Course Number Block Course Title Short Description
DSCI 511 1 Programming for Data Science Overview of data structures, iteration, flow control, and program design relevant to data exploration and analysis. When and how to exploit pre-existing libraries.
DSCI 521 1 Computing Platforms for Data Science How to install, maintain, and use the data scientific software “stack”. The Unix operating system, integrated development environments, and problem solving strategies.
DSCI 522 2 Data Science Workflows Interactive vs. scripted/unattended analyses and how to move fluidly between them. Reproducibility through automation and dynamic, literate documents. The use of version control and file organization to enhance machine- and human-readability.
DSCI 523 2 Data Wrangling Converting data from the form in which it is collected to the form needed for analysis. How to clean, filter, arrange, aggregate, and transform diverse data types, e.g. strings, numbers, and date-times.
DSCI 512 2 Algorithms and Data Structures How to choose and use appropriate algorithms and data structures to help solve data science problems. Key concepts such as recursion and algorithmic complexity (e.g., efficiency, scalability).
DSCI 551 2 Exploratory Data Analysis for Data Science Describing data in terms of its location, spread, and general distribution. How to balance the use of procedures from classical, parametric statistics with robust approaches that account for outliers and missing data.
DSCI 513 3 Databases and Data Retrieval How to work with data stored in relational database systems or in formats utilizing markup languages. Storage structures and schemas, data relationships, and ways to query and aggregate such data.
DSCI 552 3 Statistical Inference and Computation I The statistical and probabilistic foundations of inference, developed jointly through mathematical derivations and simulation techniques. Important distributions and large sample results. The frequentist paradigm.
DSCI 531 4 Data Visualization I The design and implementation of static figures across all phases of data analysis, from ingest and cleaning to description and inference. How to make principled and effective choices with respect to marks, spatial arrangement, and colour.
DSCI 524 4 Collaborative Software Development How to exploit practices from collaborative software development techniques in data scientific workflows. Appropriate use of abstraction and classes, the software life cycle, unit testing / continuous integration, and packaging for use by others.
DSCI 553 4 Statistical Inference and Computation II Methods for dealing with the multiple testing problem. Bayesian reasoning for data science. How to formulate and implement inference using the prior-to-posterior paradigm.
DSCI 561 4 Regression I Linear models for a quantitative response variable, with multiple categorical and/or quantitative predictors. Matrix formulation of linear regression. Model assessment and prediction.
DSCI 525 5 Web and Cloud Computing How to use the web as a platform for data collection, computation, and publishing. Accessing data via scraping and APIs. Using the cloud for tasks that are beyond the capability of your local computing resources.
DSCI 542 5 Communication and Argumentation Effective oral and written communication, across diverse target audiences, to facilitate understanding and decision-making. How to present and interpret data, with productive skepticism and an awareness of assumptions and bias.
DSCI 562 5 Regression II Useful extensions to basic regression, e.g., generalized linear models, mixed effects, smoothing, robust regression, and techniques for dealing with missing data.
DSCI 571 5 Supervised Learning I Introduction to supervised machine learning, with a focus on classification. K-NN, Decision trees, SVM, how to combine models via ensembling: boosting, bagging, random forests. Basic machine learning concepts such as generalization error and overfitting.
DSCI 541 6 Privacy, Ethics, and Security The legal, ethical, and security issues concerning data, including aggregated data. Proactive compliance with rules and, in their absence, principles for the responsible management of sensitive data. Case studies.
DSCI 563 6 Unsupervised Learning How to find groups and other structure in unlabeled, possibly high dimensional data. Dimension reduction for visualization and data analysis. Clustering, association rules, model fitting via the EM algorithm.
DSCI 572 6 Supervised Learning II Stochastic gradient descent. Logistic Regression. Neural networks and deep learning: state-of-the-art implementation considerations in both software and hardware (GPUs).
DSCI 573 6 Feature and Model Selection How to evaluate and select features and models. Cross-validation, ROC curves, feature engineering, the role of regularization. Automating these tasks with hyperparameter optimization.
DSCI 532 7 Data Visualization II Analysis, design, and implementation of interactive figures. How to provide multiple views, deal with complexity, and make difficult decisions about data reduction.
DSCI 554 7 Experimentation and Causal Inference Statistical evidence from randomized experiments versus observational studies. Applications of randomization, e.g., A/B testing for website optimization.
DSCI 574 7 Spatial and Temporal Models Model fitting and prediction in the presence of correlation due to temporal and/or spatial association. ARIMA models and Gaussian processes.
DSCI 575 7 Advanced Machine Learning Advanced machine learning methods, with an undercurrent of natural language processing (NLP) applications. Bag of words, recommender systems, topic models, ranking, natural language as sequence data, POS tagging, CRFs for named entity recognition and RNNs for text synthesis. An introduction to popular NLP libraries in Python.
DSCI 591 8 Capstone Project A mentored group project based on real data and questions from a partner within or outside the university. Students will formulate questions and design and execute a suitable analysis plan. The group will work collaboratively to produce a project report, presentation, and possibly other products, such as a web application.