By: Kathrin Knorr, Simon Fraser University

The article features Mihir Gajjar, a co-op student working in the Data Science Division at Statistics Canada. He completed a Bachelor of Technology in Information and Communication Technology at Ahmedabad University, India. He recently completed the Professional Master's program in Computer Science at SFU. The article also features his previous supervisor at Statistics Canada, Meredith Thomas.

SFU: Can you tell us about Statistics Canada? What is it like working there?

Mihir Gajjar: I have been working in the amazing Data Science Division at Statistics Canada. In this division, data scientists work with subject-matter analysts, methodologists, and IT specialists to develop big data-processing, machine-learning, and AI (artificial intelligence) strategies.

For me, there are several highlights about the work culture at Statistics Canada, such as the daily scrum meetings with the supervisor and team members where we prioritize the day's work and discuss other important issues. I also like the agile development approach for most of the projects so that each project has a lifespan of four months, and then the project is ready for deployment. We also have weekly machine-learning technical seminars where we learn about advancements in the field and discuss relevant research papers.

SFU: Can you tell us a bit about the project(s) you are working on in your co-op position?

Mihir Gajjar: At Statistics Canada, analysts spend a lot of time searching for information about enterprises. With the amount of news growing exponentially, it becomes difficult to manually track all the published information. The project I am working on seeks to automate the tasks of detecting events of interest from news articles and extracting their attributes.

For example, events of interest that are related to enterprises might include mergers and acquisitions, equity markets, and branch openings, whereas event attributes are things like dates and locations of said events. Ultimately, my work allows economic analysts to spend less time on information searches and devote more time to analysis. This multidisciplinary work is a collaboration between teams, including portfolio and accounts managers, methodologists, and other data scientists.

The main technical tasks include finding similarities between articles for ranking, removing duplicates, and text summarization. The goal is to provide subject-matter experts with a dashboard to support the detection and tracking of desired events over a specified time span.

The data for our models consist of 1.5 million news articles from the Dow Jones Data News and Analytics Platform and NewsDesk, a shared governmental system. Exploratory data analysis and basic text pre-processing were used to train various machine-learning models.

SFU: How did the Big Data program prepare you for your co-op position?

Mihir Gajjar: SFU's Big Data program provided me with theoretical as well as practical hands-on experience through lectures and a project-based learning environment. Subjects like machine learning helped me to develop a solid theoretical base while the practical assignments and group projects allowed me to implement the concepts and try out new tools and technologies.

Along with sound technical knowledge, the program equipped me with essential skills, such as working in a team, communicating and sharing ideas with other people, giving presentations, critical thinking, technical writing, and time management.

SFU: What are your most valuable takeaways from this co-op experience?

Mihir Gajjar: Through the project I have been working on, I learned a lot about the practical aspects of working as a data scientist. Part of the project involved extracting data using an external company's Application Programming Interface, which meant weekly meetings with its development team. This helped me learn how to think analytically and design questions which aid in understanding the quality and the depth of the data. I also learned about the importance of fully understanding the user's needs in order to develop a product that meets those requirements.

Working at Statistics Canada gave me exposure to real-world data science projects and taught me how to create and execute a technical plan to achieve the desired goals. This is my first time working as a data scientist and this experience has improved my skills and made me feel confident about working in this role moving forward in my career.

SFU: What do employers say about our students?

Meredith Thomas, Chief, Data Science Division: Mihir is a great fit for this work environment, as he is always open to learning new approaches in technology, and works well independently or in a team setting. Partnered with a senior data scientist, Mihir continues to grow in his time here at Statistics Canada, moving from Natural Language Processing projects to image processing projects with enthusiasm and focus. He is a valued member of our team.

By: Allie MacIsaac, Statistics Canada

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.

At the same time, analysts, researchers and data scientists across the Government of Canada were faced with another pressing concern…how could they provide much-needed information quickly and securely, while working remotely with limited access to their usual tools and computing infrastructure?

Fast-tracking modernization

As the need for analytical capabilities became increasingly urgent, a team of experts at Statistics Canada came together to fast-track the Data Analytics as a Service (DAaaS) project and explore open source data solutions. The aim was to equip data scientists with the work environment they need to conduct deeper analysis and to provide insights on the impact of COVID-19 in Canada.

The result is the COVID-19 cloud platform for advanced analytics: a virtual data science workspace that integrates data from reliable StatCan sources, extracts information and presents it in a central platform that includes robust presentation and dissemination options.

Not only does this solution meet the needs of data scientists, but it also drives forward modernization at the national statistical agency by helping meet the strategic objectives of the Statistics Canada Data Strategy—including an enhanced focus on data science—at an expedited pace.

A multi-disciplinary tiger team creating a "dream" data science environment

The analytical platform is the result of a collaboration between Statistics Canada's Data Science Division, the IT DAaaS team, the Cloud Team and partners at Microsoft.

Each group had an important role to play. The cloud team laid the foundation for the work, providing a robust containerized foundation using Kubernetes and the underlying Azure infrastructure as a service (IaaS) base. The DAaaS team worked on integrating service components, including the portal, using the underlying services. The Data Science team worked with the other teams to identify open source software to be installed and worked to define pipelines and data flows. By having data science experts working with cloud and platform experts, the team was able to deliver a scalable, accessible platform that met data science needs. The result is an environment with a variety of advanced tools for satellite image processing, natural language processing and automation.

By breaking down barriers internally and externally, the team was able to create a cohesive workbench in a matter of weeks—all while working securely from home. This was done with a user-centric approach to modernize the experience for data users and better meet their rapidly-evolving needs, while providing end-to-end data science support.

"The platform has had a major, positive impact on the way we work. We are able to get better results, work in an agile way and see the benefits of modernization in action," explains Sarah MacKinnon, Assistant Director of the Information Technology Project Delivery team at Statistics Canada.

Inside the workbench you will find a state-of-the-art platform, a "dream data science environment," says Sevgui Erman, Director of the Data Science Division at Statistics Canada. "This environment addresses the high-capacity computing needs of data scientists and meets our needs for collaborative workspaces and tools. The workbench is equipped with tools for continuous integration and continuous development that allow for scalable and reproducible data pipelines, as well as advanced data and model management capabilities."

"You can also build out your workflows using GitHub Actions and Kubeflow Pipelines. With templates for training, validation, preprocessing, and RESTful model serving, and with integrations with Platform as a service offerings like Databricks or managed Data Lakes, the advanced analytics workspace gives you the freedom to harness whatever tools you want, and it gives you a unified layer to use them from," adds Blair Drummond, an analyst with StatCan's Data Science Division and a member of the tiger team.

Peek inside the workbench

The team gathered the best available open source tools to create a workbench that allows users to remotely access data loaded by Statistics Canada—with a focus on COVID-19. This powerful environment employs a full suite of data science and analytics tools, including

• Jupyter Notebooks for R and Python
• Linux remote desktop
• Power BI
• QGIS
• R Shiny
• Pachyderm (data lineage and pipelines)
• Kubeflow Pipelines
• MLflow for model tracking, custom web applications
• self-serve sharable storage
• and more.

The platform also includes support channels for user feedback and guidance.

The result is that data users are better equipped to analyze the impacts of COVID-19 and share their findings in a secure, confidential manner.

Why open source software? As Blair explains, "Open source software tools give users more flexibility and autonomy over their own work. These tools are accessible and crowd-sourced, meaning that users can also get support and help with analysis." Furthermore, results are reproducible by colleagues in other departments. An approach that incorporates open source software supports collaboration between data scientists that benefits all users.

The platform in action

By leveraging the resource functionality of the platform, data scientists at StatCan have been hard at work as they put the platform to use.

One example is the work done by Kenneth Chu, a Senior Methodologist with StatCan's Data Science Division, who was one of the early adopters of the new platform and tested it's capabilities by performing a massively parallelized statistical analysis that otherwise would not have been feasible with pre-existing computing infrastructure.

Kenneth fitted a hierarchical Bayesian model (to provincial COVID-19 death count time series) that estimated the effects of social distancing measures on COVID-19 transmissibility. There were, however, certain crucial but unknown input parameters, namely, the provincial COVID-19 infection fatality rates (IFR, defined as the conditional probability of dying of COVID-19 given that one is infected with it). Their theoretically straightforward estimates are simply the provincial ratios of the number of COVID-19 deaths to the true number of COVID-19 infections. Unfortunately, the near-complete lack of knowledge of the latter, especially during the early phase of the pandemic, rendered the IFRs highly uncertain.

The parallelized sensitivity analysis involved simply executing the Bayesian analysis independently a reasonably large number of times (200, to be precise), each time sampling the provincial IFRs randomly from the full range of plausible values. Each independent execution required approximately eight hours to complete, using two computing cores. The full sensitivity analysis, executed on DAaaS, thus required 3,200 CPU core hours in total, which would have been impossible with pre-existing infrastructure.

The capacity to execute distributed/massively parallel workflows contributes to StatCan's Big Data infrastructure. In addition, such computing capacity also enables the use of many distribution-free statistical methods (e.g. resampling-, permutation-based ones), which are highly computationally intensive but complement modern complex analytical techniques from machine learning or Bayesian statistics.

Overall, the increased computing capabilities support the agency's mission to provide timely, critical information to Canadians during the unprecedented challenges of the COVID-19 pandemic.

A secure, phased approach

Currently, the COVID-19 analytical platform is accessible to Statistics Canada employees, and to other Government of Canada departments who have research data partnerships with the agency. If you are a data scientist interested in this platform, please reach out to get involved and experience the platform by emailing statcan.analyticalplatform-platformeanalytique.statcan@statcan.gc.ca.

This is part of Statistics Canada's phased approach to grant access to the platform in a secure manner. For the first phase, access to the platform was limited to internal StatCan employees working with publicly available data only. The second phase featured access to unclassified data (publicly available data only) and access to the platform was made available to select Government of Canada employees by invitation. The third phase will feature protected data and use a mix of public and other data sets. Access to this platform will be promoted externally on the StatCan website. Each phase will include the necessary safeguards to ensure a secure environment is maintained at all times, including regular security assessments.

As this project continues to progress, Statistics Canada looks forward to engaging with the data science community and continuing to provide vital information to all Canadians.

Project team and contributors:

Christian Ritter, Statistic Canada; Blair Drummond, Statistics Canada