This program is offered through the Faculty of Mathematics and Science with participating academic units currently including the Department of Computer Science, the Department of Mathematics and Statistics and the Goodman School of Business Department of Finance, Operations, and Information Systems. Program Director Anteneh Ayanso Director, Co-op, Career and Experiential Education Cara Krezek |
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Administrative Assistants Department of Computer Science Amber Cecchini 905-688-5550, extension 3513 MacKenzie Chown, J314 Department of Mathematics and Statistics Jessica Morrissette 905-688-5550, extension 3300 MacKenzie Chown, J415 Department of Finance, Operations, and Information System Val DeSimone 905-688-5550, extension 4426 GSB 475D The Bachelor of Science in Data Sciences and Analytics (Honours) program (https://brocku.ca/mathematics-science/data-science/) bridges the gap between computational data sciences and business analytics by forming well-rounded graduates with strong core knowledge and skills in programming for big data, data infrastructures, computational statistics, data mining, business analytics, and management of information systems; graduates with the ability of telling the story through state-of-the-art visualization and the communication of business intelligence to support strategic decisions. Students in the program will acquire advanced knowledge and skills in area of specialization chosen from Financial Analytics or Computational Sciences (including parallel computing, machine learning, artificial intelligence, computational statistics, cybersecurity, financial risk management, fintech, computational finance, etc.). Students can be admitted into either of the honours or co-op option of the Concentration in Financial Analytics or the Concentration in Computational Data Sciences. |
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The application form for re-admission or to transfer into BSc in Data Sciences and Analytics (Declare or Change of Major Application form) is available online at brocku.ca/webfm_send/1400 or at the Office of the Registrar. The Declare or Change Major Application form to transfer into the BSc program must be completed and returned to the Office of the Registrar to be considered for admission to the program. To be eligible to transfer from another university program into the BSc in Data Sciences and Analytics program, a student must meet the following minimum requirements:
To be eligible for transfer from a two or three-year diploma into the BSc in Data Sciences and Analytics program, a student must meet the following minimum requirements:
To be eligible for re-admission in the BSc in Data Sciences and Analytics program, a student must meet all of the following requirements:
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The Co-op option of the Data Sciences and Analytics B.Sc. program combines academic and work terms over a period of four and one-half academic years. Students spend four terms in an academic setting studying the fundamentals of data sciences prior to their first work placement. Successful completion of courses in the core areas of data sciences and analytics provides the necessary academic background for the work experience. In addition to the current fees for courses in academic study terms, Data Sciences and Analytics Co-op students are assessed an administrative fee for each work term (see the Schedule of Fees). Each four-month co-operative education work term must be registered. Once students are registered in a co-op work term, they are expected to fulfill their commitment. If the placement accepted is for more than one four-month work term, students are committed to complete all terms. Students may not withdraw from or terminate a work term without permission from the Director, Co-op Program Office. The B.Sc. in Data Sciences and Analytics Co-op program designation will be awarded to those students who have honours standing and who have successfully completed a minimum of twelve months of Co-op work experience. |
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Concentration in Computational Data Sciences
Concentration in Financial Analytics
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Concentration in Computational Data Sciences
Concentration in Financial Analytics
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Note that not all courses are offered in every session. Refer to the applicable term timetable for details. # Indicates a cross listed course * Indicates primary offering of a cross listed course |
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Students must check to ensure that prerequisites are met. Students may be deregistered, at the request of the instructor, from any course for which prerequisites and/or restrictions have not been met. Programming for Big Data (also offered as COSC 2P08) Introduction to R and Python programming; functional programming; Building packages in R and Python: creation, documentation, testing, interfaces to other software; Objects; Basic data and computations; Data visualization and graphics using R and Python; Computing with text. Lectures, 3 hours per week; lab, 1 hour per week. Prerequisite(s): COSC 2P03 Completion of this course will replace previous assigned grade and credit obtained in COSC 2P08. Data Visualization and Intelligence (also offered as ITIS 3P31) Introduction to data visualization and business intelligence; Data preparation with Excel and Business Intelligence (BI) tools; Basic and Advanced Charts; Data connections; Shaping data; Maps to visualize spatial data; Effective data visualization design; Creation of dashboards and story points with data; Worksheets and dashboards formatting; Limitations and pitfalls of data visualization and stories. Lectures, 3 hours per week; lab, 1 hour per week. Restriction: open to BSc Data Science and Analytics majors. Prerequisite(s): ITIS 1P97, STAT (MATH) 1P98 or (MATH) 2P82 Corequisite(s): COSC 3P82 Completion of this course will replace previous assigned grade and credit obtained in ITIS 3P31. Storage and Retrieval of Big Data (also offered as ITIS 3P41) Design and building of enterprise-wide data warehouses for fast storage, retrieval and integration of big data; Design of distributed databases, including data extraction, transformation, loading replication, and concurrency; NoSQL, object-oriented, and multimedia databases; query languages and reduction of large datasets; Processing data with industry standard packages and codes; Production of graphics for analysis and interpretations. Lectures, labs, 3 hours per week. Prerequisite(s): COSC 2P08, one of ITIS 3P98 or COSC 3P32. Completion of this course will replace previous assigned grade and credit obtained in ITIS 3P41. Statistical Computing with R Use of R language for data manipulation, graphical exploration, and statistical analyses; Univariate and multivariate modelling with empirical data; Robust methods; Principal component analysis; Model validation; Spatial structure and analysis. Lectures, 3 hours per week; lab, 1 hour per week. Restriction: open to Data Sciences and Analytics majors only. Prerequisite(s): STAT (MATH) 1P98 or permission of the instructor. Capstone Project Experiential academic and practical learning opportunity with a faculty member; Group projects with industry and academic partners on the design, implementation, testing, and demonstration of solutions to a practical big data problem; Project documentation and story telling through reports, visualizations and presentations. Restriction: Open to Data Science and Analytics majors only Prerequisite(s): completion of 15 credits required by the program |
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2022-2023 Undergraduate Calendar
Last updated: August 22, 2022 @ 11:28AM