Course:FRE521D/Syllabus

COURSE DESCRIPTION

This applied course develops practical skills to design and run end-to-end data pipelines for the climate, food, and environment sectors. Students will source data from files, APIs, and databases; use SQL for reliable access; build reproducible Extract, Transform, and Load (ETL) in Python; and create refreshable outputs with clear visual explanations for stakeholders. Cases include using weather and wildfire data for climate risk and logistics timing, food CPI and commodity prices for procurement and pricing decisions, and ESG ratings and water quality metrics for screening and compliance.

Beyond the technical skill-building, this course is designed to help you think and work like an applied data analyst in the climate, food, and environmental sectors. You will learn how to move from a real decision question to a clean, reproducible data pipeline that produces trustworthy insights for stakeholders. By the end of the course, you will know how to find and access the right data, build efficient SQL and Python workflows, validate and document your work for handover, and present visual stories that inform decisions for producers, retailers, investors, NGOs, or policymakers.

LEARNING OUTCOMES

SQL & Data Access

Understand database schemas, keys, and relationships to identify correct join paths
Write efficient SQL queries using joins, CTEs, window functions, and pivot/unpivot
Create analysis-ready table views that feed directly into downstream analytics

ETL with Files and APIs

Build ETL workflows that pull data from CSV/JSON files and API endpoints
Handle authentication, parameters, pagination, and rate limits for reliable API access
Store raw and cleaned data separately and document assumptions for reproducibility

Data Wrangling & Engineering Foundations

Use Python to merge, reshape, and manage data types across multiple sources
Apply validation checks for ranges, missingness, and key integrity
Maintain clear data lineage notes linking inputs to outputs for auditability

Analysis & Visualization for Decision Support

Frame a clear decision question and choose appropriate measures and groupings
Run descriptive summaries and trend checks to surface insights
Produce reproducible tables and visualizations that address stakeholder questions in climate, food, and ESG contexts

ASSESSMENTS


Participation	Throughout the term	10%
Assignments/labs		20%
Quiz		10%
Midterm		30%
Project		30%
TOTAL		100%

COURSE TOPICS

To be updated soon.

MFRE PROGRAM - COURSE PROTOCOL POLICIES

Recordings

There is no required distribution of recordings of class. Recording will be provided based upon on the decision of the course instructor. Classes are designed as and are intended to be in‐person.

Copyright

All materials of this course (course handouts, lecture slides, assessments, course readings, etc.) are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

Missing Classes/Labs

Students are expected to attend all classes, labs, or workshops. If you cannot make it to a class, lab, or workshop due to a medical or personal emergency, please email your instructor, your course assistant, and Olivier Ntwali, MFRE Program Coordinator ahead of time to let them know.

Respectfulness in the Classroom

Students are expected to be respectful of their colleagues at all times, including faculty, staff and peers. This means being attentive and conscious of words and actions and their impact on others, listening to people with an open mind, treating all MFRE community members equally and understanding diversity.

Respect for Equity, Diversity, and Inclusion

The MFRE Program strives to promote an intellectual community that is enhanced by diversity along various dimensions including status as a First Nation, Métis, Inuit, or Indigenous person, race, ethnicity, gender identity, sexual orientation, religion, political beliefs, social class, and/or disability. It is expected that all students and members of our community conduct themselves with empathy and respect for others.

Centre for Accessibility

The Centre for Accessibility (CfA) facilitates disability‐related accommodations and programming initiatives designed to remove barriers for students with disabilities and ongoing medical conditions. If you are registered with the CfA and are eligible for exam accommodations, it is your responsibility to let Olivier Ntwali, Academic Program Coordinator, and each of your Course Instructors know. You should book your exam writing with the CFA using its exam reservation system: for midterm exams or quizzes, at least 7 days in advance; and final exams, 7 days before the start of the formal exam period.

MFRE PROGRAM - ACADEMIC HONESTY POLICIES

Plagiarism and Academic Dishonesty

Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.

Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:

Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.

Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non‐acceptable conduct for examinations and graded assignments as presented via MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.

Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.