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This page contains additional information for section instructors candidates in our Master of Applied Data Science (MADS) program. 

Following the timeline of the job posting, we will be looking for section instructors for the following courses in the Spring 2024 term: 


Course Title Brief Description
Statistical Modeling and Inference for Data Science   

The course will be coding-oriented and cover the concepts underpinning and the applications of statistical modeling/inference. Students will build models with real-world data and modern data science toolkits. Concepts covered in this course will include: foundations in probability including basic rules, Bayes theorem, basic distributions; sampling and the central limit theorem; bootstrapping, confidence intervals, hypothesis testing, multiple testing; linear models, basic and multiple regression, inference for regression, regularization; classification, logistic regression and tree-based methods; prediction, model interpretation, model evaluation.


Introduction to Applied Data Science   

Data is central to data science. The data explosion experienced in every aspect of our lives, from social media to advanced instrumentation, requires a deeper understanding of the full spectrum of data life cycle management. Starting with the concept of ‘what is data?’, the first part of this course introduces various stages of the data life cycle, from defining data requirements, to data creation and gathering, to data fusion and data preparation, to data cleaning and quality control, to exploratory analytics, data interpretation, and visualization. We will explore concepts in FAIR data principles of data curation, metadata, and digital preservation policies with the aim of data reuse and reproducible science. The second part of this course will introduce the concept of relational databases that provide storage and management for structured data. We will explore concepts and implementations of relational database management systems suited for data science applications.


Programming Methods for Data Science   

This course will provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Abstract data types, lists, stacks, queues, trees and graphs. Sorting, searching, hashing and an introduction to numerical error control. Techniques of algorithm analysis and problem-solving paradigms, using relevant programming languages and tools.