Data Science

Data drives decision-making across industries and organizations around the globe. Over the last ten years, the amount of data that has been created and processed has grown rapidly. As a result, data has become the most valuable resource in the world.

Our four-year Bachelor of Science in Data Science program is offered collaboratively through the Department of Computer Science and the Department of Mathematics and Statistics.

In our Data Science program, you will learn to explore, prepare, visualize, present, compute, and make predictions based on data. In addition, you will learn valuable skills like problem-solving, time management, teamwork, integrity, and communication that will benefit you throughout your career.

What is Data Science?

Data science combines aspects of mathematics, statistics, and computer science, such as analytics, machine learning, programming, and artificial intelligence to manage, interpret, and draw inferences from large data sets. By analyzing data, you can identify issues, offer solutions, and make strategic decisions for better outcomes.

Data science is especially important in today’s world because of the large volume of data collected in a variety of settings such as health, climate research, and biology. Graduates will be equipped with the knowledge base and skill set to pursue a career in data analysis.

Some of the data science courses at the U of R include:

CS 210 - Data Structures and Abstractions

This course introduces data abstraction, data structures and their implementations, the basics of algorithmic analysis, and the fundamental computing algorithms. Topics include stacks, queues, heaps, recursion, Master Theorem, asymptotic notation, computational complexity, empirical performance measurement, recursion-based sorting algorithms, hashing, and trees (including binary trees, B-trees, and AVL trees).

CS 320 - Introduction to Artificial Intelligence

Foundations and main methods of Artificial Intelligence. Problem characteristics and spaces. Search and optimization techniques with a focus on uninformed and heuristic algorithms. Two player games and constraint satisfaction. Modelling and simulation. Comparison of logic based, fuzzy, and probabilistic reasoning and knowledge representation methodologies. Machine learning: learning tasks, inductive learning, statistical-based learning, over-fitting, accuracy

CS 340 - Advanced Data Structures and Algorithm Design

Fundamental algorithms: depth- and breadth-first traversals, pattern matching, and graph algorithms. Algorithmic strategies: brute-force, greedy, divide-and-conquer, backtracking, branch-and-bound, dynamic programming, and randomized. Algorithm analysis, complexity theory, performance evaluation. Parallelism: fundamentals, algorithms, communication.

CS 375 - Database and Information Retrieval

Information management: concepts and applications. Motivation for database systems. Components of database systems. Data modeling: conceptual, spreadsheet, relational, object-oriented, and semi-structured models. Querying and database query languages. Caching and transaction processing. Other topics include distributed databases, physical database design, and information retrieval systems.

CS 465 - Data Mining

Knowledge Discovery from Data (KDD). Topics include knowledge discovery, data preparation, data warehousing, pattern mining, classification and regression, cluster analysis, outlier detection, mining complex data types.