We recently spoke with Patrick Boily, manager and senior consultant at the Centre for Quantitative Analysis and Decision Support (CQADS). The centre is offering a three day series of data analysis workshops this month, letting budding data scientists dive into the world of data science, data mining, and extracting useful insights.
Can you tell us about CQADS and its objectives?
CQADS opened its doors in 2013. We’re housed under Carleton University’s Faculty of Science but our consultants have mostly been drawn from the ranks of profs and students at the School of Mathematics and Statistics.
The main raison d’être for the Centre is that, while the best decisions are those backed by evidence, the ideal decision-making environment is still rarely met — scarcity of data often means that we let tradition and instinct take over, while an excess means that we risk drowning in data.
CQADS has four main objectives:
• Providing consulting services and sharing our expertise while solving real-world and academic problems
• Providing funding, training, and experience for graduate students and postdoctoral researchers
• Facilitating collaborations through cross-disciplinary research involving mathematics and statistics
• Stimulating the dissemination of quantitative research through short courses and seminars
Can you describe the fall workshop series and what it offers participants?
The Fall Workshop Series combines the first four of the Centre’s data analysis workshops (there are 14 in total); they cover what I consider to be the bare minimum that a data scientist or a data analyst should have in their grab bag before they can embark safe and sound on the data analysis boat:
1. Introduction to Analytics: Preparing and Visualizing Data
An introduction to the notions that must be mastered prior to, and after, embarking on data analysis, along with a discussion of common challenges and pitfalls.
2. Mining for Information Gold: Data Science Concepts and Techniques
An introduction to the fundamental data science concepts involved in data mining, with an in-depth discussion of three common concepts: classification, clustering, and association rules.
3. Simple Data Discovery: Exploring Data with R
An introduction to extracting patterns and knowledge from real datasets using R, to see how data science can provide insight into problems.
4. Getting Technical: More Data Science Methods
A continuation of the introduction of data science concepts started in the second workshop, with a detailed discussion of ten specific methods/concepts that are commonly used by data scientists.
Let’s face it, nobody is going to become a data scientist with only three days’ worth of training. I could take any of the topics covered in any of these workshops and spend three days on them and I still would only be scratching the surface. It takes time, it takes practice and study to learn how to deal with data in an insightful manner.
What the workshops are going to give is a good look under the data science hood, without getting lost in details and minutia. There will be some deeper dives over some technical areas, but we will resurface early enough not to lose sight of the larger context. There will be opportunities to play with data, to discuss good practices and case studies, to take a representative snapshot of the data science landscape (with some noted exceptions, see below). And there’s a reception on Oct. 24th too!
These are not workshops about mathematical formulas or Big Data. Familiarity with mathematical notation and concepts will help, but mathematical sophistication is not required. Big Data we tackle in future workshops.
Why do you feel this workshop is necessary when there is plenty of existing literature on data science and analytics?
In my experience with clients over the years, there is no shortage of desire to incorporate data and analytics in the everyday operations of most organizations.
• That requires managers who understand enough about data to be able to ask the right questions of their analysts, to provide them with the right data and tools to succeed, and to hire the right experts in the first place.
• Employees and analysts are faced with different challenges when organizations become data-friendly (especially if the change is sudden). Engineers, economists, sociologists, psychologists, programmers (etc.) have all worked with data at some point in their careers; they’ve heard about Support Vector Machines or neural networks or segment analysis. Learning about these concepts can allow them to remain part of the organization’s vision so to speak… but they might find that branching out on their own into the world of data science can be daunting.
• And the experts, those who design algorithms, those who can sit through a graduate course in pattern recognition or deep learning without flinching, what they might need is the ability to speak the language of the analysts and the managers.
These are the main services that I think the workshops provide: a visitors’ map for managers, a translation guide for experts, and a starting point for analysts whose experience lies in other quantitative domains, all rolled into one.
It’s much easier to find the right literature and online learning tools with a guide, fellow travelers, and a dictionary.
Tell us a little bit about yourself and your role with CQADS
I’ve come to data science and consulting the long way around. To give you an idea, I only got my first email address when I started grad school … it just wasn’t that necessary at the time! Imagine living without one now … I studied pure mathematics and didn’t really play with data until I graduated.
Even though I don’t always understand the appeal of some of the apps flying around, I’m generally glad to see interest in analytical endeavours grow, and I like to see it done well.
I’ve been the Director and Managing Consultant at CQADS since its opening: over the years, I’ve had the chance to work on plenty of quantitative projects, providing expertise and supervising graduate students in operations research methods, data science and predictive analytics, stochastic and statistical modeling, and simulations. I also head the training group which is putting together the 14 data analysis workshops. And for the time being, I lead the workshops (although I am all in favour of incorporating other voices).
We’ll continue this interview next week, where Patrick discusses the future of data science and offers advice for organizations wishing to pursue their own evidence-based decision making.
Read part two of this interview here.