¶¡ÏãÔ°AV

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¶¡ÏãÔ°AV Calendar | Fall 2017

Big Data

Master of Science

¶¡ÏãÔ°AV Requirements

To qualify for admission to the M.Sc. program in Big Data, a student must satisfy the university admission requirements for a master's program as stated in Section 1.3.3 of the Graduation ¶¡ÏãÔ°AV section of the ¶¡ÏãÔ°AV calendar, and the student must hold a bachelor's degree, or equivalent in Computing Science or a related field, with a cumulative grade point average (GPA) of 3.0 (on a scale of 0.0 - 4.0) or the equivalent.

The School's Graduate ¶¡ÏãÔ°AVs Committee may offer, at its discretion, M.Sc. admission to exceptional students without an undergraduate degree in Computing Science or a related field. Minimally we require demonstrated competence in computing science at the third year level equivalent to CMPT 300 (Operating Systems 1), CMPT 307 (Data Structures and Algorithms) and CMPT 354 (Database Systems).

Students who do not have the proper background in Computing Science may take the three courses listed above in the Summer semester before the Fall cohort begins and then join the M.Sc. program in Big Data.

Program Requirements

Students will complete a minimum of 30 units of graduate work. These units are divided into three sections: a minimum of 15 units of graduate course work; 12 units of specialized lab work; 3 to 6 units for co-op.

Course work

One of

CMPT 705 - Design and Analysis of Algorithms (3)

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

Section Instructor Day/Time Location
Leonid Chindelevitch
Sep 5 – Dec 4, 2017: Mon, Wed, Fri, 12:30–1:20 p.m.
Burnaby
or CMPT 881 - Special Topics in Theoretical Computing Science (3)

Complete all of

CMPT 726 - Machine Learning (3)

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

Section Instructor Day/Time Location
Oliver Schulte
Sep 5 – Dec 4, 2017: Tue, 12:30–1:50 p.m.
Sep 5 – Dec 4, 2017: Thu, 12:30–1:50 p.m.
Burnaby
Burnaby
CMPT 741 - Data Mining (3)

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

Section Instructor Day/Time Location
Ke Wang
Sep 5 – Dec 4, 2017: Mon, 11:30 a.m.–12:20 p.m.
Sep 5 – Dec 4, 2017: Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
Burnaby
CMPT 886 - Special Topics in Operating Systems (3)

Section Instructor Day/Time Location
William Sumner
Sep 5 – Dec 4, 2017: Tue, 2:30–4:20 p.m.
Sep 5 – Dec 4, 2017: Thu, 2:30–3:20 p.m.
Burnaby
Burnaby

One of

IAT 814 - Knowledge, Visualization and Communication (3)

Provides a cognitive and computational framework for understanding and designing graphical and visual representations. Investigates several psychological and computational models of diagram processing, and explores diverse interactive graphical systems.

Section Instructor Day/Time Location
Wolfgang Stuerzlinger
Sep 5 – Dec 4, 2017: Mon, 12:30–1:50 p.m.
Sep 5 – Dec 4, 2017: Wed, 10:30–11:50 a.m.
Surrey
Surrey
Lyn Bartram
Sep 5 – Dec 4, 2017: Wed, Fri, 9:30–10:50 a.m.
Burnaby
CMPT 711 - Bioinformatics Algorithms (3)

Fundamental algorithmic techniques used to solve computational problems encountered in molecular biology. This area is usually referred to as Bioinformatics or Computational Biology. Students who have taken CMPT 881 (Bioinformatics) in 2007 or earlier may not take CMPT 711 for further credit.

Section Instructor Day/Time Location
Leonid Chindelevitch
Sep 5 – Dec 4, 2017: Mon, Wed, Fri, 9:30–10:20 a.m.
Burnaby
CMPT 825 - Natural Language Processing (3)

In this course, theoretical and applied issues related to the development of natural language processing systems and specific applications are examined. Investigations into parsing issues, different computational linguistic formalisms, natural language syntax, semantics, and discourse related phenomena will be considered and an actual natural language processor will be developed.

Section Instructor Day/Time Location
Anoop Sarkar
Sep 5 – Dec 4, 2017: Fri, 1:30–4:20 p.m.
Burnaby
Anoop Sarkar
TBD
CMPT 829 - Special Topics in Bioinformatics (3)

Examination of recent literature and problems in bioinformatics. Within the CIHR graduate bioinformatics training program, this course will be offered alternatively as the problem-based learning course and the advanced graduate seminar in bioinformatics (both concurrent with MBB 829). Prerequisite: Permission of the instructor.

CMPT 880 - Special Topics in Computing Science (3)

This course aims to give students experience to emerging important areas of computing science. Prerequisite: Instructor discretion.

CMPT 882 - Special Topics in Artificial Intelligence (3)

Section Instructor Day/Time Location
Anoop Sarkar
Sep 5 – Dec 4, 2017: Mon, Wed, 2:30–3:50 p.m.
Burnaby
CMPT 886 - Special Topics in Operating Systems (3)

Section Instructor Day/Time Location
William Sumner
Sep 5 – Dec 4, 2017: Tue, 2:30–4:20 p.m.
Sep 5 – Dec 4, 2017: Thu, 2:30–3:20 p.m.
Burnaby
Burnaby
CMPT 888 - Special Topics in Computer Graphics, HCI, Vision and Visualization (3)

Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.

CMPT 894 - Directed Reading (3)

Section Instructor Day/Time Location
TBD
TBD
TBD

Lab Work

Students will take the following two lab courses worth 6 credits each. Only students enrolled in the professional master's in Big Data will be permitted to enroll in these courses:

CMPT 732 - Programming for Big Data 1 (6)

This course is one of two lab courses that are part of the Professional Master’s Program in Big Data in the School of Computing Science. This lab course aims to provide students with the hands-on experience needed for a successful career in Big Data in the information technology industry. Many of the assignments will be completed on massive publically available data sets giving them appropriate experience with cloud computing and the algorithms and software tools needed to master programming for Big Data. Over 13 weeks of lab work and 12 hours per week of lab time, the students will obtain a solid background in programming for Big Data.

Section Instructor Day/Time Location
Sep 5 – Dec 4, 2017: Mon, 9:00–10:50 a.m.
Burnaby
G201 Sep 5 – Dec 4, 2017: Tue, Thu, 9:00–10:50 a.m.
Burnaby
G202 Sep 5 – Dec 4, 2017: Tue, Thu, 3:00–4:50 p.m.
Burnaby
G203 Sep 5 – Dec 4, 2017: Wed, Fri, 9:00–10:50 a.m.
Burnaby
CMPT 733 - Programming for Big Data 2 (6)

This course is one of two lab courses that are part of the Professional Masters Program in Big Data in the School of Computing Science. This lab course aims to provide students with the hands-on experience needed for a successful career in Big Data in the information technology industry. Many of the assignments will be completed on massive publically available data sets giving them appropriate experience with cloud computing and the algorithms and software tools needed to master programming for Big Data. Over 13 weeks of lab work and 12 hours per week of lab time, and building on the previous lab course CMPT 731, the students will obtain a solid background in programming for Big Data. Prerequisite: CMPT 732: Programming for Big Data 1.

Co-op

A co-op internship is an integral part of this program. Students will register for one or two co-op terms. With assistance from the co-op coordinator for this program, students will be expected to find a suitable industry partner for the co-op placement. The student may instead choose to conduct research into Big Data at one of the various Computing Science research labs as a paid research assistant to satisfy their co-op requirement. In extenuating circumstances, a student may appeal to the program director to take an elective course from the list of electives for this program instead of a co-op. Students are required to enroll in at least one of the required courses in the term following the co-op term(s).

Academic Requirements within the Graduate General Regulations

All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.