DA 401W: Prescriptive Analytics

Textbook Information

Writing About Data (1st Edition) by Joanna Wolfe. ISBN: 9781319339784; E-Text ISBN: 9781319339784

Published Remarks

None

Hardware Requirements

None

Software Requirements

No additional purchase needed for software used in the course.

Proctored Exams

None

Course Description

DA 401W is a four-credit course with lecture and writing components. (DA201W, DA 302W, and DA 401W each carry only 1 credit of "writing"; all three courses must be taken to meet the writing requirement). This course will introduce students to how data analytics assists in making decisions and advocating for a course of action. The core objective of this course is to help students develop a set of viable decision options (based on considerations including costs and benefits, key stakeholder preferences, ethical guidelines, etc.), ranking those decision options to create alternative courses of action, and how to achieve an optimal decision. Topics covered will include optimization, sensitivity analysis, decision making, linear programming, and simulation.

Welcome!

Welcome to DA 401W: Prescriptive Analytics! We’re thrilled to have you in this course, where you’ll continue your learning of data-driven decision-making. Throughout the modules, you’ll explore powerful techniques like linear programming, network analysis, and simulation modeling to solve real-world problems. You’ll learn how to optimize solutions, model complex systems, and analyze data to make actionable recommendations. Each module is designed to teach you valuable skills to enhance your data analytics skills and prepare you for practical, impactful decision-making.

The writing component of this course is a strategic part of your training in data analytics. Recommendations based on prescriptive analytics must be communicated in such a way that decision-makers have complete and clear information to inform their paths forward. For this reason, effective writing is essential for providing a transparent account of relevant factors upon which data-driven recommendations are made.

We’re excited to see you apply these concepts and develop a strong foundation in prescriptive analytics!

Course-Level Learning Objectives

By the end of the course, successful students will be able to:

  • Define and use the fundamental concepts of data analytics and decision-making processes, including optimization, sensitivity analysis, and linear programming.
  • Identify and define decision variables that represent quantities in a given problem context, demonstrating the ability to quantify real-world situations.
  • Recognize and express relevant constraints using linear inequalities or equations, ensuring a comprehensive understanding of the limitations impacting decision-making.
  • Propose strategic actions to address issues of feasibility or optimality in decision-making scenarios, ensuring that recommendations are grounded in analytical findings.
  • Communicate complex ideas clearly and effectively across various writing formats, including reports, presentations, and analyses
  • Critically evaluate ethical and social implications of data-driven decision-making by analyzing machine learning biases, addressing stakeholder concerns, and assessing corporate social responsibility strategies to promote fairness, sustainability, and social justice.
  • Demonstrate proficiency in professional data reporting and communication by organizing findings in the IMRD (Introduction, Methods, Results, Discussion) framework, documenting methods accurately, and presenting decision-making outcomes transparently for diverse stakeholder audiences.

Course Topic Outline

The following is an abbreviated list of the module topics.

  • Module 1: Linear Programming and Writing a Case Study Concept
  • Module 2: Graphical Method for Linear Programming and Decision making Factors
  • Module 3: Excel Solver for Linear Programming and Creating a Q&A page
  • Module 4: Scheduling and Reflecting on Corporate Responsibility
  • Module 5: Blending and DA & Social Justice
  • Mid-Term (on Modules 1-5) and Beginning the Final Report
  • Module 6: Transportation and Transshipment and Writing Results and Methods
  • Module 7: Network Analysis and Writing Discussions/Conclusions/Recommendations
  • Module 8: Integer Programming and Writing Front Matter
  • Module 9: Sensitivity Analysis
  • Module 10: Duality
  • Final Exam (on Modules 6-10)
  • Module 11: ARENA Simulation
  • Final Report
  • Final Project Video

DA 401W will rely upon a variety of methods to assess and evaluate student learning, including:

  • DA Graded Exercises – In most of the modules, there will be a graded assignment based on the data analytics content. These exercises will allow you to practice the DA related content through similar exercises and scenarios seen in the module content and the module practice exercises.
  • Writing Related Assignments – There are several writing assignments that will allow you to further hone your communication skills. Many of the assignments allow you to learn through writing. Topics of these assignments include creating a Q&A page, considering corporate responsibility, and more
  • Final Project – Starting at the mid-point of the course, you will work on a multi-phase final project. You will begin by choosing one of two case options that you will focus on throughout your final project phases. You will work on the case and your writing in phases, leading up to the submission of a final report. You will then create a summary video of your work to share with your classmates. This assignment is meant to allow you to practice your data analytics skills in conjunction with your writing skills.
  • Mid-Term and Final Exam – There will be a mid-term and final exam. These are set up as assignments that you will submit. The mid-term is on content from modules 1-5. The final is on content from modules 6-10. (A note: Module 11 is not assessed in the final exam.)
  • Other Assignments – At the beginning of the course, you will be asked to introduce yourself to your classmates.

You will earn a grade that reflects the extent to which you achieve the course learning objectives listed above. Grades are assigned by the percentage of possible points earned in each module’s activities. Below is a breakdown of each assignment category.