Beyond Technical Skills: The Case for a Liberal Arts Approach to Data Analytics
by Professor of Mathematics and Data Analytics Dick Forrester & Associate Professor of Economics and Data Analytics Emily Marshall
First published July 28, 2021, in Education Reimagined
We are at the precipice of a transition to a data-centric era that will vastly alter our understanding of the world and how decisions are made. Everything from business models to governmental operations have evolved and transformed to seize the opportunities of data-driven environments. Vast amounts of data, such as financial, spatial, medical, and textual are collected every second. Getting this data into the right hands can help to leverage today’s greatest opportunities and tackle our most daunting challenges, including climate change, the future of work, globalization, social justice, and health care.
Unfortunately, there is a significant shortage of qualified data scientists. In response to this need, many colleges and universities have developed programs to cultivate the young minds curious to examine and translate the flood of data shaping our present and future. Given the technical proficiencies needed to be a data scientist, it should come as no surprise that most of these programs are at larger universities that are more focused on professional training. However, we believe that a liberal arts approach is needed to empower the current generation of young learners to be the next generation of data scientists. Indeed, data science is inherently interdisciplinary and requires critical thinking, thoughtful analysis, and communication skills, which are the hallmarks of a liberal arts education and ingredients of powerful learning experiences.
Defining Data Analytics
But what exactly is data analytics? The term data analytics refers to the science of extracting information from data to uncover patterns, find relationships, draw conclusions, and make decisions. While there are some differences between data science and data analytics, we will refer to them interchangeably here.
The applications of data analytics are endless, including identifying and predicting disease, tackling global warming, and gaining insight on social issues such as income inequality and mass incarceration. There are many technical skills needed to be a successful data scientist, including a strong background in mathematics, computer programming, and statistics. Specifically, one needs to be well-versed in statistical modeling, machine learning, data visualization, data wrangling, and data management. However, successful data scientists must also employ many non-technical or soft skills, such as the ability to form a question, critical thinking, effective communication (oral and written), proactive problem solving, and intellectual curiosity, among others.
It is in the development of these soft skills that a liberal arts education truly shines. It focuses on introducing and nurturing these skills in the context of a young person’s layered learning journey, which extends far beyond a chosen major. The goal of a liberal arts education is preparing students not only to be successful in their future career, but also to understand and engage with the world around them and become life-long learners.
And we feel that young people who express a unique interest in data analytics (or any chosen sphere of study in higher education) will thrive best in a liberal arts environment—particularly because of the skills intentionally fostered in that environment.
The Characteristics of a Data Scientist
The ability to define a problem statement and ask the right questions. Fundamentally, data analytics is about forming and answering questions. While it is easy to come up with questions, it is actually quite difficult to ask the right questions—data science is only as good as the questions you ask. A liberal arts education steeped in philosophy, history, and the sciences prepares students to ask interesting and meaningful questions.
The ability to adapt and evolve with an everchanging set of technical skills. The technical proficiencies needed to be a successful data scientist are constantly changing. Liberal arts students are taught to think broadly and view problem solving as an iterative, evolutionary process. Moreover, a liberal arts education enables students to be lifelong learners who are endlessly inquisitive and growth oriented. Given the speed at which data science is evolving, the ability to drive your own learning is paramount, which is a fundamental goal of a liberal education.
The development of interpersonal skills and the ability to work in teams. There is no doubt that data science is a team sport as practitioners rarely work alone. Data science teams bring together individuals with different skill sets and viewpoints. Liberal arts students are known for having strong interpersonal skills and working well in team settings. In particular, the liberal arts focus on embracing diversity and inclusion, which are vital to successful collaborations.
The ability to communicate effectively. Of all the soft skills needed to be a data scientist, none is more important than effective communication. Data scientists must be able to explain their conclusions and rationally justify their approaches. They must be storytellers, making use of oral and written communication along with data visualization, to create a narrative that helps to translate their results into actionable insights. Liberal arts are known for building strong verbal and oral communicators.
Curiosity. Intellectual curiosity is a hallmark of the liberal arts, which drives individuals to look for root causes and really get to the heart of the matter. This is an important skill for data scientists as the field is about the search for answers, the discovery of underlying truths, and the pinpointing of hidden insights.
The knowledge of ethical foundations. There are many ethical issues that arise in the practice of data science, such as discrimination, privacy, consent, trust, and justice. Data scientists need not only to have the technical expertise, but also need to know how to responsibly collect data and use it ethically. Liberal arts students are well-equipped to consider the ethical applications of their technical knowledge to wide-ranging social and business challenges. The liberal arts prepare individuals to have a firm understanding of ethics, morality, and, ultimately, empathy.
Holistic approach. A liberal arts education prepares individuals to examine ideas from multiple viewpoints, look at problems from different angles, and appreciate diversity of thought and opinion. Data science requires this holistic approach to solving problems with data. Data scientists must be prepared to view a problem in different ways in order to gain deep insight.
Domain knowledge. Within data analytics, domain knowledge is an understanding of the field, environment, and source problem from which the data is derived. It is challenging to analyze a dataset and build a model in a field for which you have limited knowledge. By combining multiple disciplines of study, a liberal arts education exposes students to a wide range of subjects from the humanities to the sciences. This broad-based foundation prepares individuals to expand their knowledge quickly and to learn the domain knowledge necessary to analyze any dataset effectively.
Problem-solving skills. Liberal arts institutions are known for producing well-rounded students that are creative thinkers with excellent problem-solving skills. Almost every aspect of a data analytics project requires problem solving. This includes defining the right question, formulating and evaluating hypotheses, and testing and drawing conclusions. With their ability to see problems in different ways, liberal arts students are well-positioned to tackle problems and identify the most effective methods for teasing information out of data.
Eager to engage the world. Data scientists must not only understand the theoretical foundations of the field, but they must also know how to apply them to real-world scenarios. Liberal arts colleges are driven by the belief that engaged learning is necessary for achievement. That is why such schools endeavor to provide real world experiences, such as internships, externships, study abroad, undergraduate research, and civic engagement. This helps to ensure that students go on to live fully and make connections to the world around them.
The skills required to be a data scientist are multifaceted, encompassing both technical and non-technical skills. We believe that a liberal arts approach is needed to tackle not only the problems of today, but also the problems of tomorrow. Data scientists with a liberal arts background are better equipped to adapt in this ever-changing field and increasingly complex world.