Syllabus - Data Driven Decisions

Syllabus - Data Driven Decisions#

Monte Carlo Analysis for Technical Communications#

Course Description: This course explores the integration of Monte Carlo analysis—a powerful probabilistic modeling technique—with technical communication. Students will learn how to effectively communicate results from Monte Carlo methods for both technical and non-technical audiences. Topics covered include probability modeling, Monte Carlo simulation, statistical analysis, and the creation of technical reports and presentations.

Course Goal:#

Appreciate the intersection of quantitative engineering and social design. As engineers, we are humans designing for other humans. The questions we ignore are often more detrimental than the questions we ask. Our goal is to discuss how engineering design affects people, communities, and history.

Course Outcomes:#

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

  1. Criticize and edit generative AI outcomes

    • Identify and revise passive voice constructions in technical writing

    • Improve technical documents for conciseness by eliminating unnecessary words and redundancies

    • Proof read work to identify and improve technical communications

    • Quantify generalizations made by generative AI claims

    • Quantify changes in a doument to attribute authorship and intellectual property

  2. Discuss Uncertainty in Design:

    • Perform statistical analysis of simulation data to extract meaningful insights

    • Present clear and concise statistical findings

    • Distinguish between qualitative (social) and quantitative (engineering) uncertainty in technical documents

    • Illustrate the importance of social uncertainty in engineering design

  3. Monte Carlo Simulation:

    • Create Monte Carlo simulations using software tools (e.g., Python, R) to analyze complex systems and scenarios

    • Interpret and communicate the results of Monte Carlo simulations

    • Explain the principles and applications of Monte Carlo simulation in data-driven decision-making

    • Develop probabilistic models for real-world scenarios

  4. Create Visualizations of Data:

    • Generate effective and informative visual representations of engineering data using appropriate tools (e.g., Excel, Python, or MATLAB)

    • Select suitable visualizations for different data sets and e.g. plots, bar charts, scatter plots, histograms, contour plots, communication purposes

  5. Research Engineering Designs and Processes

    • Find, review, and cite reliable sources of information (textbooks, articles, journals, news sources)

    • Contextualize technical and social information to validate a design or process

    • Compose a unique case study in engineering decisions using data from models and sources

  6. Compose Writing According to Audiences:

    • Identify and analyze the needs and expectations of different audiences

    • Edit technical writing to communicate with audiences

Course Information#

Course Title: ME3256 - Data Driven Decisions: Monte Carlo Analysis for Technical Communications

Format: Hybrid

Instructor: Prof. Ryan C. Cooper ryan.c.cooper@uconn.edu

Prerequisites: Basic understanding of probability and statistics is recommended. Familiarity with programming languages such as Python or R is beneficial but not required. I will provide example code and resources. We are coding to learn, not learning to code.

Office Hours/Availability: by appointment https://cooperrc.github.io

Preferred Contact: Post questions and comments to Discussions

Assessment Methods:#

  1. Writing Assignments: Write 500-word technical documents

  2. Technical Report: Present a Monte Carlo solution to an engineering design problem in a 3000-word technical report

  3. Peer Review and Feedback: Evaluate peers’ technical communication, specifically revisions on the main technical report project

  4. Online Activities and Discussions: Actively participate in online activities and revisions

Grading Policy:#

  • Written Assignments: 30%

  • Technical Report: 30%

  • Online Activities and Participation: 20%

  • Peer Review: 20%

Note on Writing assignments: Each written assignment will ask you to explain a technical result to a given audience. You will get comments on the assignment and a current grade. You can increase your grade by addressing the comments and resubmitting the work.

Note on Technical Report: The project is a longer technical document for other entry-level engineers. You will outline the report and share your proposed figures and images and communicate your findings throughout the course. You should plan to give and receive feedback on each stage of the writing process:

  • outline

  • first draft

  • final draft

Academic Integrity:#

  • The instructors encourage you to use a wide variety of resources including peers, online research, instructors, textbooks, articles, and even generative AI. Be sure you properly cite your sources and explain your own intellectual contributions. You should not let resources substitute any of our course learning objectives.

  • Read and understand The UConn Student Code of Conduct. Students will follow all University regulations concerning the final exam.

Required Resources#

Minimum Technical Skills:#

  • Ability to follow coding tutorials

  • Comfortable executing code in a prompt

  • Comfortable working in a web browser

  • Ability to search for answers and references in Google, Wikipedia and news sources

Grading Scale#

Explanation

Letter Grade

GPA

Excellent

A

4

A-

3.7

Very Good

B+

3.3

Good

B

3

B-

2.7

C+

2.3

Average

C

2

Fair

C-

1.7

Poor

D+

1.3

D

1

Merely Passing

D-

0.7

Failure

F

0

Due Dates and Late Policy#

The course calendar identifies course due dates. Deadlines are based on Eastern Time; if you are in a different time zone, please adjust your submittal times accordingly. The instructor reserves the right to change dates accordingly as the course progresses. I will communicate changes via the calendar or another appropriate notification.

Late Policy: You must submit something for every due date. You can resubmit any assignment to get points back, but if you miss the submission date, its a 0. If you are stuck on a topic or requirement submit some questions you have about the assignment and we can revise and improve the work.

Feedback and Grades#

We will make every effort to provide feedback and grades within 2 business days. To keep track of your performance in the course, refer to grades in HuskyCT. You can resubmit any assignment with improvements. Add a private comment to your submission when you have incorporated comments.

Student Responsibilities and Resources#

As a member of the University of Connecticut student community, you are held to certain standards and academic policies. In addition, there are numerous resources available to help you succeed in your academic work. Review these important standards, policies and resources, which include:

  • The Student Code

    • Academic Integrity

    • Resources on Avoiding Cheating and Plagiarism

  • Copyrighted Materials

  • Netiquette and Communication

  • Adding or Dropping a Course

  • Academic Calendar

  • Policy Against Discrimination, Harassment and Inappropriate Romantic

  • Relationships

  • Sexual Assault Reporting Policy

Students with Disabilities#

The University of Connecticut is committed to protecting the rights of individuals with disabilities and assuring that the learning environment is accessible. If you anticipate or experience physical or academic barriers based on disability or pregnancy, please let me know immediately so that we can discuss options. Students who require accommodations should contact the Center for Students with Disabilities, Wilbur Cross Building Room 204, (860) 486-2020 or http://csd.uconn.edu/.

Software/Technical Requirements (with Accessibility and Privacy Information)#

The software/technical requirements for this course include:

NOTE: This course was not tested or designed for mobile devices.

Help#

Technical and Academic Help provides a guide to technical and academic assistance.