Data Sciences and Analytics

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

Academic Advisor

Janelle Montague

General Information

Administrative Assistants

Department of Computer Science

Brittani Allan

905-688-5550, extension 3513

MacKenzie Chown, J314

Department of Mathematics and Statistics

Alan Liu

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.

Re-Admission and Transfer Eligibility

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 on the Office of the Registrar's Forms and self-service page. 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:

	A minimum 70 percent in each transferred university level course in business, computer science, mathematics and statistics.
	A minimum 70 percent overall average.
	Meeting English language proficiency 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:

	A minimum of 80 percent overall average.
	Meeting English language proficiency 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:

	A minimum 65 percent grade in transferred university level courses in business, computer science, mathematics and statistics.
	A minimum 70 percent overall average.
	Completion of at least two and one-half credits required in the program.

Co-op Option

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.

Program Notes

Program Common Core Courses

Specialty Core Courses

Concentration in Computational Data Sciences

COSC 1P50, 2P03, 2P13, 2P89, 3P32, 3P71, 4P01, MATH 1P05, 1P06, 1P66, 1P67, 2P03, STAT 2P81, 2P82, 3P82, 3P86, 4P87

Concentration in Financial Analytics

ACTG 1P91, DASA 3P41, 3P87, ECON 2P91, FNCE 2P91, 3P93, 3P96, 4P04, 4P16, ITIS 3P91, 3P92, 3P98, 4P21, 4P23, 4P25, MATH 1P97, MGMT 1P93, OBHR 2P51, OPER 2P91, STAT 1P98

Specialty Elective Courses

Concentration in Computational Data Sciences

Four credits from: COSC 2P12, 3P03, 3P93, 3P94, 3P96, 4P32, 4P42, 4P50, 4P80, DASA 3P41, ENTR 2P51, ITIS 3P92, 4P21, 4P23, 4P25, MATH 2P75, 2P91, 2P94, 3P72, 3P75, 3P99, MGMT 1P93, OBHR 2P51, STAT 3P81, 3P85, 4P81, 4P82

Concentration in Financial Analytics

Three credits from: ACTG 2P12, COSC 2P03, 3P71, 4P01, ENTR 2P51, ITIS 4P26, MATH 1P67, 2P75, 2P91, 2P94, 3P99, MKTG 2P51, OPER 3P92, 3P94, 4P31, 4P41, STAT 4P87

Honours Program

Concentration in Computational Data Sciences

Year 1

	COSC 1P02 and 1P03
	ECON 1P91 and 1P92
	ITIS 1P97
	MATH 1P05, 1P06, 1P12, 1P66, and 1P67

Year 2

	COSC 1P50, 2P03, and 2P13
	ITIS 2P91
	MATH 2P03
	STAT 2P81 and 2P82
	one-half credit from COSC 2P12, ENTR 2P51, MGMT 1P93, MATH 2P75, 2P94, OBHR 2P51 (see program notes 2, 4)
	one Humanities context credit

Year 3

	DASA 2P08 and 3P31
	COSC 2P89, 3P32, 3P71, and 4P01
	STAT 3P82 and 3P86
	one-half credit from COSC 3P03, MATH 2P91, 2P94, 3P75, STAT 3P85 (see program notes 2,4)
	one-half elective credit (see program notes 2, 4)

Year 4

	DASA 4F01
	STAT 4P87
	three credits from COSC 3P93, 3P94, 3P96, 4P32, 4P42, 4P50, 4P80, DASA 3P41, ITIS 3P92, 4P21, 4P23, 4P25, MATH 3P72, 3P99, STAT 3P81, 4P81, 4P82 (see program notes 2, 4)
	one-half elective credit (see program notes 2, 4)

Concentration in Financial Analytics

Year 1

	ACTG 1P91
	COSC 1P02 and 1P03
	ECON 1P91 and 1P92
	ITIS 1P97
	MATH 1P12 and 1P97
	MGMT 1P93
	STAT 1P98

Year 2

	FNCE 2P91
	ITIS 2P91 and 3P98
	OBHR 2P51
	OPER 2P91
	one credit from ACTG 2P12, ENTR 2P51, MATH 1P67, 2P75, 2P94, MKTG 2P51 (see program notes 2, 4)
	one Humanities context credit
	one-half Sciences context credit

Year 3

	DASA 2P08, 3P31, and 3P87
	ECON 2P91
	FNCE 3P93 and 3P96
	ITIS 3P92 and 4P21
	one credit from COSC 2P03, 4P01, MATH 2P91, OPER 3P92, 3P94, 4P31 (see program notes 2, 4)

Year 4

	DASA 4F01 and 3P41
	FNCE 4P04 and 4P16
	ITIS 3P91, 4P23, and 4P25
	one credit from COSC 3P71, 4P01, ITIS 4P26, MATH 3P99, OPER 3P94, 4P31, 4P41, STAT 4P87 (see program notes 2, 4)

Data Sciences and Analytics Co-op (Honours Only)

Concentration in Computational Data Sciences

Year 1

	COSC 1P02 and 1P03
	ECON 1P91 and 1P92
	ITIS 1P97
	MATH 1P05, 1P06, 1P12, 1P66, and 1P67

Year 2

	COSC 1P50, 2P03, and 2P13
	ITIS 2P91
	MATH 2P03
	STAT 2P81 and 2P82
	SCIE 0N90
	one-half credit from COSC 2P12, ENTR 2P51, MGMT 1P93, MATH 2P75, 2P94, OBHR 2P51 (see program notes 2, 4)
	one Humanities context credit

Spring/Summer Sessions:

DASA 0N01 and 2C01

Year 3

	DASA 2P08 and 3P31
	COSC 2P89, 3P32, 3P71, and 4P01
	STAT 3P82 and 3P86
	one-half credit from COSC 3P03, MATH 2P91, 2P94, 3P75, STAT 3P85
	one-half elective credit (see program notes 2, 4)

Spring/Summer Sessions:

DASA 0N02 and 2C02

Year 4

Fall Term:

DASA 0N03 and 2C03

Winter Term:

	STAT 4P87
	one and one-half credits from COSC 3P93, 3P94, 3P96, 4P50, 4P80, ITIS 4P23, STAT 3P81, 4P82(see program notes 2, 4)

Spring/Summer Sessions:

DASA 4F01

Year 5

Fall Term:

	one and one-half credits from COSC 4P32, 4P42, DASA 3P41, ITIS 3P92, 4P21, 4P25, MATH 3P72, 3P99, STAT 4P81 (see program notes 2, 4)
	one-half elective credit (see program notes 2, 4)

Concentration in Financial Analytics

Year 1

	ACTG 1P91
	COSC 1P02 and 1P03
	ECON 1P91 and 1P92
	ITIS 1P97
	MATH 1P12 and 1P97
	MGMT 1P93
	STAT 1P98

Year 2

	FNCE 2P91
	ITIS 2P91 and 3P98
	OBHR 2P51
	OPER 2P91
	SCIE 0N90
	one credit from ACTG 2P12, ENTR 2P51, MATH 1P67, 2P75, 2P94, MKTG 2P51 (see program notes 2, 4)
	one Humanities context credit
	one-half Sciences context credit

Spring/Summer Sessions:

DASA 0N01 and 2C01

Year 3

	DASA 2P08, 3P31, and 3P87
	ECON 2P91
	FNCE 3P93 and 3P96
	ITIS 3P92 and 4P21
	one credit from COSC 2P03, 4P01, MATH 2P91, OPER 3P92, 3P94, 4P31 (see program notes 2, 4)

Spring/Summer Sessions:

DASA 0N02 and 2C02

Year 4

Fall Term:

DASA 0N03 and 2C03

Winter Term:

	FNCE 4P04 and 4P16
	ITIS 3P91 and 4P23
	one-half credit from COSC 4P01, STAT 4P87, OPER 3P94, 4P31 (see program notes 2, 4)

Spring/Summer Sessions:

DASA 4F01

Year 5

Fall term:

	DASA 3P41
	ITIS 4P25
	one-half credit from COSC 3P71, ITIS 4P26, MATH 3P99, OPER 4P41(see program notes 2, 4)

Course Descriptions

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

Prerequisites and Restrictions

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.

*DASA 2P08

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 1P03.

*DASA 3P31

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.

*DASA 3P41

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.

#DASA 3P87

Statistical Computing with R

(also offered as STAT 3P87)

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.

DASA 4F01

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