthcare.
See salaries and explore the career path for Operations Research Analyst
Data Scientist
A Data Scientist uses data to solve problems and improve decision-making. This course, Optimization for Decision Making, can be a valuable tool for Data Scientists, as it provides a strong foundation in the principles of linear optimization. This course can help Data Scientists develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as marketing, finance, and healthcare.
See salaries and explore the career path for Data Scientist
Business Analyst
A Business Analyst uses data and analysis to help businesses make better decisions. This course, Optimization for Decision Making, can be a valuable tool for Business Analysts, as it provides a strong foundation in the principles of linear optimization. This course can help Business Analysts develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as product development, marketing, and finance.
See salaries and explore the career path for Business Analyst
Management Consultant
A Management Consultant helps organizations improve their performance. This course, Optimization for Decision Making, can be a valuable tool for Management Consultants, as it provides a strong foundation in the principles of linear optimization. This course can help Management Consultants develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as operations, finance, and marketing.
See salaries and explore the career path for Management Consultant
Financial Analyst
A Financial Analyst uses data to evaluate investments and make recommendations. This course, Optimization for Decision Making, can be a valuable tool for Financial Analysts, as it provides a strong foundation in the principles of linear optimization. This course can help Financial Analysts develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as portfolio management, investment banking, and corporate finance.
See salaries and explore the career path for Financial Analyst
Operations Manager
An Operations Manager plans and oversees the day-to-day operations of an organization. This course, Optimization for Decision Making, can be a valuable tool for Operations Managers, as it provides a strong foundation in the principles of linear optimization. This course can help Operations Managers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as manufacturing, logistics, and healthcare.
See salaries and explore the career path for Operations Manager
Industrial Engineer
An Industrial Engineer designs and improves systems and processes. This course, Optimization for Decision Making, can be a valuable tool for Industrial Engineers, as it provides a strong foundation in the principles of linear optimization. This course can help Industrial Engineers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as manufacturing, logistics, and healthcare.
See salaries and explore the career path for Industrial Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course, Optimization for Decision Making, may be helpful for Software Engineers, as it provides a foundation in the principles of linear optimization. This course can help Software Engineers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as software design, development, and maintenance.
See salaries and explore the career path for Software Engineer
Product Manager
A Product Manager plans and manages the development and launch of new products. This course, Optimization for Decision Making, may be helpful for Product Managers, as it provides a foundation in the principles of linear optimization. This course can help Product Managers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as product planning, development, and launch.
See salaries and explore the career path for Product Manager
Marketing Manager
A Marketing Manager plans and executes marketing campaigns. This course, Optimization for Decision Making, may be helpful for Marketing Managers, as it provides a foundation in the principles of linear optimization. This course can help Marketing Managers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as marketing planning, execution, and measurement.
See salaries and explore the career path for Marketing Manager
Financial Manager
A Financial Manager plans and manages the financial resources of an organization. This course, Optimization for Decision Making, may be helpful for Financial Managers, as it provides a foundation in the principles of linear optimization. This course can help Financial Managers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as financial planning, budgeting, and investment.
See salaries and explore the career path for Financial Manager
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course, Optimization for Decision Making, may be helpful for Actuaries, as it provides a foundation in the principles of linear optimization. This course can help Actuaries develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as insurance, pensions, and investments.
See salaries and explore the career path for Actuary
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course, Optimization for Decision Making, may be helpful for Economists, as it provides a foundation in the principles of linear optimization. This course can help Economists develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as economic policy, forecasting, and analysis.
See salaries and explore the career path for Economist
Statistician
A Statistician collects, analyzes, and interprets data. This course, Optimization for Decision Making, may be helpful for Statisticians, as it provides a foundation in the principles of linear optimization. This course can help Statisticians develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as data analysis, forecasting, and modeling.
See salaries and explore the career path for Statistician
Market Researcher
A Market Researcher studies the market for a product or service. This course, Optimization for Decision Making, may be helpful for Market Researchers, as it provides a foundation in the principles of linear optimization. This course can help Market Researchers develop the skills they need to identify decision variables, objective functions, and constraints, and use them to formulate and solve optimization problems. This can be essential for making better decisions in a variety of settings, such as market research, analysis, and forecasting.
See salaries and explore the career path for Market Researcher

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Optimization for Decision Making.
Cover image
Linear Programming and Network Flows
Save
Provides a comprehensive overview of linear programming and network flows. It classic textbook that is widely used in academic institutions and by industry professionals.
Linear Programming and Network Flows
Hardcover
$$$
Linear Programming and Network Flows
Hardcover
$$
Linear Programming and Network Flows
Hardcover
$$
Linear Programming and Network Flows
Kindle Edition
$$$$
Cover image
Optimization for Decision Making
Save
Provides a practical introduction to optimization for decision making. It covers a wide range of topics, including linear programming, integer programming, and nonlinear programming.
Optimization for Decision Making: Linear and...
Hardcover
$$$$
Optimization for Decision Making: Linear and...
Kindle Edition
$$$$
Cover image
Introduction to Applied Linear Algebra
Save
Provides a comprehensive overview of convex optimization. It covers a wide range of topics, including linear programming, semidefinite programming, and conic programming.
Introduction to Applied Linear Algebra: Vectors,...
Hardcover
$$
Introduction to Applied Linear Algebra: Vectors,...
Kindle Edition
$$
Cover image
A Course in Reinforcement Learning
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Provides a comprehensive overview of nonlinear programming. It covers a wide range of topics, including unconstrained optimization, constrained optimization, and dynamic programming.
A Course on Reinforcement Learning
Hardcover
$$$
Cover image
Integer and Combinatorial Optimization
Save
Provides a comprehensive overview of network optimization. It covers a wide range of topics, including network flows, minimum cost flows, and maximum flows.
Integer and Combinatorial Optimization
Paperback
$$$$
Integer and Combinatorial Optimization (Wiley...
Kindle Edition
$$$$
Cover image
Introduction to Stochastic Programming
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Provides a comprehensive overview of stochastic optimization. It covers a wide range of topics, including Monte Carlo simulation, dynamic programming, and robust optimization.
Introduction to Stochastic Programming (Springer...
Paperback
$$$
Introduction to Stochastic Programming (Springer...
Kindle Edition
$$$
Cover image
23 European Symposium on Computer Aided Process...
Save
Provides a comprehensive overview of multi-objective optimization. It covers a wide range of topics, including evolutionary algorithms, genetic algorithms, and particle swarm optimization.
23 European Symposium on Computer Aided Process...
Kindle Edition
$
Cover image
Modern Mathematics for the Engineer: Second Series
Save
Provides a comprehensive overview of optimization theory and methods. It covers a wide range of topics, including unconstrained optimization, constrained optimization, and dynamic programming.
Modern Mathematics for the Engineer: Second Series...
Paperback
$$
Modern Mathematics for the Engineer: Second Series...
Kindle Edition
$$

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Rating
4.8 Filled star Filled star Filled star Filled star Half star
Effort
13 hours to complete
Level
Introductory
Via
Coursera
Institution
University of Minnesota
Instructor
Soumya Sen
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша
This course belongs to a collection called Analytics for Decision Making. It's comprised of four courses:
  1. Optimization for Decision Making (viewing)
  2. Introduction to Predictive Modeling
  3. Simulation Models for Decision Making
  4. Advanced Models for Decision Making
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Designed for learners who are looking to enter the field of business analytics, as it will teach skills that are core to entry-level positions
Assumes that learners have little to no knowledge in data analytics or the concepts of prescribed analytics, making the course a suitable starting point
Led by Soumya Sen, a subject-matter expert with years of experience in applied analytics
Emphasizes the practical application of linear optimization models, providing learners with immediately usable skills
Mainly focuses on the concept of linear optimization, limiting its scope in terms of prescriptive analytics techniques
Relies on Excel Solver for problem-solving, which may not be the most effective tool for complex optimization problems
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