Data science insights from Albertan experts in tech and academia

In today's rapidly evolving digital landscape, data has become the new currency that drives businesses and shapes our daily lives.

A recent Tech Thursday panel discussion brought together experts from diverse backgrounds to explore the challenges and opportunities presented by the data revolution. The discussion touched upon the increasing need for data-driven solutions, the delicate balance between profits and privacy, and the ethical concerns surrounding data collection and usage.

Meet the panelists

The panel comprised esteemed individuals who have been instrumental in driving data science education. We had the privilege of hearing from:

• Junaid Qazi, PhD, president and chief data scientist at DataSkillz

• Jim Stallard - professor in the Department of Mathematics and Statistics at the University of Calgary

• Usman Alim - associate professor in the Department of Mathematics and Statistics at the University of Calgary

• Lauren Dwyer - academic chair for SAIT’s School of Advanced Digital Technology

Foundations of data science

Dr. Stallard, a professor in the Department of Mathematics and Statistics at the University of Calgary (U of C), initiated the discussion by highlighting the broad and transformative nature of data science. In the past, data science encompassed various disciplines, but in recent years, it has expanded to include data engineering, a crucial aspect responsible for data storage, processing, and integration.

Dr. Alim, an associate professor at U of C, emphasized his background in computer graphics and how data science has grown into a field of its own within the past decade. He mentioned the Master of Data Science and Analytics program at U of C, which equips students with comprehensive knowledge through an intensive one-year course-based curriculum and hands-on experience in internships.

Dr. Dwyer, academic chair at SAIT School of Advanced Digital Technology, reinforced the idea that data science is an evolving journey. While the tools and technologies may change over time, the core foundations of data literacy and understanding how to ask the right questions remain essential aspects of data science.

Future roles in data science

Dr. Qazi, the president and chief data scientist at DataSkillz, shared his journey from working with vast amounts of data during his PhD to establishing his consultancy firm focused on data science and machine learning. When it comes to the pipeline of individuals in the data science space, Dr. Qazi envisioned various key roles.

1. Data scientists: The core of the data science ecosystem, data scientists analyze data, build predictive models, and extract valuable insights to support decision-making.

2. Data engineers: Crucial for the successful implementation of data science solutions, data engineers are responsible for data processing, storage, and integration.

3. Generative AI experts: With the rise of generative AI, experts in this field play a significant role in creating AI models that can generate data, images, and even entire new data sets.

The future of data science

In response to the second question about the future of data science roles, the panelists expressed excitement and anticipation. They acknowledged that the data science landscape is continuously evolving due to technological advancements. Dr. Stallard foresaw an expansion in roles related to AI research and development, with specialized positions in areas like ethical AI and explainable AI becoming increasingly relevant.

Dr. Alim predicted that the demand for data science professionals would soar as businesses recognize the value of data-driven decision-making. This demand would extend beyond the traditional technology sector, encompassing industries like healthcare, finance, and even arts and culture.

Dr. Qazi added that with the integration of data science into various domains, hybrid roles would emerge, combining expertise in data science with other specialized fields. These roles would drive innovation, leading to transformative applications of data science in society.

Data education initiatives

Dr. Dwyer, the academic chair for data analytics at SAIT, provided valuable insights into the institution's data-centric programs. SAIT currently offers two data-centric programs, with another one in development. One of the key courses is in data analytics, which is complemented by a business intelligence and reporting course. Dr. Dwyer emphasized that data science comes in various forms, each aiming to utilize data analysis to improve peoples’ lives.

Dr. Dwyer's personal journey in communication and cultural studies, where she focused on social robots and opinion development for individuals experiencing loneliness, exemplifies how diverse data applications can be. It shows that data science not only holds potential in solving economic disparities but also extends to various social and humanitarian domains.

Addressing ethical concerns

Dr. Dwyer also shared her involvement as a program manager in an artificial intelligence consortium across five Canadian universities. This consortium, led by professionals like Dr. Qazi, aims to promote responsible and ethical policy changes in AI and data usage. By instilling ethical practices, data science can not only bridge economic gaps but also create a fair and inclusive future.

Dr. Alim highlighted the comprehensive data science and analytics programming offered at the University of Calgary (U of C). With a fast-track four-month program, students are immersed in an intensive and focused learning experience, equipping them with the skills needed to thrive in the data-driven world. The program's emphasis on IBM technologies, supported by experts like Jim Stallard, further aligns graduates with industry demands and prepares them for impactful careers.

The quest for privacy

A participant raises the question of whether true privacy is attainable in today's data-centric society. Dr. Dwyer's response is straightforward: it may seem nearly impossible to maintain complete privacy. As digital citizens, we leave a trail of data behind us with every online interaction, purchase, or even social media post. This pervasive data collection has become an integral part of modern life, both enriching our experiences and raising concerns about privacy.

Identifying talent gaps in hiring

When it comes to hiring data science professionals, the panelists acknowledged the existence of various gaps. Dr. Qazi mentioned that data science is a vast field encompassing numerous elements, and candidates often require training in diverse aspects to become industry-ready. To bridge these gaps, educational institutions like U of C offer comprehensive courses that equip students with the necessary skills to excel in data science roles.

Dr. Alim pointed out the diverse areas where data science is making significant strides, from finance and health to government and social services. As each domain has unique data needs and analytical requirements, candidates must be equipped to approach data science challenges from various angles.

Upskilling revolution: The need for continuous learning

Dr. Qazi emphasized the need for an upskilling revolution to meet the demands of the rapidly evolving data science field. With the World Economic Forum projecting that more than 50% of professionals will require upskilling by 2025, the importance of continuous learning becomes evident. Industry leaders and academic institutions must collaborate to ensure that data professionals stay updated with the latest tools and technologies.

The power of domain knowledge

Dr. Qazi shared a compelling example of how domain knowledge can unlock the potential of data science. Working with the Ministry of Health in Saudi Arabia, Dr. Qazi faced challenges with data-sharing in the government sector. However, by focusing on domain-specific knowledge, he empowered medical doctors to predict and analyze health data efficiently. The understanding of domain-specific insights enhances data science initiatives and facilitates meaningful outcomes.

Data science: Beyond programming

Panelists emphasized that data science goes beyond programming; it is about critical thinking and imagination. Dr. Dwyer highlighted the role of data scientists in adding value to data by asking the right questions and interpreting results creatively. The ability to look beyond traditional methods and think out-of-the-box is critical for success in the ever-evolving data science landscape.

Closing thoughts

In this insightful discussion on data science, we delved into the challenges and opportunities presented by the rapidly evolving field. The data science talent gap emerged as a pressing concern, with the industry demanding a workforce equipped with relevant skills. Upskilling and reskilling became the key to bridging this gap and preparing professionals for the future. Industry-academia collaboration and skill-focused curricula were highlighted as vital components for producing competent data scientists.

Ethics and privacy also took center stage, as data collection and utilization raised ethical dilemmas. While complete data privacy seems elusive in the digital age, the responsibility falls on individuals and organizations to prioritize ethical data practices. The potential risks of biased data and data misuse call for data analysts and scientists to be vigilant and socially responsible.

As data continues to shape our world, businesses and governments rely on it for decision-making. However, as we navigate this data-driven landscape, we must remain mindful of the ethical implications and strive to harness data's power responsibly. By aligning industry needs with skill development and embracing ethical data practices, we can pave the way for a data-driven future that benefits society as a whole.

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