Professional Master of Science in Big Data
丁香园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 丁香园AV section of the 丁香园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 丁香园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 30 units of graduate work. These units are divided into three sections: 15 credits of graduate course work; 12 credits of specialized lab work; 3 credits for co-op.
Course work
One of
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.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Joseph Peters |
Jan 6 鈥� Apr 13, 2015: Wed, 11:30 a.m.鈥�12:20 p.m.
Jan 6 鈥� Apr 13, 2015: Fri, 10:30 a.m.鈥�12:20 p.m. |
Burnaby Burnaby |
Complete all of
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.
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.
One of
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.
Advanced topics in the field of scientific and information visualization are presented. Topics may include: an introduction to visualization (importance, basic approaches and existing tools), abstract visualization concepts, human perception, visualization methodology, 2D and 3D display and interaction and their use in medical, scientific, and business applications. Prerequisite: CMPT 316, 461 or equivalent (by permission of instructor). Students with credit for CMPT 878 or 775 may not take this course for further credit.
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.
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.
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:
This course is one of two lab courses that are part of the Professional Master鈥檚 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 |
|---|---|---|---|
|
Anoop Sarkar |
Jan 6 鈥� Apr 13, 2015: Tue, 11:30 a.m.鈥�2:20 p.m.
Jan 6 鈥� Apr 13, 2015: Thu, 11:30 a.m.鈥�2:20 p.m. |
Burnaby Burnaby |
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 co-op term. With assistance from the co-op coordinator for this program, students will be expected to find a suitable industry partner for the co-op term. 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 semester following the co-op term.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the (residence, course work, academic progress, supervision, research competence requirement, completion time, and degree completion), as well as the specific requirements for the program in which they are enrolled, as shown above.