From Psychology to Data Science: How Teaching Fellow Graham Albert Utilizes Data
Graham Albert is a teaching fellow for the ‘DS 100 - Data Speaks Louder than Words’ class at the Faculty of Computing & Data Sciences. He works under Clinical Assistant Professor, Langdon White, to introduce students to the foundational aspects of data science. This includes leading lab discussions on the research design process, familiarizing students with data science techniques, and hosting office hours outside of class.
“I was very interested in being a TF for CDS because most research-oriented departments include an undergraduate statistics course,” Albert explains. “This is something that I would like to teach should I become a professor.”
Albert is a quantitative researcher with a strong background in research methods. Before coming to Boston, Albert attended Nipissing University, Canada where he completed a Bachelor of Arts in Psychology. There, he worked at the Human Evolution Lab where he investigated the effects of evolution on human mating behavior. Here, Albert explored how the pitch of a man’s voice influences their perceived attractiveness to women and their perceived dominance to other men.
Today, he is a PhD candidate in the Anthropology Department at Boston University. His work at BU focuses on the evolution of human behavior, specifically, how an individual's interpersonal perceptions are affected by facial and vocal characteristics. Currently, Albert is conducting research to investigate how individuals use aspects of a man’s facial appearance to discern dominance.
Throughout his career, Albert has utilized statistical and data science methods and believes that they are an essential part of his research work and have served to supplement his role as a teaching fellow.
“My academic background as someone who studies the evolution of human behavior has provided me with several skills that lend themselves well to being a TF in data science,” Albert says. “I know how to identify gaps in a field of research, develop research questions and hypotheses to address those gaps, design studies to test my hypothesis and analyze my results. All these skills are essential to being a good data scientist.”
DS 100 is a class that aims to establish strong critical thinking skills for future data scientists. It focuses on teaching students how to draw accurate conclusions from data analysis, how to utilize data to strengthen and communicate their arguments, as well as understand the strengths and limitations of their analysis on particular phenomena.
“As someone who is aspiring to mentor both undergraduate and graduate students, I think that teaching students to solve their problems independently is an important skill to develop,” Albert remarks. “I want to help my students learn how to independently identify and solve issues in their code.”
For students looking into pursuing a career in data science, Albert has two main points of advice. First, he believes that students should set clear goals.
“Pursue a career in data science because you like conducting research and analyzing data,” Albert advises. “You need to establish a purpose for entering a particular career field. This will be both beneficial to you and those you work with.”
Second, he encourages them to enter the career field with an interested and inquisitive mind.
“As a data scientist you should be open to lifelong learning,” Albert says. “You always need to be working to update your skill set and you should be open and willing to learn about unfamiliar research areas.”