Beyond Technical Skills: The Case for a Liberal Arts Approach to Data Analytics

Dickinson College has recently launched a new interdisciplinary major in Data Analytics. This major will give undergraduate learners a depth and breadth of knowledge through foundational courses in mathematics and computer science, a course in the philosophy and ethics of data, an experiential component, and a three-course disciplinary focus. In a senior seminar course, students will use their domain knowledge to meaningfully apply their skills in a particular subject area.

A team of faculty, staff, administrations, and alumni at Dickinson College worked together on the development and implementation of this new major. Dr. Dick Forrester and Dr. Emily Marshall, Professor and Associate Professor (respectively), are co-chairs of the new Data Analytics department. Below, they lay out the skills needed by a data scientist and why a liberal arts education is well-positioned to help learners develop those skills.

As you take in their insights, consider how the “Characteristics of a Data Scientist” relate to the characteristics any young learner would need to powerfully contribute their gifts and talents to the world.

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 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 where the focus is largely 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.

Data science is inherently interdisciplinary and requires critical thinking, thoughtful analysis, and communication skills—all of which are the hallmarks of a liberal arts education and ingredients of powerful learning experiences.

Defining Data Analytics

Before we go deeper into the skills needed by a data scientist, and the crossover those skills have with many other professional pursuits, let’s clearly define 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 skills (often called “soft” skills), such as the ability to form a question, to think critically, to communicate effectively (oral and written), to problem solve proactively, and to bring intellectual curiosity to any challenge.

It is in the development of these non-technical 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 to not only be successful in their future career but also understand and engage with the world around them, as they 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

1. 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.

2. The ability to adapt and evolve with an ever-changing 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.

3. 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; rather, data scientists most often work in teams that bring together individuals with different skill sets and viewpoints. The development of strong interpersonal and collaboration skills is a mainstay of liberal arts degree programs, with a particular focus on the value of diverse and inclusive teams.

4. The ability to communicate effectively. Of all the non-technical 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 students are known for building strong verbal and oral communicators.

5. 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. 

6. 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.

7. 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.

8. 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.

9. Problem-solving skills. Liberal arts institutions are known for producing well-rounded students who 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.

10. 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 those foundations 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.

As outlined above, the skills required to be a data scientist are multifaceted, encompassing both technical and non-technical skills. While we have focused on the characteristics and non-technical skills needed to be a data scientist, many of these same skills are critical for success in almost any career path.

In fact, according to CareerBuilder, 77% of employers indicate that non-technical skills are just as important as technical skills. Indeed, they are so important that the U.S. Department of Labor has developed a youth program targeted at 14-21 year-olds called “Soft Skills to Pay the Bills.” This curriculum focuses on six main skills, including communication, enthusiasm and attitude, teamwork, networking, problem solving and critical thinking, and professionalism.

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.