Prescriptive Analytics from edX

Welcome to Thayer School of Engineering at Dartmouth’s Prescriptive Analytics for Digital Transformation. This comprehensive course is designed to equip you with the tools and methodologies needed to transform raw data into actionable strategies for decision-making in complex, real-world scenarios. By the end of this course, you will be able to design and implement optimization models that solve intricate business problems and align with digital transformation initiatives.

This course provides a deep dive into optimization principles and practical applications, beginning with foundational concepts such as decision variables, objective functions, and constraints. You’ll learn to differentiate between linear and non-linear optimization problems, gaining insight into when and how to transform non-linear models into linear ones for more efficient problem-solving. Through hands-on activities and Python-based exercises, you will implement linear optimization models to address challenges like inventory management, resource allocation, and advertising optimization.

As you progress, the course introduces more complex scenarios that require mixed-integer linear optimization. By incorporating integer variables into your models, you’ll unlock the ability to tackle discrete decision-making problems, such as determining warehouse locations, project selection, and resource distribution. These advanced techniques will help you formulate and solve optimization problems that mirror the complexities of modern business environments.

The course also covers practical tools like Pyomo and cloud-based platforms, ensuring you gain scalable, real-world skills. You’ll explore advanced methods such as branch-and-bound for binary integer optimization, enabling efficient solutions for large-scale problems. Applying these techniques to examples like portfolio optimization and logistics planning lets you see how prescriptive analytics drives operational efficiency and strategic decision-making across industries.

You'll consolidate your learning by applying prescriptive analytics to a capstone project. You’ll develop optimization models, analyze results, and prepare a professional report with actionable recommendations tailored to stakeholders. This hands-on experience will prepare you to lead data-driven innovations and effectively communicate the value of prescriptive analytics in decision-making.

Guided by Professors Vikrant Vaze and Reed Harder, this course blends rigorous academic instruction with practical, real-world applications. Whether a seasoned professional or new to analytics, you’ll leave this course with the skills and confidence to tackle complex decisions and contribute to your organization’s digital transformation.

What's inside

Learning objectives

● optimize decision-making using python : build and solve linear and mixed-integer optimization models with python tools like pyomo, tackling real-world challenges in logistics, resource allocation, and planning.
● transform non-linear problems : apply linearization techniques to convert complex non-linear constraints into linear forms for efficient and scalable solutions.
● model complex decisions : incorporate integer variables and logical rules into optimization models to handle discrete decisions, such as project selection or facility placement.

● evaluate and refine models : use sensitivity analysis, branching, bounding, and pruning techniques to ensure robust and effective solutions that adapt to changing conditions.
● leverage prescriptive analytics for strategy : apply optimization and prescriptive analytics to develop actionable recommendations, enhancing efficiency and decision-making in digital transformation contexts.

● optimize decision-making using python : build and solve linear and mixed-integer optimization models with python tools like pyomo, tackling real-world challenges in logistics, resource allocation, and planning.
● transform non-linear problems : apply linearization techniques to convert complex non-linear constraints into linear forms for efficient and scalable solutions.
● model complex decisions : incorporate integer variables and logical rules into optimization models to handle discrete decisions, such as project selection or facility placement.
● evaluate and refine models : use sensitivity analysis, branching, bounding, and pruning techniques to ensure robust and effective solutions that adapt to changing conditions.
● leverage prescriptive analytics for strategy : apply optimization and prescriptive analytics to develop actionable recommendations, enhancing efficiency and decision-making in digital transformation contexts.

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Read about what's good

what should give you pause

and possible dealbreakers

Uses Python and Pyomo, which are valuable tools for building and solving optimization models in various industries and academic research

Covers linearization techniques, which are essential for addressing complex, real-world problems that often involve non-linear constraints

Explores sensitivity analysis, branching, bounding, and pruning techniques, which are crucial for refining models and ensuring robust solutions

Requires learners to use Python, which may pose a barrier to entry for those without prior programming experience

Presented by Dartmouth College, which is known for its engineering program and contributions to the field of analytics

Reviews summary

Practical prescriptive analytics with python

According to learners, this course provides a positive and highly practical introduction to prescriptive analytics, focusing on optimization models using Python and Pyomo. Students particularly appreciate the hands-on approach, noting that the concepts are well explained and directly applicable to real-world problems. The course structure, moving from linear to mixed-integer optimization, is seen as logical and effective. While some find the capstone project challenging, many consider it a valuable culmination of the learning. Overall, it is viewed as a strong course for those looking to implement optimization techniques.

The final project is demanding but rewarding.

"The capstone project is quite challenging, requiring integration of all learned concepts."

"While difficult, the capstone project was incredibly rewarding and cemented my understanding."

"It was tough, but completing the capstone made me feel prepared to tackle real-world problems."

"I struggled a bit with the complexity of the final project, but the effort was worth it."

"The project pushed me to apply everything, which was great for learning."

Lectures are well-structured and easy to follow.

"The instructors explain complex topics in a clear and understandable manner."

"Lecture videos were concise and focused, making it easy to absorb the material."

"I found the explanations of different optimization techniques very lucid."

"Complex math was broken down effectively, which I really appreciated."

Provides a solid understanding of core optimization principles.

"The course provides a solid foundation in linear and mixed-integer optimization principles."

"Concepts like decision variables, objective functions, and constraints were explained very clearly."

"I finally understand the difference between linear and non-linear models and how to approach them."

"Gained a deep understanding of the fundamentals needed for prescriptive analytics."

"The theoretical background provided was sufficient to grasp the practical applications."

Applies analytics to real-world business scenarios.

"The examples used for inventory, resource allocation, etc., were very relevant to business applications."

"I can see how I can immediately apply these optimization techniques in my professional role."

"Great course for anyone looking to use data to make better business decisions."

"The focus on solving business problems using analytics is a major strength."

"It directly addresses how prescriptive analytics drives operational efficiency."

Learn to build optimization models using Python.

"The use of Pyomo and Python is fantastic, really helped solidify my understanding by actually coding the models."

"I loved how practical this course is, applying concepts directly using Python scripts from the start."

"Building the optimization models in Python gave me the confidence to apply these methods in my job."

"Excellent practical application using Pyomo. The hands-on coding exercises are key."

"Found the Python examples very clear and easy to follow for implementing the theory."

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 Prescriptive Analytics with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are foundational for understanding optimization techniques used in prescriptive analytics.

Browse courses on Linear Algebra

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Review key concepts like vectors, matrices, and linear transformations.
Practice solving systems of linear equations.
Work through examples of eigenvalue and eigenvector calculations.

Brush Up on Python Programming

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Strengthen your Python skills, as the course heavily relies on Python and Pyomo for implementing optimization models.

Browse courses on Python

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Review basic Python syntax and data structures.
Practice writing functions and classes in Python.
Familiarize yourself with numerical libraries like NumPy and Pandas.

Read 'Linear Programming: Methods and Applications' by Saul I. Gass

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Supplement your understanding of linear programming with a classic text that covers methods and applications in detail.

View Linear Programming: Methods and Applications:... on Amazon

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Obtain a copy of 'Linear Programming: Methods and Applications'.
Read the chapters on the Simplex method and duality theory.
Work through the examples provided in the book.

Four other activities

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Solve Linear Optimization Problems with Pyomo

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Practice formulating and solving linear optimization problems using Pyomo to solidify your modeling skills.

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Install Pyomo and a suitable solver (e.g., GLPK).
Find a set of linear optimization problems online or in textbooks.
Formulate each problem as a Pyomo model.
Solve the models using the chosen solver and interpret the results.

Optimize a Supply Chain Network

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Apply your knowledge to a real-world problem by designing an optimization model for a supply chain network.

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Define the scope of the supply chain network (e.g., number of suppliers, warehouses, and customers).
Gather data on costs, capacities, and demands.
Formulate a mixed-integer linear program to minimize total costs.
Implement the model in Pyomo and solve it.
Analyze the results and propose actionable recommendations.

Create a Presentation on Prescriptive Analytics

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Solidify your understanding by creating a presentation that explains the concepts and applications of prescriptive analytics to a non-technical audience.

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Choose a specific application area for prescriptive analytics (e.g., healthcare, finance, logistics).
Research the current state of prescriptive analytics in that area.
Prepare a presentation that explains the key concepts, benefits, and challenges of using prescriptive analytics.
Present your findings to a group of peers or colleagues.

Read 'Practical Optimization: Algorithms and Engineering Applications' by Andreas Antoniou and Wu-Sheng Lu

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Expand your knowledge of optimization algorithms with a book that covers both theory and practical applications.

View Practical Optimization on Amazon

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Obtain a copy of 'Practical Optimization: Algorithms and Engineering Applications'.
Read the chapters on linear and nonlinear programming.
Work through the examples provided in the book.

Career center

Learners who complete Prescriptive Analytics will develop knowledge and skills that may be useful to these careers:

Supply Chain Analyst

A Supply Chain Analyst optimizes supply chain operations to improve efficiency and reduce costs. This course will be extremely useful to this career role. The course's focus on optimization principles and practical applications, including linear and mixed-integer optimization, directly supports the responsibilities of a Supply Chain Analyst. Specifically, skills in addressing inventory management and resource allocation challenges help to improve supply chain performance. The ability to model complex decisions, coupled with hands-on experience using Python, will be valuable for anyone who wants to be a Supply Chain Analyst.

See salaries and explore the career path for Supply Chain Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical methods to help organizations investigate complex issues, identify and solve problems, and make better decisions. This course helps build a foundation in prescriptive analytics, which is directly applicable to the quantitative analysis required in this role. Leveraging skills learned around optimization principles, you can develop models to address real-world business problems. You will also learn how to transform complex non-linear constraints into linear ones for efficient solutions, which is critical for operations research. Furthermore, the course emphasizes the use of Python-based tools like Pyomo and cloud-based platforms, offering hands-on experience that will be valuable as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Business Analytics Consultant

Business Analytics Consultants help organizations improve their performance by analyzing data and developing strategies. This course directly prepares individuals to excel as Business Analytics Consultants by providing a deep dive into optimization principles and practical applications. The ability to design and implement optimization models, solve intricate business problems, transform non-linear problems, and leverage tools like Pyomo are all directly applicable. Such skills are invaluable for helping clients make better decisions and achieve their strategic goals.

See salaries and explore the career path for Business Analytics Consultant

Logistics Manager

Logistics Managers oversee the flow of goods and materials in an organization's supply chain. This course directly supports the responsibilities of a Logistics Manager. Learning how to optimize decision-making using tools and languages such as Python is extremely relevant to this field. Furthermore, the course helps to build and solve linear and mixed integer programming models. Learning to apply prescriptive analytics for strategy is also useful for a Logistics Manager.

See salaries and explore the career path for Logistics Manager

Management Consultant

Management Consultants analyze business problems and develop strategies for improvement. This course helps prepare you to use prescriptive analytics to drive operational efficiency and strategic decision-making across industries. By learning to design and implement optimization models, you can tackle intricate business problems and align them with digital transformation initiatives. You can apply the skills learned in this course to develop optimization models, analyze results, and provide actionable recommendations for stakeholders. A Management Consultant benefits from understanding how to transform non-linear problems into linear ones and how to leverage tools such as Pyomo.

See salaries and explore the career path for Management Consultant

Digital Transformation Manager

Digital Transformation Managers lead initiatives to integrate digital technology into all areas of an organization. This course is tailored to equip professionals for roles such as Digital Transformation Manager. The course focuses on using prescriptive analytics to drive strategic decision-making. The course is valuable for leading data-driven innovations and effectively communicating the value of prescriptive analytics to stakeholders. By learning to optimize decision-making using tools such as the Python programming language, Digital Transformation Managers can improve their success.

See salaries and explore the career path for Digital Transformation Manager

Revenue Management Analyst

Revenue Management Analysts optimize pricing and inventory to maximize revenue. This course is designed to equip you with the tools and methodologies needed to transform raw data into actionable strategies for decision-making in complex, real-world scenarios. This course would be valuable for a Revenue Management Analyst. The models that one could develop by learning these tools will provide actionable insight into how decisions can affect revenue.

See salaries and explore the career path for Revenue Management Analyst

Pricing Analyst

Pricing Analysts determine optimal pricing strategies for products or services. This course provides a strong foundation for a career as a Pricing Analyst. The ability to design and implement optimization models to address real-world business problems, including those related to revenue optimization, is directly applicable. A Pricing Analyst would benefit from the course's focus on transforming non-linear problems into linear ones. By leveraging tools such as Python, you can implement linear optimization models to address challenges.

See salaries and explore the career path for Pricing Analyst

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning algorithms and systems. You can use optimization techniques learned in this course to use in machine learning applications. Furthermore, the concepts behind how to transform non linear problems into linear ones can be useful in this career. By learning how to evaluate and refine models, you can build robust solutions.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, and interpret data to identify trends and relationships. While it may be common to hold an advanced degree to become a statistician, this course provides a solid basis with which to apply prescriptive analytics to develop actionable recommendations. You can improve your skills as a Statistician by learning how to optimize decision making using Python. Learning to evaluate and refine models helps to find robust and effective solutions that adapt to changing conditions.

See salaries and explore the career path for Statistician

Business Intelligence Analyst

A Business Intelligence Analyst uses data to analyze an organization’s performance and identify areas for improvement. This course helps to optimize decision-making using the power of the Python programming language. This course helps to build models, which could be useful for this role. As a Business Intelligence Analyst, you can apply prescriptive analytics to develop actionable recommendations.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

A Financial Analyst provides guidance to businesses and individuals making investment decisions. This course may be useful. The skills you learn in this course could be beneficial for portfolio optimization, in order to provide insights on the best possible investments. Additionally, the course could help you to understand how to evaluate and refine models to ensure robust and effective solutions that adapt to changing conditions. This could be of use to a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientists analyze large datasets to extract meaningful insights and actionable recommendations. This course may be useful because it introduces optimization principles and their practical applications, which allows Data Scientists to build and solve linear and mixed-integer optimization models. The ability to transform non-linear problems into linear forms, coupled with hands-on experience in Python, enhances a Data Scientist's ability to handle complex real-world challenges. Working with tools like Pyomo for scalable solutions, as taught in this course, will be beneficial for those working as Data Scientists.

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Quantitative Analyst

Quantitative Analysts, often working in the finance industry, develop and implement complex mathematical models to analyze financial markets and manage risk. This course may be useful in this role. Skills in optimization principles can be applied to such a role, as well as techniques to transform non-linear problems into linear forms. You will also learn how to evaluate and refine models to ensure robust and effective solutions that adapt to changing conditions.

See salaries and explore the career path for Quantitative Analyst

Data Architect

Data Architects design and build systems for storing and managing data. This course may be useful for this role. You can improve as a Data Architect by learning to transform non-linear problems. Moreover, this course helps to leverage prescriptive analytics for strategy. By studying this course, you improve your ability to evaluate and refine models.

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Prescriptive Analytics

What's inside

Learning objectives

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Practical prescriptive analytics with python

Activities

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