From Psychology to Data Science: A Journey of Curiosity and Application

When I tell people I work in data science and data analytics, they’re often surprised. Many assume my background must be in mathematics, statistics, or computer science. But, in fact, my academic journey began in psychology, a choice driven by curiosity about human behavior rather than a love of numbers. This article recounts my transition from psychology to data analytics and data science, and how the skills I developed in psychology gave me a unique perspective in this field.

Starting with Psychology: An Unexpected Encounter with Statistics

Choosing psychology as my major wasn’t because I had a particular affinity for math. I was “okay” at it but found it lacking in tangible application, until I began my degree. In my first week, I discovered that nearly half of my schedule revolved around statistics and research methodology. In psychology, these tools are essential for answering questions about human behavior through experiments, data collection, and analysis. I became engaged in statistical analysis not for its own sake, but because it was a means to understand and test theories.

My growing interest led me to pursue a master’s and eventually a PhD in health psychology. My doctoral research focused on predicting why children have accidents, using both neural networks and path analysis to build a predictive model. This involved examining factors within the child (such as perception and safety skills), as well as environmental factors and parental influences. I designed models to understand behavior theoretically and predict future outcomes. Through this work, I gained experience in using advanced statistical techniques, including neural networks and structural equation modeling.

From Academia to the Public Sector

After completing my PhD, I realized I wanted to move from academia to a more applied field. My journey took me to the public sector, where I worked with the NHS and local government. I began by conducting studies, gathering data through surveys, and performing analyses to inform policies and initiatives. Over time, I realized that much of the data I needed was already being collected in administrative systems, from patient visits to benefits applications.

Analyzing this administrative data revealed insights into organizational questions, and I found myself conducting the same type of research and analysis I’d done in academia. I just didn’t realize then that this role was essentially data analytics. It was in the public sector that I began to see how my research skills could be used to answer practical questions in real-world settings.

Discovering Data Analytics in the Private Sector

The turning point came when I transitioned to the private sector. I everntually went to join ITV, where I set up the analytics function as well as other departments, including CRM, SEO, and digital marketing. I was applying the same research and analytics skill set but in a business context, but also handling everything from databases to analytics. This transition coincided with the growing demand for predictive modeling skills in the private sector. My background in machine learning (although I called it “advanced statistical techniques” back then) and experience with predictive modeling proved to be invaluable.

As data science gained prominence, I realized that the work I’d been doing all along aligned with what the industry now called “data science.” By the time I moved on to organizations like Adam and Eve/DDB, my skill set in data science was well-established, and I had the opportunity to lead teams and shape data strategy.

Psychology as a Unique Advantage in Data Analytics

As I advanced in my career, I increasingly managed teams of data analysts, data scientists, and engineers. My background in psychology gave me an edge, especially when working with consumer and customer data. Analyzing behavioral data allowed me to apply psychological theories to understand and explain patterns. Theoretical frameworks like the Theory of Planned Behavior or the Health Belief Model provided insights into why people behaved as they did, which proved helpful when presenting data to senior management.

In addition, my diverse background enabled me to communicate complex data findings in human terms, something that can be challenging in a purely technical context. My psychology background helped me bridge the gap, making data insights accessible and relatable for decision-makers.

Founding My Own Consultancy

Eventually, my career led me to start my own analytics consultancy, Be Data Solutions. This decision was driven by a desire to bring my experience in both psychology and data science to organizations looking to leverage data for better decision-making. Today, I manage teams across various functions, from data engineering to software development. My academic foundation allows me to oversee the development of data products with a thorough understanding of the methodologies involved.

The Power of an Interdisciplinary Background

In many ways, my transition from psychology to data science was natural. Although it might seem unconventional, my background in psychology equipped me with a toolkit that proved invaluable in the data science and analytics world. I may not be the best statistician or programmer, but my combination of research, analytics, and psychology has allowed me to carve out a unique niche.

For those considering a similar career path, I would say that unconventional backgrounds can bring a fresh perspective. In my case, psychology gave me insights into human behavior, which has been a tremendous asset in analyzing consumer data. So, while my journey may seem surprising, it has proven to be a seamless and rewarding transition into the world of data science and analytics.