SciNet Certificate Program

May 13, 2013 in for_researchers, for_users

SciNet has been teaching courses on scientific technical computing and high performance computing for the Toronto-area research community since 2009. Since 2013, it offers recognition to attendees in the form of SciNet Certificates. There are currently three certificate offerings: A Certificate in Scientific Computing, a Certificate in High Performance Computing, and a Certificate in Data Science.

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.

Prerequisites

Some requirements such as programming skills vary per course; these are announced when the courses are announced, on SciNet’s course website: https://courses.scinet.utoronto.ca.

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 httsp://www.scinethpc.ca/getting-a-scinet-account. Students at the University of Toronto that are not SciNet users (yet), can sometimes get a temporary account to take the courses.

Do the certificates count as University credits for my graduate degree?
No, the SciNet Certificates are not University credits, and will not appear on transcripts. However, several SciNet courses are offered in partnership with other departments at the University of Toronto for graduate credit.

Can I attend the courses online?
Recordings and slides of most courses are posted on the SciNet education site afterwards, so they could be watched remotely. However, only those SciNet courses with graded homework can be taken for SciNet certificate credits remotely, while for the others you can only get credit by physically attending.

Certificate in Scientific Computing

Scientific computing is now an integral part of the scientific endevour. 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 coursework in general scientific computing topics such as software development, version control, testing, visualization, and data management.

Requirements

  • A total of 36 credit-hours or more from the list of SciNet courses on scientific computing (see below).
  • Upon completion of your certificate requirements, you can request your certificate by emailing support@scinet.utoronto.ca (we are working on automating this process).

Recent Courses that count towards the Scientific Computing Certificate

  • Advanced Linux Command Line (3 credits)
  • Advanced Shell Programming (3 credits)
  • Intro to Linux Shell (May 2019) 3 credits
  • Introduction to Computational BioStatistics with R (8 credits)
  • Introduction to Programming with Python (8 credits)
  • Numerical Computing with Python (16 credits)
  • Quantitative Applications for Data Analysis ( 8 credits)
  • Scientific Computing for Physicists (28 credits)

The latest version of this list can be found on the education site.

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 successfully completed at least 36 credit-hours of coursework in high performance computing topics such as programming models like OpenMP, MPI, CUDA or parallel development tools like debuggers.

Requirements

  • A total of 36 credit-hours or more from the list of SciNet courses on high performance computing (see below).
  • Upon completion of your certificate requirements, you can request your certificate by emailing support@scinet.utoronto.ca (we are working on automating this process).

Recent Courses that count towards the High Performance Computing Certificate

  • Intro to SciNet/Niagara (1 credit)
  • Shared Memory Programming with OpenMP (part of the Ontario Summer School June – 6 credits)
  • Programming Clusters with MPI (part of the Ontario Summer School June – 9 credits)
  • Programming GPUs with CUDA (part of Ontario Summer School – 12 credits)
  • Intro to HPC and SciNet (part of Ontario Summer School June – 3 credits)
  • Advanced Parallel Scientific Computing (12 credits)
  • Scientific Computing for Physicists (8 credits)

The latest version of this list can be found on the education site.

Certificate in Data Science

To reflect the growing trend in data-driven science SciNet is pleased to announce the addition of a new Certificate program, focused on Data Science. To earn the SciNet Certificate in Data Science, users or students need to take at least 36 credit-hours of data science related SciNet courses.

Requirements

  • A total of 36 credit-hours or more from the list of SciNet courses on data science (see below).
  • Upon completion of your certificate requirements, you can request your certificate by emailing support@scinet.utoronto.ca (we are working on automating this process).

Recent Courses that count towards the Data Science Certificate

  • Advanced Neural Networks (4 credits)
  • Introduction to Computational BioStatistics with R (28 credits)
  • Machine Learning with Python (3 credits)
  • Neural Network Programming (16 credits)
  • Quantitative Applications for Data Analysis (28 credits)
  • Relational Database Basics( 3 credit)
  • Visualization Suites (3 credits)

The latest version of this list can be found on the education site.

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