ADSC 3610 - Database Systems in Applied Data Science II - Syllabus

This course will introduce students to the fundamentals of non-relational databases, with a focus on MongoDB as the main platform. We will cover data modeling, CRUD operations, and advanced querying techniques using MongoDB and its integration in Python via Pymongo. The course then transitions to PySpark, covering distributed data processing, DataFrames, Spark SQL, and machine learning applications. In the final section, the focus shifts to data governance, ethics, and security, addressing key topics such as data quality management, privacy, legal compliance, and the ethical implications of AI.

Learning objectives

• Understand the fundamental concepts of non-relational databases and their differences from relational databases.

• Gain proficiency in using MongoDB for data storage, retrieval, and manipulation.

• Learn to design and implement efficient data models in MongoDB.

• Master the basics of PySpark for big data processing and analytics.

• Perform data wrangling and transformation tasks using PySpark.

• Understand the principles of distributed computing and how they apply to PySpark.

• Grasp the key principles of data governance, compliance, and privacy, ensuring responsible and secure data management.

• Explore and address the ethical implications of AI and machine learning

Schedule & office hours

Lectures every Tue, Thu, Fri from 9:30 - 10:20 AM in the computer lab OM 1241 at Old Main building.

Office hour: Thu 11:30 AM-12:30 PM at OM 1232. Please book your timeslot here

Communication

• For any course-related questions, such as lectures, assignments, exames, course logistics, please ask them under the discussion forum in Moodle.

• For any individual-related questions, such as academic concession, deadline extension, personal circumstances, etc., please email me at lnguyen[at]tru[dot]ca

• Response time: I will try our best to reply to your inquiries as soon as possible during the normal working hours (9AM-5PM Mon-Fri). If you send me a message outside of regular working hours, please expect a response on the next working day.

Lectures

Please find the list of lecture, and the required readings below. This list will be updated frequently so please revisit to get the latest version.

Week no.	Week day	Date	Topic
1	Tue	Sep 3	Introduction to noSQL database, READINGS
1	Thu	Sep 5	Introduction to MongoDB and the document model, READINGS
1	Fri	Sep 6	Setup MongoDB Atlas & connecting to a MongoDB database, READINGS
2	Tue	Sep 10	Data modelling 1: types of data relationships,embedding, referencing MongoDB rule READINGS
2	Thu	Sep 12	Data modelling 2: Schema design pattern, READINGS
3	Tue	Sep 17	MongoDB CRUD operations: Insert,Find, Replace and Delete
3	Thu	Sep 19	MogoDB CRUD operations: Modifying query results, MongoDB Aggregation in Python
4	Tue	Sep 24	MongoDB ACID transactions
4	Thu	Sep 26	MongoDB Atlas search, Atlas Vector Search for LLMs
5	Tue	Oct 1	Part 1: MongoDB review
5	Thu	Oct 3	MIDTERM 1: Cover materials from week 1 - 4
6	Tue	Oct 8	Introductino to PySpark ,Working with RDDs
6	Thu	Oct 10	Introductin to DataFrames in Pyspark
7	Tue	Oct 15	Basic dataframe operations
7	Thu	Oct 17	Spark SQL
8	Tue	Oct 22	Dataframe optimization techniques , Working with structured streaming
8	Thu	Oct 24	Spark streaming continued
9	Tue	Oct 29	MLflow
9	Thu	Oct 31	Course recap
10	Tue	Nov 5	MIDTERM 2: Cover materials from week 5 - 9
10	Thu	Nov 7	MID-TERM BREAK
11	Tue	Nov 12	Data privacy overview management
11	Thu	Nov 14	Data privacy , Data security fundamentals
12	Tue	Nov 19	Bias and fairness in machine learning
12	Thu	Nov 21	Mitigate bias and fairness in machine learning
13	Tue	Nov 26	AI and ethics
13	Thu	Nov 28	Course conclusion

Assessment overview

You are responsible for the following deliverables, which will determine your course grade:

Assignment	Note	Weight	Date
Weekly worksheet x 10	Open-book. Release on Mondays	20%	Sundays 11:59PM
Midterm 1	Closed-book, Moodle timed-quiz, 45 minutes	25%	Thu, Oct 3, 9:30AM
Midterm 2	Closed-book, Moodle timed-quiz, 90 minutes	25%	Nov 5th, 09:30 AM
Exam	Closed-book, Moodle timed-quiz, 90 minutes	30%	Dec 04, 02:00 PM

Weekly assignments

Every week, you will have a worksheet that is worth 2%. These low-stakes assignments consist of multiple choice questions and small exercises that help you consolidate your understanding of the materials and serve as a formative assessment.

The worksheet will be distributed via Github classroom every Monday (except for midterms’ weeks) and the deadline for each worksheet is the same Sunday of that week at 11:59 PM.

Mid-terms & exam

There will be two midterms and one final exam in this course.

The midterms are closed-book format, and they will take place on Moodle for the duration of 45 minutes. It will consist of a mix of multiple choice, fill in the blank, and open-ended questions.

Midterm 1 will cover the materials on MongoDB Midterm 2 will be an individual project cover the materials on Spark

The final exam is closed-book format, taking place on Moodle for the duration of 90 minutes. It will consist of a mix of multiple choice, fill in the blank, and open-ended questions.

The final exam will cover all the materials in the course.

Attendance, late assignments, academic concessions, academic accomodation

Attendance

A registered student who does not attend the first two events (e.g., lectures/labs/ etc.) of their course(s) and who has not made prior arrangements acceptable to the instructor(s) may, at the discretion of the instructor(s), be considered to have withdrawn from the course(s) and have their course registration(s) deleted.

Please refer to TRU’s attendance policy. In addition, we will take attendance during class via Moodle’s QR code.

In the CS department, you need to get at least 75% attendance for passing any course.

Academic concessions

If you encounter situations that may impede your ability to meet course requirements—such as illness, family emergencies, or other significant life events—please notify the instructor at least 24 hours before deadline. Academic concessions, including extensions or alternative assessments, will be considered on a case-by-case basis. You may be required to provide documentation to support your request. Concession requests after the deadline has passed will likely be refused.

Late Assignments

Assignments are expected to be submitted on time. Late submissions will incur a penalty of 25% per day, up to a maximum of 75%. After 3 days, late assignments will no longer be accepted and will receive a grade of zero. Extensions may be granted in exceptional circumstances, provided that you contact the instructor before the deadline.

Accessbility

Students registered with the Accessibility Services who require accommodations must provide their Letter of Accommodation to the instructor as soon as possible. This letter will outline the necessary accommodations to ensure an equitable learning environment. Please ensure that this is done early in the term to facilitate timely arrangements.

Policy on the use of generative AI

Please refer to TRU’s guideline on the use of generative AI tools such as chatGPT or Copilot in this course.

https://libguides.tru.ca/artificialintelligence