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Friday March 24, 2017 - 13:46

SciNet Certificate Program

Since 2012, SciNet's Certificate Program offer recognition to the attendees of our training sessions and courses on scientific technical computing and high performance computing.

There are currently three certificate programs:

Requirements for these certificates are based on credit-hours of SciNet courses successfully completed. For a short course (typically a day long or shorter, with no between-course homework), a lecture hour counts as one credit hour; for a long course with homework due between sessions, a lecture hour counts as 1.5 credit hours. Note that credits will not be given for attending only part of a course.

Details on the three certificate programs can be found below. If you are a SciNet user, you can login to see your progress towards these certificates.


1. Certificate in Scientific Computing

Scientific computing is now an integral part of the scientific endeavour. It is an interdisciplinary field that combines computer science, software development, physical sciences and numerical mathematics. This certificate indicates that the holder has successfully completed at least 36 credit-hours worth of completed SciNet courses in general scientific computing topics.

Required Credits: 36 credits

Eligible courses:

EES1137 Quantitative Applications for Data Analysis (Winter 2017)  8 credits
PHY1610 Scientific Computing for Physicists (Winter 2017)  36 credits

Past eligible courses in the last year:

SCMP101 Introduction to the Linux Shell  3 credits
HPC243 Debugging (Ontario Summer School July 2016)  3 credits
HPC253 Visualization (Ontario Summer School July 2016)  6 credits
HPC111 Python for High Performance Computing (Ontario Summer School July 2016)  3 credits
SCMP111 Python for Scientific Computing (Ontario Summer School July 2016)  3 credits
SCMP171 R for Data Science (Ontario Summer School July 2016)  3 credits
DAT123 Storage and I/O in Large Scale Scientific Projects (Sept. 2016)  2 credits
SCMP112 Intro to Scientific Computing with Python (Nov.2016)  12 credits
DAT121 Research Data Management (May 2016)  1 credits


2. Certificate in High Performance Computing

High Performance Computing, or supercomputing, is using the largest available computers to tackle big problems that would otherwise be intractable. Such computational power is needed is a wide range of fields, from bioinformatics to astronomy, and big data analytics. Since the largest available computers have a parallel architecture, using and programming high performance computing applications requires a specialized skill level. This certificate indicates that the holder has taken at least 36 credit-hours worth of successfully completed courses in high performance computing topics.

Required Credits: 36 credits

Eligible courses:

HPC105 Intro to SciNet (May. 2017)  1 credits
PHY1610 Scientific Computing for Physicists (Winter 2017)  10 credits

Past eligible courses in the last year:

HPC243 Debugging (Ontario Summer School July 2016)  1 credits
HPC133 Programming GPUs with CUDA (Ontario Summer School July 2016)  12 credits
HPC111 Python for High Performance Computing (Ontario Summer School July 2016)  3 credits
HPC123 Programming Clusters with MPI (Ontario Summer School July 2016)  9 credits
HPC171 Parallel R for Data Science (Ontario Summer School July 2016)  3 credits
HPC113 Shared Memory Programming with OpenMP (Ontario Summer School July 2016)  6 credits
HPC101 Intro to HPC and SciNet (Ontario Summer School July 2016)  3 credits
HPC162 Advanced Parallel Scientific Computing (Sept. 2016)  12 credits


3. Certificate in Data Science

The SciNet Certificate in Data Science attests that the holder has taken successfully completed at least 36 credit-hours of data science-related SciNet courses. Topics of these courses include "Hadoop workshop", "Scalable data analysis with R / Python", "Database Basics", "Visualization", "Machine Learning", and I/O

Required Credits: 36 credits

Eligible courses:

DAT211 Introduction to Neural Network Programming  3 credits
EES1137 Quantitative Applications for Data Analysis (Winter 2017)  28 credits

Past eligible courses in the last year:

HPC253 Visualization (Ontario Summer School July 2016)  6 credits
SCMP111 Python for Scientific Computing (Ontario Summer School July 2016)  3 credits
HPC171 Parallel R for Data Science (Ontario Summer School July 2016)  3 credits
SCMP171 R for Data Science (Ontario Summer School July 2016)  3 credits
DAT123 Storage and I/O in Large Scale Scientific Projects (Sept. 2016)  6 credits
SCMP112 Intro to Scientific Computing with Python (Nov.2016)  4 credits
DAT172 Introduction to Data Analysis with R (Oct.2016)  12 credits
DAT121 Research Data Management (May 2016)  3 credits

About the Certificates


Ever since SciNet's operations started 2009, we have has been teaching courses on scientific technical computing, high performance computing, and data analysis for the Toronto-area research community. Since December 2012, SciNet offers recognition to attendees of these training events in the form of SciNet Certificates. Requirements for these certificates are based on credit-hours of SciNet courses successfully completed. For a short course (typically a day long or shorter, with no between-course homework), a lecture hour counts as one credit hour; for a long course with homework due between sessions, a lecture hour counts as 1.5 credit hours.

What are the prerequisites?

Some requirements such as programming skills vary per course; these are announced when the courses are announced, and are listed on the courses pages on this site. Many courses have a hands-on component that requires participants to bring their laptop.

Who can participate in the certificate program?

SciNet courses and certificates are open to all SciNet users (for some courses, a SciNet account is not necessary, but these are exceptions). In general, any academic researcher from a Canadian research institution with significant high performance computing requirements to support his or her research may apply for an account on SciNet. For information on how to get an account, go to http://www.scinethpc.ca/2011/09/getting-a-scinet-account.

Do the certificates count as University credentials?

No, these certificates are not University credentials, and will not appear on transcripts. However, four of our courses (SCMP112 Research Computing with Python, SCMP122 Scientific Software Development, SCMP132 Numerical Tools for Physical Scientists, and HPC162 High Performance Scientific Computing) are offered in cooperation with the Astronomy, Physics and Chemistry departments of the University of Toronto as graduate minicourses, and can count towards course credit; interested students in other departments are encouraged to contact SciNet and their graduate coordinator.

In the winter of 2016, we will also be teaching "PHY1610 Scientific Computing for Physicists" as part of Uot's Physics Department graduate program. UoT graduate students in a different departments that allow to take physics courses as part of your program, should qualify to take this course for credit as well, but should confirm this with to their graduate coordinator.

Can I attend the courses online?

Although we are looking into this, currently, this is not possible. You can only get credit for a course by physically attending it. Recordings and slides of many of the courses are, however, posted on this site, and on the SciNet wiki.

How do obtain the certificate?

Upon completion of your certificate requirements, you must request your certificate by email to courses@scinet.utoronto.ca with the list of SciNet courses on scientific computing that you have taken.

What do the course numbers mean?

Courses that can be taken for a certificate will have a designation of the form ABCxyz, where ABC signifies the type of certificate that the course counts towards (currently SCMP or HPC for Scientific Computing or High Performance Computing, respectively). Of the three-digit xyz part, the first digit expresses whether the course is basic (x=1) or more advanced (x=2), the second (y) indicates the topic, while the third indicates the format (e.g, z=1 is typically a short lecture, z=2 is a graduate-style mini-course, z=3 is one or two full days, and z=4 is a longer multiday course).


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