Course:FRE521D/Syllabus
COURSE DESCRIPTION
In this course we will explore data and try to find patterns. We will look at the field of data science, using R, SQL, and Python to move through a data pipeline, from database, through some data wrangling, data visualization and inference.
LEARNING OUTCOMES
I. Machine Learning Fundamental
Provides a basic understanding of the technical elements of machine learning through a business lens and with applications in climate, food and the environment.
Learning Outcomes
§ Identify and describe machine learning terminology, different branches of machine learning, and steps needed to build a model .
§ Demonstrate a theoretical and practical understanding of a number of machine learning approaches including regression, classification, clustering, decision trees and random forest.
§ Separate data into training and testing data to prevent overfitting and optimize predictions.
§ Describe the best and worst practices of managing machine learning projects.
§ Gain practical experience and understand the value of applying machine learning to the climate, food and environmental sector. Example: Assessment of the ESG activities of a firms
II. SQL – Database Basics
Covers SQL’s foundational syntax and essential transformations with practical, hands-on examples. In addition, you will learn importance of structure in databases and why structure is essential plus the significance of SQL in data analysis and the modern job market.
Learning Outcomes
§ Describe the structure and design of relational databases.
§ Structure data using data models.
§ Conduct basic SQL Queries, Functions and Aggregation for retrieving and filtering data from a database, transforming and combining data from multiple tables.
§ Identify best practices and common errors when using SQL.
§ Gain practical experience and understand the value of applying SQL to the climate, food and environmental sector. Example: Carbon Emissions Calculator or query of data of Europe's rivers, lakes, groundwater bodies and transitional, coastal and marine waters to review pollution and quality.
III. Data Access and Querying on Cloud Based Platforms: API methods.
Covers a review of the different data formats, such as CSV, JSON, or XML and how to access.
Learning Outcomes
§ Describe and conduct data wrangling, data cleaning, and preliminary visualization with Python
§ Identify the advantages and disadvantages of the major data formats and best fit with analysis objectives.
ASSESSMENTS
| Participation | Throughout the term | 10% |
| Assignments/labs | 20% | |
| Quiz | 10% | |
| Midterm | 30% | |
| Project | 30% | |
| TOTAL | 100% |
COURSE TOPICS
1. Machine Learning (Week 1 to 3)
§ Terminology and Building Models
§ Machine learning approaches: regression, classification, clustering, decision trees and random forest.
§ Best and worst practices of managing machine learning projects.
§ Machine Learning pipeline development and value
§ Application: Climate Example: Assessment of ESG compliance by firms
2. SQL ( Week 3 and 5 )
§ Intro ( significance of SQL in data analysis and the modern job market, Understanding databases: Why are they essential?)
§ Databases
o Different types of databases: Relational vs. Non-relational
o Introduction to RDBMS (Relational Database Management System) structure
§ Intro to SQL
§ Basic SQL Queries ( ‘Select’, ‘Distinct’, ‘Where’, ‘Order by’, ‘Limit’
§ Functions and aggregation
o Basic functions: ‘count()’, ‘sum()’, ‘AVG()’, ‘MIN()’ ,‘Max()’
o Grouping data using ‘GROUP BY’
o Filtering on aggregated data using ‘HAVING’
§ Joins and relationship.
§ Best and worst practices of managing machine learning projects.
§ Application: Climate Example: https://wbwaterdata.org/dataset/waterbase-water-quality
3. Data Access and Querying on Cloud Based Platforms: API methods. (Week 5 to 6)
§ Data formats of CSV, JSON, or XML and how to access
§ Data wrangling, data cleaning, and preliminary visualization with Python
§ Advantages and disadvantages of the major data formats
§ Determining the best match between data formats and select analysis.
PRESENTATION OR VIDEO
1. Current Event Connections: Illustrate how your course ties into contemporary issues.
Machine Learning is the tool for analyzing big data and increasingly food and environment application have a big data component. This course will apply the data tools to areas such as ESG assessment, climate impact, and business ratings.
2. Career Relevance: Highlight how the course aligns with prospective career paths, emphasizing real-world applicability.
Businesses such as banks (lending assessment), agribusinesses (precision ag) are increasingly reliant on SQL and machine learning for assessments and predictive analytics.
3. Course Evaluations:
Major project provides the opportunity to practice and data skills to a real-world data set.
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.