Data Science Network for the Federal Public Service (DSNFPS)

2021 Census Comment Classification

2021 Census Comment Classification

In an effort to improve the analysis of the 2021 Census of Population comments, Statistics Canada’s Data Science Division worked in collaboration with Census Subject Matter Secretariat to create a proof of concept on the use of machine learning techniques to quickly and objectively classify census comments. In addition to classifying comments by subject matter area, the model also sought to classify comments regarding technical issues and privacy concerns.

A Brief Survey of Privacy Preserving Technologies

Big data technologies such as deep learning have increased the utility of data exponentially, and cloud computing has been an enabling vehicle for this, in particular when working with unclassified data. However, computations of unencrypted sensitive data in a cloud environment may expose this data to confidentiality threats and cyber-security attacks. To address the new requirements for operating in the cloud, we consider a class of new cryptographic techniques called Privacy Preserving Technologies (PPTs) that might help increase utility by taking greater advantage of technologies such as the cloud or machine learning while continuing to preserve the security of information resources.

First Data Science Network Directors' Meeting

On November 25, 2020, senior managers involved in many facets of data science gathered for the first directors' meeting of the Data Science Network for the Federal Public Service. This meetings was an important stepping stone for the Network, as it continues to grow and expand its reach in the public service and beyond.

Use of Machine Learning for Crop Yield Prediction

Data scientists at Statistics Canada recently investigated how to incorporate machine learning techniques into an official statistics production environment to improve the crop yield prediction method and how to evaluate prediction methods meaningfully within the production context.

NRCan’s Digital Accelerator: Revolutionizing the way NRCan serves Canadians through digital innovation

Natural Resources Canada (NRCan) has been integrating advanced analytics into its science and research programs and aims to lead the digital transformation of the natural resource sector. Learn how their Digital Accelerator supports the exploration of innovative applications and the development of strategic partnerships to augment NRCan’s expertise.

Version Control with Git for Analytics Professionals

Analytics and data science workflows are becoming more complex than ever before, and the need to enable collaboration among team members is one that has parallels in classic computer science workflows. We take a look at leveraging Git and applying it to collaboration problems faced by analytics teams.

Using data science and cloud-based tools to assess the economic impact of COVID-19

As COVID-19 continues to impact the economy at a rapid pace, it is more important than ever for Canadians and businesses to have reliable information to understand these changes. A team of data scientists and analysts at Statistics Canada are working hard to meet this information need by automating the extraction and near real-time analysis of text data from a variety of sources.

Protected workloads on public cloud

This summer saw an increased need for flexible services that could be accessed outside of traditional networks and scale rapidly, all while maintaining the security of information entrusted to the public service. The opportunity for data science to provide timely insights to help decision makers and the public alike has never been so great, but at the same time data scientists need to be able to ensure data and workflows operate in secure environments.

The COVID-19 cloud platform for advanced analytics

As Canadians grew increasingly concerned about the impact of COVID-19 on our society and our economy in March 2020, Statistics Canada set to work collecting vital information to support citizens and critical government operations during unprecedented times.

Co-op student explores the power of Big Data

A look at what it is like to be a co-op student within the Data Science Division at Statistics Canada, this article highlights the experience of Mihir Gajjar, a co-op student from Simon Fraser University’s (SFU) Big Data program.

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