University of Calgary : Data Science DATA

Data Science DATA

DATA 201 211 221 299 305 311 335 399 414 433 443 471 479 499 501 543 599 601 602 603 604 605 606 607 608 609 610 611 612 613 614 621 622 623 624 691 693 695

For more information about these courses, contact the Department of Mathematics and Statistics science.ucalgary.ca/mathematics-statistics.

Junior Courses

Data Science 201

Thinking with Data

An introduction to tools and techniques for managing, visualizing, and making sense of data. Includes an introduction to data cleaning, basic statistics, exploratory visualization, sensemaking, and data presentation.
Course Hours: 3 units; (2-3)
Also known as: (formerly Science 201)

Data Science 211

Programming with Data

A hands-on introduction to basic coding skills, including core programming concepts and the fundamentals of reading, writing, and executing code – with a focus on data manipulation. Emphasizes important tools and practices for programming with data, including development environments, source control, and debugging.
Course Hours: 3 units; (2-3)
Prerequisite(s): Data Science 201.
Antirequisite(s): Credit for Data Science 211 and any one of Computer Science 215, 217, 231, 235, Computer Engineering 339, Engineering 233 or Digital Engineering 233 will not be allowed.

Data Science 221

Introduction to Data Science

An introduction to commonly used programming tools and techniques for managing, visualizing, and making sense of data. Human implications of data collection, analysis, and use, including ethics, privacy, and alternate ways of knowing.
Course Hours: 3 units; (3-2)
Prerequisite(s): Computer Science 217 or 231.

Data Science 299

Special Topics in Data Science

Exploration of various areas in Data Science. Topics will vary from year-to-year. It will be offered as required to provide the opportunity for students to engage in additional areas in Data Science. Before registration, consult the Faculty of Science for topics offered.
Course Hours: 3 units; (3-0) or (3-2T)
Prerequisite(s): Consent of the Faculty.
MAY BE REPEATED FOR CREDIT

Senior Courses

Data Science 305

Computational Statistical Modelling

Random variables and their probability models. The Central Limit Theorem and parameter estimation. Statistical modelling of univariate and multivariate data with applications to discrete and continuous data. Data transformations. Introduction to simulation-based inference including randomization and permutation tests.
Course Hours: 3 units; (3-2)
Prerequisite(s): Data Science 201; and 3 units from Data Science 211, Computer Science 217, 231 or 235; and 3 units from Statistics 205, 217, 327, Biology 315, Economics 395, Political Science 399, Psychology 300, Sociology 311, Engineering 319, Digital Engineering 319 or Linguistics 560.
Antirequisite(s): Credit for Data Science 305 and Statistics 323 will not be allowed.

Data Science 311

Data Processing and Storage

An introduction to fundamental data structures, including lists, stacks, trees, hash tables, and graphs, and their application for data processing, analysis, and storage. Covers the fundamental design and use of relational databases, with an emphasis on SQL.
Course Hours: 3 units; (2-3)
Prerequisite(s): Data Science 201; and 3 units from Data Science 211, Computer Science 217, 231, 235, Digital Engineering 233 or Engineering 233.
Antirequisite(s): Credit for Data Science 311 and either Computer Science 319 or 331 will not be allowed.

Data Science 335

Statistical Modelling I

An introduction to statistical computing and Bayesian modelling. Topics covered include random numbers generation, system/process simulation and evaluation, numerical integration, constrained and unconstrained optimization, Bayesian inference framework, single and multi-parameter models, regression models, Bayesian hierarchical modelling, Markov chain Monte Carlo.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 305.

Data Science 399

Special Topics in Data Science

Data Science 414

Ethics of Models, Metrics, Algorithms, and Data

Ethical frameworks pertaining to data science. Algorithmic fairness and bias. Explainable results in machine learning and artificial intelligence. Privacy and data governance for large datasets. Human implications of data science in Indigenous and underserved communities.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 221.
Antirequisite(s): Credit for Data Science 414 and Philosophy 314 will not be allowed.

Data Science 433

Essential Optimization

Unconstrained optimization and constrained optimization. Linear programing. Graph algorithms (including shortest-path and min-cut/max-flow) and integer programming. Simulated annealing and evolutionary algorithms. Other aspects of optimization as time permits.
Course Hours: 3 units; (3-2)
Prerequisite(s): Computer Science 217, Mathematics 267 and 311.

Data Science 443

Statistical Machine Learning

Core concepts of statistical machine learning, with an emphasis on the statistical background of the machine learning discipline. Classification and regression, nearest neighbours, Bayesian learning, decision tree, support vector machine, linear discriminant analysis, dimensionality reduction, and an introduction to neural networks.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 305 and Computer Science 319.
Antirequisite(s): Credit for Data Science 443 and Computer Science 544 will not be allowed.

Data Science 471

Data Management for Data Scientists

Current mechanisms for data management. It investigates various types of data models for dealing with various types of data, including big data with all its V’s (e.g., volume, velocity, variety, etc.). In particular, the current offering of the course covers, the conceptual model, the relational model, its standard query language SQL, and relational database design. Additionally, it covers NoSQL databases as well as ACID and BASE data management techniques, with emphasis on state-of-the-art platforms like HADOOP and SPARK.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 221 and Computer Science 319.

Data Science 479

Cloud-based Platforms for Data Science

Introduction to local and public cloud-based systems. Virtualization and containers. Creation, management, and cloud-based deployment of applications which solve data science problems. Privacy, security, and performance.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 471.

Data Science 499

Special Topics in Data Science

Data Science 501

Data Science Capstone

A substantial research project in the field of Data Science. The project will consist of all of the elements in the data cycle: collection, cleaning, exploratory analysis, statistical and computational analysis and presentation.
Course Hours: 3 units; (1.5-5)
Prerequisite(s): Data Science 311 or Computer Science 471; and 3 units from Data Science 305, Statistics 323, Biology 315, Sociology 315, Economics 395, Linguistics 560, Psychology 301 or 312.

Data Science 543

Deep Learning

Fundamental concepts in neural networks, convolutional and recurrent networks, as well as deep unsupervised learning. Common applications of neural networks and deep unsupervised learning.
Course Hours: 3 units; (3-2T)
Prerequisite(s): Data Science 443.

Data Science 599

Special Topics in Data Science

Graduate Courses

Data Science 601

Working with Data and Visualization

An introduction to fundamental data science concepts including basic data organization, data collection, and data cleaning. Provides a review of basic programming concepts in Python, as well as an introduction to problem-solving concepts including algorithmic complexity, recursion, vectorization, and regular expressions. Covers the fundamentals of data visualization and critical thinking.
Course Hours: 3 units; (3-0)
Prerequisite(s): Admission to the Graduate Certificate in Fundamental Data Science and Analytics, or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.

Data Science 602

Statistical Data Analysis

An introduction to the foundations of statistical inference including probability models for data analysis, classical and simulation-based statistical inference, and implementation of statistical models with R.
Course Hours: 3 units; (3-0)
Prerequisite(s): Admission to the Graduate Certificate in Fundamental Data Science and Analytics, or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.

Data Science 603

Statistical Modelling with Data

An introduction to the creation of complex statistical models, including exposure to multivariate model selection, prediction, the statistical design of experiments and analysis of data in R.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 602 and admission to the Graduate Certificate in Fundamental Data Science and Analytics or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.

Data Science 604

Working with Data at Scale

Explores the progression of data storage and processing, beginning with small, simple files and continuing to big, cloud-based databases. Data representation and simple file structures. An introduction to core database concepts with a variety of underlying data models, with a practical introduction to both SQL and NoSQL systems. Parallel and distributed storage and processing, including an introduction to cloud computing. Practical implications of storing and manipulating data at scale, with emphasis on ethics, privacy, and security.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601 and admission to the Graduate Certificate in Fundamental Data Science and Analytics or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.

Data Science 605

Actionable Visualization and Analytics

Introduces deeper tools, skills, and techniques for collecting, manipulating, visualizing, analyzing, and presenting a number of different common types of data. With a data life-cycle perspective, looks into data elicitation and preparation as well as the actual usage of data in a decision-making context. Introduces techniques for visualizing and supporting the interactive analysis and decision making on large complex datasets. Focus on critical thinking and good analysis practices to avoid cognitive biases when designing, thinking, analyzing, and making decisions based on data.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.

Data Science 606

Statistical Methods in Data Science

Design of surveys and data collection, bias and efficiency of surveys. Sampling weights and variance estimation. Multi-way contingency tables and introduction to generalized linear models with emphasis on applications.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.

Data Science 607

Statistical and Machine Learning

Latent variable models for clustering and dimension reduction. Parametric and nonparametric methods for regression and classification including naïve Bayes, decision trees, random forests, and boosting. Model assessment and selection. Deep learning.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.

Data Science 608

Developing Big Data Applications

Provides advanced coverage of tools and techniques for big data management and for processing, mining, and building applications that leverage large datasets. Addresses database and distributed storage design for both SQL and NoSQL systems, and focuses on the application of distributed computing tools to perform data integration, apply machine learning, and build applications that leverage big data. Students will also examine the security and ethical implications of large-scale data collection and analysis.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.

Data Science 609

Modelling of Financial and Energy Markets Data

Coverage of main tools and techniques for modelling, machine learning and statistical analysis of big data in financial and energy markets. Topics will include: a simple financial and energy market models; risk-free and risky assets data modelling; discrete-time and continuous-time financial and energy market data modelling (CRR, Black-Scholes); modelling of forwards, futures, swaps in financial and energy markets; risk-neutral valuation, options, option pricing with financial and energy markets data; world energy data trends-crude oil, natural gas, and electricity; renewable energy data modelling: wind, solar, etc.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603 and 604, and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Financial and Energy Markets Data Modelling.

Data Science 610

Advanced Modelling of Financial and Energy Markets Data

More advanced coverage of tools and techniques for modelling of financial and energy markets data. Topics will include: modelling of dynamics for forwards and futures in energy and options in financial markets data; Black-76 and Margrabe’s formulas for pricing of futures and options contracts; modelling paradigms beyond Black-76; data modelling with stochastic interest rates; one-, two- and three-factor Schwartz’s models (with stochastic convenience yield and interest rate) for pricing forward and futures data in energy markets; modelling of high-frequency and algorithmic trading data.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603 and 604, and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Financial and Energy Markets Data Modelling.

Data Science 611

Predictive Analytics

Overview of the basic concepts and techniques in predictive analytics as well as their applications for solving real-life business problems in marketing, finance, and other areas. Techniques covered in this course include: decision trees, classification rules, association rules, clustering, support vector machines, instance-based learning. Examples and cases are discussed to gain hands-on experience.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.

Data Science 612

Decision Analytics

Introduces fundamental concepts and modelling approaches to solve problems that are faced by decision makers in today’s fast-paced and data-rich business environment. Different decision alternatives are analyzed and evaluated with the use of computer models. Topics include the most commonly used applied optimization, simulation and decision analysis techniques. Extensive use will be made of appropriate computer software for problem solving, principally with spreadsheets.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.

Data Science 613

Introductory Data Analytics

Introduction to new tools for data analytics that can be used to discover, collect, organize, and clean the data to make it ready for analysis. Emphasis is placed on software tools used to interact with data sources and provision of user skills to create business applications that encompass a variety of business data sources; such as customers, suppliers, markets, competitors, and regulators. Software packages used to clean and organize the data for analysis will be introduced, as well as software to enable users’ understanding of the data that is collected.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.

Data Science 614

Advanced Data Analytics

Examination of tools and methods used in data analysis, including basic and advanced analytic tools, as well as machine learning techniques. One or more data analysis packages/programs are used to analyze different types of business data. Statistical and other analytic methods, such as data mining, machine learning and various techniques, and their application to business data analytics are explored.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604, 611 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.

Data Science 621

Advanced Statistical Modelling

An introduction to the fundamental statistical methods used in health data science including interpretation and communicating the results of these methods. Explores modelling using an epidemiological paradigm such as the assessment for modification and confounding. Introduces fundamental health research methods including study design and the evidence hierarchy.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.

Data Science 622

Machine Learning for Health Data Science

An introduction to the application of machine learning methods to problems in health data. The concepts of precision medicine and precision public health are introduced and the role of data science in these endeavors is explored. Using real examples from health data, various contemporary machine learning techniques are taught.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Antirequisite(s): Credit for Data Science 622 and Community Health Sciences 615 will not be allowed.

Data Science 623

Big Data in Health

Many of the major health data assets that exist in Alberta and Canada will be explored through hands-on experience with several datasets. Issues relating to access, confidentiality, privacy and data stewardship will be examined. Methodological challenges related to data linkage will be discussed. Students will work with large health databases including health administrative data, electronic medical record data and various other databases.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.

Data Science 624

Advanced Exploration and Visualization in Health

Explores the synthesis and summary of large volumes of information into interpretable and compelling results. Software packages useful for visualization of data are examined, including software for geographic information systems, augmented reality, and infographics. Data Science software commonly used in health industry is examined. Fundamental design principles are introduced to guide the approach to data presentation, communication, and interpretation.
Course Hours: 3 units; (3-0)
Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Antirequisite(s): Credit for Data Science 624 and Community Health Sciences 636 will not be allowed.

Data Science 691

Integrated Topics in Data Science and Analytics

Provides a framework for students to initiate, perform and successfully complete a real-world project in Data Science and Analytics. This includes problem identification and formulation; discussion of legal, social, and ethical issues in data-driven projects; holistic processing and application of data; communication and data storytelling skills as well as leadership skills. In addition, students will be exposed to emerging topics in Data Science and Analytics.
Course Hours: 6 units; (3-0)
Prerequisite(s): Admission to the Master of Data Science and Analytics and 24 units in 600-level Data Science courses.

Data Science 693

Professional Internship in Data Science and Analytics

Students will integrate professional competencies and advanced analytical tools and apply them to a specific domain.
Course Hours: 6 units; (200 hours)
Prerequisite(s): Data Science 691.
Antirequisite(s): Credit for Data Science 693 and 695 will not be allowed.
NOT INCLUDED IN GPA

Data Science 695

Research Internship in Data Science and Analytics

Exposure to advanced data analytics methods and research methods applied to subdomains of data science, including business analytics and big data problems in healthcare.
Course Hours: 6 units; (6-0)
Prerequisite(s): Data Science 691.
Antirequisite(s): Credit for Data Science 695 and 693 will not be allowed.
NOT INCLUDED IN GPA

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