Tools for leaders: Data analytics and visualization

Tell us about the course you teach and how it fits into the curriculum.

Students in the Master of Engineering Leadership (MEL) program pursue an interdisciplinary curriculum that includes sector-specific technical courses and business courses taught by faculty from the UBC Sauder School of Business.

APPP 505, Analytics and Interpretation for Applied Sciences, which I teach in the first semester, is designed to give students hands-on experience working with different kinds of data and analytics, helping them bridge the gap between technical expertise and strategic decision-making.

The course teaches students how to make data-driven decisions within an ever-changing environment, including the growing impact of artificial intelligence on analytics. Students learn to use data ethically and effectively to advance their professional practice.

What content do you cover in the course?

The course is structured to provide students with practical, relevant experience in data and analytics. The first part of the course seeks to build trust in making decisions informed by data. We do this by better understanding data quality and through the applied practice of data visualization using tools like Tableau.

In the second half of the course, students gain hands on experience building predictive models that can help them forecast future outcomes using scripting languages like Python.

Artificial intelligence (AI) is quickly changing the field of analytics and decision making. Throughout the course, we discuss these technologies and how they impact students in their careers. We compare and contrast the results of analytics built using more conventional methods with what can be done using AI. Lastly, we help students approach these technologies in an ethical way, considering privacy impacts, referencing when AI has provided assistance, and building awareness of best practices for these tools in the workplace and academia.

A final group project brings everything together. Students pursue a problem of interest, such as something from their workplace or just an issue they want to know more about, source and assess data quality, visualize trends and develop a predictive model based on the dataset.

Last year, for example, one group evaluated the return on investment of pursuing a master’s degree, analyzing hiring trends and salary expectations across cities and industries. It was an interesting project and it helped the students identify opportunities where they could make the greatest impact.

Read how students in an earlier cohort developed an interactive tool for exploring real estate trends in Metro Vancouver – and their reflections on how this course will empower them in their careers.

What insights do students gain from these projects?

Students quickly learn that data quality is often the biggest challenge. It can be hard to find good quality datasets, particularly in the public domain. If you take green energy as an example, there is a lot of data on energy use, but it tends to be aggregated at a high level (by year and country), and doesn’t have the level of specificity that would be useful from a predictive modelling perspective.

Although it can be frustrating, it’s a good learning experience for understanding the detail required for reliable analysis and developing strategies for overcoming obstacles such as limited sample sizes or incomplete information.

How does this equip students to be better leaders?

Data can be a powerful tool for leaders – for building business cases, communicating complex topics or highlighting findings in a compelling and action-oriented way.

Predictive modelling also enables leaders to be proactive rather than reactive. For example, leaders can use forecasts to justify an investment or course of action, or to anticipate an organization’s needs. Students leave the course with the confidence to integrate analytics into decision-making.

Anything else you want to add?

What sets the MEL apart is the diversity of the student cohort. Students are exposed to a wide range of perspectives and are quickly able to expand their network, which is one of the most valuable aspects of the experience.

This course – and the program as a whole – also gives you the ability to experiment. You can be creative and take the time to explore projects you find interesting (which can be hard to do in your professional role). Going back to school gives you the chance to push beyond your comfort zone and try out new approaches. This course helps support that because there is an ocean worth of data out there!