Why Psychologists can be great Data Scientists
By: Maarten Grootendorst
Tags: Careers, Machine Learning, Psychology, Data Science, Career Advice

Why Psychologists can be great Data Scientists

The Intersection of Psychology and Data Science

In one of my previous articles, I talked about transitioning from psychology (or any social science) to data science. The focus was mostly on the skills one needed to gain to become a fully-fledged data scientist.

However, what if you already had made the transition? What could you do to leverage your existing psychological knowledge as a data-driven professional? It would be such a shame to throw-away years of studying and ignore all that you have learned!

I truly believe that psychologists have specific skills that can be used to become great data scientists! Sure, as a psychologist I am biased, but let’s just ignore that for a moment 😉*

This article will focus on identifying which psychological skills can be used, and how, in your work as a data-driven professional.

This will not be as self-evident as a tutorial. The purpose here is to inspire you to look for creative ways to be both a psychologist and a data scientist in your work. I hope to achieve this by providing you with some clear examples that might trigger you to apply old psychological methods in a new way.

*NOTE: The skills in this article are not exclusive to psychologists or those with a social background. The focus is as such since chances are that a psychologist is likely a better communicator than a software engineer.

1. Psychological Skills

Photo by Markus Winkler

Let us start at the beginning, why would I think that a psychologist could make a great data scientist? Surely, it is not because they are great coders or because they have extensive experience with git, right? No! It is mostly due to two broad skills they typically have acquired:

Communication skills 💬

Research skills 🔍

Now you might think that this does not seem like much, and by looking at these two skills I would be inclined to agree with you. It does not seem very impressive… However, mastering these skills requires significant effort and the impact of using them can be greater than you might think.

Both skills are umbrella-concepts and, as such, contain several sub-skills that each contributes in its own way to the fields of psychology and data science.

Below, I will briefly go through some of these skills to give you a feeling as to why communication and research skills are more important to data science then you might realize.

ELI5 💬

Explain it like I am 5 (ELI5) is often used to ask for a simple explanation to a complex topic. If you can explain it to a 5-year old, you truly understand the topic and shows that you can communicate the concepts across all knowledge levels.

As a psychologist, you are already trained to do so. It is vital that your patients understand complex methods, such as cognitive-behavioral therapy, as it would be difficult to apply treatment if they do not.

Stakeholder management 💬

It is important to realize that companies are social organizations through meetings, conference calls, lunches, etc. Being able to communicate at an advanced level is of great advantage.

Especially when it comes to stakeholder management, truly understanding their problems, and thereby the solutions they prefer requires that you have a feel of who they are as a professional. Why is this person asking for this feature? What is it that they are trying to achieve?

Ethical Decision Making 💬

A psychologist should know about ethics regardless of your specialization. In the context of data science, this involves patient confidentiality, privacy, decision making, etc. Understanding and having to work in complex ethical environments helps in preventing biases such as the selection bias, confirmation bias, or response bias.

Experimental knowledge 🔍

Now those with a social background have learned a bit more than just communication skills. In most programs, a large portion of the courses is geared towards the understanding of experimental setups involving humans.

Rigor in experimental design, nuanced interpretation of results, intellectual curiosity, are all skills befitting of a good psychologist.

Statistics 🔍

Interestingly, most psychology programs are much more statistics-heavy than you would expect. In my program, one-fourth of all courses were some form of statistics. This ranged from statistical tests and validating questionaries, to A/B testing and

Those studying social sciences tend to have more in-depth knowledge about experimental design and statistics than most other programs. Seeing as a great deal of machine learning stems from statistics, this is an awesome skill to have.

Subject Matter 🔍

When analyzing human behavior, such as designing experiments for A/B testing or interpreting geographical data of individuals, then it would definitely be helpful to have a good grasp on the things that drives humans.

Psychologists can of great help when analyzing data pertaining to human behavior!

2. Type of Data Professionals

Photo by Markus Winkler

Although the skills above are important, they might be less or more important depending on the work you actually do. Thus far, I have been looking for the perspective of a data scientist. In practice, the work between data scientists might differ greatly as professions within the data-domain are still ill-defined.

Data Professions

In order to understand how psychology can contribute to a data-driven professional, it is important that you define what kind of professional you want to be!

In my experience there are roughly 7 types of data-driven professionals*:

Manager (managing a team of data-driven professionals)

Business Intelligence Consultant

Data Analyst

Data Scientist

Machine Learning Engineer

Data Engineer

AI Researcher

These roles significantly differ in the need for both technical and psychological skills. A manager, for example, typically would not need as much technical knowledge as an AI researcher but should definitely be more psychologically inclined in order to properly manage its team.

The data profession that suits you best can be condensed into a simple question:

What balance do you want between psychological- and technical skills in your work?

Answering this question might help you understand what kind of professional you want to be. If the answer to that question is that you are not looking for technical work, then most psychology professions would suit you (i.e., Clinical Psychology, Social Psychologist, I/O Psychologist, etc.).

However, the question becomes more difficult if there is some defined amount of both psychological- and technical skills you would like. The answer…a quadrant!

3. The Quadrant for Psychology in Data

The extent to which your psychological skills actually are helpful greatly depends on the kind of work you do. If you work as a data engineer and are mostly focused on creating data pipelines, then it is less helpful and necessary to have these skills.

Below, I have created a quadrant that indicates the need for psychological skills versus the need for technical skills in which several professions are placed. Although it is a simplification of reality (who actually wants to see a 4-dimensional graph 😅), hopefully, this helps you in deciding the profession that best suits you.