SciNet Events |

September,2020 | |
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Tue 29th Sep 11:00 am 12:00 pm | This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture. Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online |

Tue 29th Sep 12:00 pm 1:30 pm | In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online |

October,2020 | |

Thu 1st Oct 11:00 am 12:00 pm | This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture. Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online |

Thu 1st Oct 12:00 pm 1:30 pm | In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online |

Tue 6th Oct 11:00 am 12:00 pm | This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture. Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online |

Tue 6th Oct 12:00 pm 1:30 pm | In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online |

Thu 8th Oct 11:00 am 12:00 pm | Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online |

Thu 8th Oct 12:00 pm 1:30 pm |