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ACENET has the following training sessions coming up that may be of interest to faculty and students. The sessions are online and free of charge.

Cloud From A to Z
18, 20, 25, 27 October, 1300-1600hrs Atlantic / 1330-1630hrs NL

This is an intermediate level series. Cloud computing provides great flexibility, allowing complete control of the computing environment. In addition, the environment can be copied, backed up, created and recreated in an automated way. In these lessons, we will start you on the path towards making use of the great flexibility and power of cloud computing. We will be using the popular static website generator Jekyll. This is an in-depth workshop for those with no prior cloud experience, at the end of which you will have a virtual machine and a Jekyll website. Prerequisite: Completion of Software Carpentry's Unix Shell, or similar experience. This workshop will have two options: An Instructor Led and Self-Study option. We recommend that should you choose the Self-Study option that you have some prior knowledge of the topics to be pilipili漫画ful.聽

Introduction to MATLAB
1 November, 1300-1500hrs Atlantic / 聽1330-1530hrs NL

Through live demonstrations and examples, learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Topics will include: accessing data from many sources; using interactive tools for iterative exploration, design, and problem solving; automating and capturing your work in easy-to-write scripts and programs; and sharing your results with others by automatically creating reports. This session is designed for those 聽new to MATLAB, or more experienced users, as it will include some tips and tricks.

Introduction to Deep Learning
2 November, 1300-1600hrs Atlantic, 1330-1630hrs NL

This tutorial delivered by the Acadia Institute for Data Analytics and hosted by ACENET, is a gentle hands-on introduction to developing predictive models using deep learning artificial neural networks. It provides a high-level overview of the key elements of neural networks and deep learning (BP, CNN, LSTM), along with recent advances that allow deep networks to solve challenging problems such as object recognition in images (e.g. classification of animal or letter) and sequence prediction (e.g. next word in a sentence, like Google auto-complete). Participants will build their own deep models using prepared software (Keras and Tensorflow) working in the browser. All necessary code is provided, however a basic level of Python programming experience is needed.聽

Using MATLAB with Python
3 November, 1300-1500hrs Atlantic / 1330-1530hrs NL

One of the challenges in software development is to integrate different technologies or product stacks efficiently, streamlining development and facilitating collaboration between teams. In this session we will discuss how to work together with Python and MATLAB, thus expanding the possibilities when carrying out data science projects. Highlights include: calling Python libraries directly from MATLAB; calling a live MATLAB session from Python; and, packaging MATLAB analytics as royalty-free .py libraries.

AI with MATLAB: Part 1 鈥� 聽Machine Learning with Signals
8 November, 1300-1500hrs Atlantic / 1330-1530hrs NL

Machine learning (ML) algorithms use computational methods to 鈥渓earn鈥� information directly from data without relying on a predetermined equation as a model. In this hands-on workshop, you will use MATLAB to apply ML techniques to Signal data. Topics include: fundamentals of ML (supervised learning, feature extraction, and hyperparameter tuning); building and evaluating ML models for classification and regression of signals; automatic hyperparameter tuning and feature selection to optimize model performance; and, deploying ML models.

HSS Working with Data Series: Introduction to Spreadsheets
9 November, 1300-1600hrs Atlantic / 1330-1630hrs NL

This is a hands-on introductory workshop focused on fostering best practices for data organization in spreadsheets. Participants will learn how to organize their data to prioritize clarity, reproducibility, and interoperability, such that they can seamlessly load their data later into an analysis program. The spreadsheet programs covered will be Microsoft Excel and Google Sheets. The examples explored will be from the field of Social Sciences, but the principles are relevant for any discipline that collects data in spreadsheets. No previous experience with spreadsheets or programming is required.

AI with MATLAB: Part 2 鈥� 聽Deep Learning with Signals
10 November, 1300-1500hrs Atlantic / 1330-1530hrs NL

In this hands-on workshop, you will learn how to apply various Deep Learning (DL) techniques to biomedical signal data using MATLAB. You will discover tools and fundamental approaches for developing advanced predictive models. We will cover the complete AI pipeline from signal exploration to AI modeling to deployment. You will write code and use MATLAB Online to: annotate time series biomedical signals automatically; apply advanced signal pre-processing techniques for feature extraction; train deep learning models using CNNs and LSTMs; and, discuss interoperability with Python frameworks.

HSS Working with Data Series: Introduction to Regular Expressions
16 November, 1300-1600hrs Atlantic / 1330-1630hrs NL

This is an introductory lesson adapted from the that introduces people with library- and information-related roles, or those in the Humanities and Social Sciences professions that work with data, to using regular expressions. Regular expressions are a concept and an implementation used in many different programming environments for sophisticated pattern matching. They are an incredibly powerful tool that can amplify your capacity to find, manage, and transform data and files. The lesson provides background on the regular expression language and how it can be used to match and extract text and to clean data. No previous experience with programming is required.

Using Git Tools to Manage File Changes and Collaborate: Version Control
17 November, 1200-1600hrs Atlantic / 1230-1630hrs NL

Version control is the practice of managing and sharing changes to documents, programming code, websites or any other files to keep track of what鈥檚 been changed, by whom, when and why. All previous versions of files are saved and you can even revert to a previous version. Git-portal sites, like GitHub or GitLab, offer many useful features to facilitate collaborative development. In this beginner level session, we will show you how to create a repository, record changes to files, explore and restore from the recorded history and how to resolve conflicts (when one member overwrites another鈥檚 changes).

HSS Working with Data Series: OpenRefine
23 November, 1300-1600hrs Atlantic / 1330-1630hrs NL

This adapted lesson introduces people working in Humanities, Social Sciences, and library- and information-related roles to working with data in OpenRefine. OpenRefine can be used to standardize and clean data across your file, and is most useful when working with a comma separated values file (csv) or a tab delimited file (tsv). It can help you get an overview of a data set; resolve inconsistencies in a data set; help you split data up into more granular parts; and more. At the conclusion of the lesson you will understand what the OpenRefine software does and how to use the OpenRefine software to work with data files. This lesson will be co-facilitated by an academic librarian who will give real life examples of using OpenRefine in their work. No previous experience with the software is required.

Using Git Tools to Manage File Changes and Collaborate: Collaboration Platforms
24 November, 1200-1600hrs Atlantic / 1230-1630hrs NL

This session will focus on collaborative development workflows using Git-collaboration sites like GitHub, GitLab or Bitbucket and will demonstrate how to work with branches, issue tracking, contribute to projects using pull-/merge-requests, code-review, how to run CI/CD-pipelines and use other common features of these platforms. Prerequisite: basic experience using Git or participation in the 17 November workshop.

HSS Working with Data Series: Introduction to Research Data Management
30 November, 1300-1600hrs Atlantic / 1330-1630hrs NL

This is an introductory workshop to research data management for Humanists and Social Scientists. The Tri-Agencies, including the Social Sciences and Humanities Research Council (SSHRC), require that researchers make their data openly available to the public. What does this mean for Humanists and Social Scientists that don鈥檛 work with traditional 鈥渄ata鈥� and instead work with humans, books, or art? The session will focus on the importance of data management planning. Specifically, we will cover the tools and services available to Atlantic Canadian researchers that can help you better manage your data, enhance the discoverability of your research, and ensure that your valuable research data are preserved for future reuse. Special attention will also be given to managing sensitive data, including FRDR鈥檚 Sensitive Data Pilot Project. This session will be co-facilitated by a data librarian to foster a discussion of the role of research data management in the Humanities and Social Sciences.

Data Visualization with R
7 December, 1300-1500hrs Atlantic / 1330-1530hrs NL

When working with large sets of numbers, it is often more useful to display the information graphically using histograms, scatter plots, bar charts, box plots and other depictions. This workshop teaches participants how to gain insights into data through visualization using R as the programming language. Participants learn how to: create simple scatterplots, histograms, and box plots; compare the plotting features of base R and the ggplot2 package; plot with ggplot2; plot time series data; and arrange and export plots. Basic knowledge of R is recommended, although not mandatory.

ACENET and MGF: Introduction to Genomics Data Organization & Analysis
13, 14, 15, 16 December, 1200-1600hrs Atlantic / 1230-1630hrs NL

This is a beginner level workshop series that is hands-on, covering the fundamentals of Unix command line and basic genomic skills for short read sequence data. Participants will learn how to do quality assessment, read trimming and filtering, data management, and task automation on the high performance computing infrastructure resources from the Digital Research Alliance of Canada (formerly Compute Canada). Participants will be encouraged to help one another and to apply what they have learned to their own research problems. The workshop will cover a variant calling pipeline and participants will learn skills that will be broadly applicable to other genomics tasks.