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Computer Science, Faculty of Science

DSCI: Data Science

DSCI 511 (1) Programming for Data Science
Pseudo-code. Program design and structure. Flow control. Iteration. Lists (arrays). Functions. File I/O. Classes, objects, methods, and libraries. This course is not eligible for Credit/D/Fail grading.
DSCI 512 (1) Algorithms and Data Structures
Basic algorithms. Recursion. Data structures including linked lists, queues, stacks, trees, graphs, and hash tables. Searching and sorting. Introduction to complexity including Big-O notation, efficiency, and scalability. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 513 (1) Databases and Data Retrieval
Relational schemas. SQL queries. Database programming using embedded SQL. XML and XQuery. This course is not eligible for Credit/D/Fail grading.
DSCI 521 (1) Computing Platforms for Data Science
Introduction to software, shells, tools, and file systems for use in the Data Science program. Installation, configuration, and use of statistical and programming software including Integrated Development Environments (IDEs). Problem resolution skills. This course is not eligible for Credit/D/Fail grading.
DSCI 522 (1) Data Science Workflows
Interactive and non-interactive data analysis. Scripting. Dynamic reporting. Reproducibility. Project and file management. Version control. Automated workflows. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 523 (1) Data Wrangling
Manipulation of tabular and non-tabular data using software tools. Organizing, filtering, sorting, grouping, reformatting, converting, and cleaning data to prepare it for further analysis. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 524 (1) Collaborative Software Development
Software life cycle. Unit testing. Continuous integration. Submission to a relevant repository for distribution. Packaging for installation and use by others. Software licenses. Classes and abstraction. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 522.
DSCI 525 (1) Web and Cloud Computing
Networks and the Internet, scraping data, APIs, cloud computing, Web services for scalable computing, Web hosting, Web publication platforms, introduction to parallel computing. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 522, DSCI 523.
DSCI 531 (1) Data Visualization I
Descriptive plots using statistical and programming software. Basics, mechanics, and principles of data visualization. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 532 (1) Data Visualization II
Interactive visualization, design choices, dynamic change over time, multiple views, data reduction, dealing with complexity. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 531.
DSCI 541 (1) Privacy, Ethics, and Security
Privacy and data. Ethics boards, legal issues, licensing. Physical and logical data security, social engineering. Encryption, data anonymization, privacy-preserving techniques. Case studies. This course is not eligible for Credit/D/Fail grading.
DSCI 542 (1) Communication and Argumentation
Claims, reasons, and evidence. Strengths and weaknesses of models. Effective oral and written presentation of scientific results, including interpretation of data and recognition of assumptions, bias, validity, and reliability. Citations, references, and peer-review. This course is not eligible for Credit/D/Fail grading.
DSCI 551 (1) Descriptive Statistics and Probability for Data Science
Descriptive statistics including measures of location and spread. Random variables, distributions, and parameters. Categorical variables. Uncertainty. Missing data. This course is not eligible for Credit/D/Fail grading.
DSCI 552 (1) Statistical Inference and Computation I
Random variables, parameters, observed data, statistics (distinctions and connections). Estimation: point and interval. Two-group comparisons, frequentist version. Simulation-based approaches. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 551.
DSCI 553 (1) Statistical Inference and Computation II
Multiple hypothesis testing, false discovery rate. Two-group comparisons, Bayesian paradigm. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 552.
DSCI 554 (1) Experimentation and Causal Inference
Randomization. A/B testing. Blocked designs. Orthogonality. Batch effects, confounding. Causality. Contemporary examples. Simulations. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 553, DSCI 561.
DSCI 561 (1) Regression I
Linear models: continuous response; one or more categorical covariates and/or one or more continuous covariates. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 552.
DSCI 562 (1) Regression II
Non-parametric regression and smoothing. Data-driven parameter selection. Robust regression. Mixed effects. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 561.
DSCI 563 (1) Unsupervised Learning
Unsupervised learning. K-means/medoids. Model-based clustering. Expectation-maximization algorithm. Hierarchical clustering. Dimension reduction. Matrix decomposition. Heatmaps, contour plots, dendograms. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 571 (1) Supervised Learning I
Decision trees. k-th nearest neighbour classifiers. Naive Bayes classifiers. Logistic regression. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 511, DSCI 521.
DSCI 572 (1) Supervised Learning II
Support Vector Machines. Random Forests. Ensemble Classifiers. Graphical models. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 571.
DSCI 573 (1) Feature and Model Selection
Performance of a classification model. Generalization error, overfitting of training data. Shrinkage, feature selection, Akaike Information Criterion, Bayesian Information Criterion. k-fold cross validation. Bootstrapping. Receiver Operating Characteristic curve. Elastic nets, regularization. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 571.
DSCI 574 (1) Spatial and Temporal Models
Time series. State space and change point detection. Hidden Markov Models. Gaussian processes. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 572.
DSCI 575 (1) Advanced Machine Learning
Neural networks trained with backpropagation. Deep learning. Overfitting and underfitting. Active data acquisition. Hyperparameter optimization. This course is not eligible for Credit/D/Fail grading.
Prerequisite: DSCI 572.
DSCI 591 (6) Capstone Project
A capstone design project designed to give students experience in leading complex multidisciplinary projects relevant to data science. This course is not eligible for Credit/D/Fail grading.
Prerequisite: All of DSCI 513, DSCI 524, DSCI 525, DSCI 532, DSCI 541, DSCI 542, DSCI 554, DSCI 563, DSCI 573, DSCI 574, DSCI 575.

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