Data science involves the use of scientific methods, algorithms, and systems to extract information from structured and unstructured data. As a discipline, data science synthesizes mathematics, statistics, computer science, domain knowledge, and other inputs to analyze events and trends.
In a world that has gone digital, data scientists are among the most sought-after IT professionals. Basically, a data scientist must be able to write clean code and use statistics to learn from data.
According to career site Indeed.com, data scientists not only combine math and computer science, but must understand the industry they serve. Data scientists use unstructured data to produce reports and solutions related to their field.
According to Indeed, data scientists should be familiar with cloud computing, statistics, advanced math, machine learning, data visualization tools, query languages, and database management. Ability to program with Python and R is generally expected.
Staffing firm Robert Half notes that landing data science jobs, especially at the entry level, is not insurmountable. Despite recent cutbacks, recruitment for the tech sector remains active, as IT employers are hiring at pre-pandemic levels or beyond.
“As companies accelerate their digital transformation, data scientists are needed in all major industries, from technology and manufacturing to financial services and healthcare, as well as in organizations across academia, government and the not-for-profit sector,” says Robert Half. “That’s because organizations of all types need to turn the numbers into recommended strategies and actions.”
To find out what it takes to become a data scientist, we spoke with Daryl Kang, data scientist at mobility-as-a-service provider Uber Technologies.
Kang received a Bachelor of Arts from the University of California, Los Angeles, where he majored in Business Economics with a minor in Accounting. “I was a first generation student,” he says. “I was graduated summa cum laude in 2.5 years, which allowed me to have the financial means to pursue higher education.
Kang then pursued a Master of Science in Data Science at Columbia University. To qualify for the data science program, one needed a foundation in math, probability, statistics, and computer science.
“Initially, I was motivated to pursue a career in banking and finance,” says Kang. “Having earned a degree in economics, I had assumed that was the most natural career path.”
However, during a gap year after finishing college, Kang had the opportunity to work on personal projects that matched his passions. “I was motivated to major in economics after being inspired by the book, Freakonomics“, he says. “It showed me the power of data to answer questions that are universally applicable to any field.”
Around this time, Kang also discovered a passion for programming, having “hit the ceiling of what was possible with Excel,” he says. He spent several months learning to program through free online courses.
“It gave me a clear insight into the field of data science, and with it the clarity to recognize it as a continuation of my passion for economics,” says Kang. “At this point, I was determined to pursue my graduate studies in data science as a career change.”
Foundations: Discipline, passion and empathy
Growing up in Malaysia, Kang says he experienced a strict public education system, “where discipline was a key value that was instilled in me. It definitely paved the way for building a strong work ethic which has helped me in my data science career as the role can be demanding. »
Additionally, Kang’s experience in a liberal arts program at UCLA helped foster a sense of appreciation for other fields of study and a general desire to learn. “It gave me the discipline, but more importantly the passion, to pursue the continuous learning that is essential to following the field of data science,” he says.
Kang also notes that starting with a non-technical background helps him empathize with non-technical stakeholders, which he uses to communicate effectively in his role.
Kang’s first exposure to working in data science was during an internship with entertainment company Viacom (now Paramount). He spent seven months working as a data scientist intern. “It was my first real experience with data science in industry,” he says. “I worked on the box office revenue forecast.”
The experience was instrumental in helping Kang bridge the gap between academia and industry. He was able to identify gaps in his skills that he would need to fill in order to succeed in applied data science, he says.
In 2018, Kang joined media company Forbes as a data scientist, focusing primarily on building recommender systems. One example was a system that recommends trending news articles to newsroom editors.
“The focus was on back-end engineering, and it gave me the opportunity to better improve my software engineering skills,” says Kang. “It was also an opportunity to learn about the end-to-end lifecycle of delivering a data product, from setting up the back-end infrastructure, to analyzing insights from the data, by presenting this information to the end user.”
To be effective in his role at Forbes, Kang needed to have a strong foundation in Python and software architecture.
After about three years with the company, Kang joined Uber as a data scientist in a role heavily focused on product analytics. “I worked specifically on growing and acquiring merchants. This meant that the deliverables were more focused on informing business decisions and making product recommendations. Kang notes that data engineering was also an important part of the role. Company.”
At Uber, Kang says he must have been familiar with experience design, “which is an integral part of Uber’s principles in data-driven decision-making.”
The typical work week of a data scientist
“Meetings, unsurprisingly, are a key part of the week,” says Kang. “These are opportunities to deliver reports, presentations and build empathy for stakeholders.” Often these stakeholders are product managers, although it is not uncommon to collaborate with other functions such as user experience researchers, product designers or engineers.
“Depending on the ongoing projects, the rest of the time could be spent on analysis, such as running descriptive analysis to prepare a monthly performance report or diagnostic analysis to investigate a change in a metric, developing pitches or more specifically defining the narrative and coming up with recommendations,” says Kang.
Memorable career moment
“One of my favorite memories of my time at Forbes was mentoring a team of grad students through their capstone project as part of an industry outreach program,” says Kang. . “It was refreshing to be a mentor for the first time, and it was as much a learning experience for me as it was for the students. The fact that the team also won first place in the end-of-semester showcase competition was just the icing on the cake.
“Fortune smiles on the daring,” says Kang. “A lot of things seem overwhelming at first but will subside with time and repetition. Also, it’s important to know the difference between a positive and a negative challenge. Letting go of bad activities allows us to focus on the things that matter.
As a practical matter, Kang recommends anyone interested in data science to start by learning Python and statistics. “If you’re not discouraged and curious enough, you’ll naturally fall into the fields of data science and machine learning next.”