The history of data science as a unique discipline is relatively brief, but its foundation goes back hundreds, even thousands, of years in the field of statistics. Statistical data is at the heart of science, accounting, logistics, and generally organizing human societies.
As the power of computing revolutionized the ability to collect, store, and manipulate ever-larger volumes of data, the field of statistics branched off into what we know today as data science.
Data science is based on the collection, preparation, analysis, management, visualization, and storage of large volumes of information. In other words, computer science deals with Big Data storage and analysis.
A Brief History of Data Science
Scientists have always relied on data to test their hypotheses. Their concern with data is typically applied to their specific goals and fields of study, not to the data as a separate discipline. They cared about what the statistical data told them, not the bit of data itself. And speaking of “bit,” this is where we begin our short history of data science.
In 1947, John Tukey coined the term “bit” while working for Bell Labs building statistical methods for computers. Considered by most as the grandfather of data science, Tukey was first and foremost a mathematician. His pioneering work led him to consider statistics in a new light. In 1962, Tukey authored the paper The Future of Data Analysis. Writing for Toward Data Science, prominent data scientist Ron Sielinski explains how Tukey’s paper is generally regarded as a “seminal moment in the history of data science.” When recounting a history of data science, many cite this paper as the launching point.
In that paper, Tukey wrote: “For a long time I thought I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt…I have come to feel that my central interest is in data analysis…”
Data Analytics: Statistics in the Computer Age
Tukey saw how merging the depth of statistics with the power and speed of computers brought data to life in new ways. In hours instead of days or weeks.
Over the years, hours turned to minutes, seconds, and nanoseconds. In a relative flash, the bits of data – their analysis and application, became a new branch of science. Evolved from statistics but a distinct discipline focused on understanding how vast amounts of data should be collected, stored, manipulated, and analyzed.
As Sielinksi writes, the groundbreaking concept in The Future of Data Science is the assertion that “statistics and data analysis are separate disciplines.” Tukey’s mark in the field of statistics, analysis, and mathematics spans his entire career. He developed the Fast Fourier Transform (FFT) algorithm, box plot, and “multiple statistical techniques that bear his name,” writes Sielinksi.
In 1977, Tukey published Exploratory Data Analysis. Tukey’s thinking was seminal in establishing the idea of data analytics as a science by virtue of its “reliance upon the test of experience as the ultimate standard of validity.” Data science and analytics in the ensuing decades since Tukey’s defining presence in the field continues to evolve.
In such a world, there is a growing need for trained data scientists and analysts in all sectors of the economy.
The Demand for Data Analysts
There is a strong and growing demand for data analysts. Defined by the Bureau of Labor Statistics as “operations research analysts,” the average annual salary is $84,810 as of 2019. Further, the BLS projects a growth rate of 26 percent in the field through 2028.
Technical and Soft Skills of a Data Analyst
What we know of today as data science encompasses several tools, methods, and skillsets. Included among these are data analytics, data mining, machine learning, artificial intelligence and big data.
Given the ubiquitous presence of data science and its sub-disciplines in nearly every facet of modern life, successful data scientists must also possess the soft skills required to interpret and apply data analytics to specific human needs. High-level technical competence must be flexible enough to adapt to the task at hand.
As Razvan Veliche, former director of data science at Analysis Group and current CEO of MountainCrest Corp., an independent data analytics consultancy, says, beyond the technical skills of a data analyst, it is important to know “how to think around data and literally think of data as a Playdoh.”
Get Educated at Boston College
Along with his extensive industry experience, Veliche is also a professor teaching Big Data Econometrics at Boston College, part of the curriculum for the school’s online graduate certificate program in Data Analytics. This online program is an entry point for undergrads who understand the value of a career in data analytics. For experienced professionals, the program prepares them to adapt and thrive in a rapidly changing world.
For those seeking a career at the top levels of data science, data analytics, and global economics theory, the Master of Science in Applied Economics from Boston College is a perfect choice. Students get a thorough grounding in both the theory and practical application of the tools and methods necessary to lead in the complex world of policy, industry trends, and analytic strategies (Veliche’s data Playdoh). Graduates of the program emerge with the skills, expertise, and hands-on experience required to lead in specialized fields such as health care, finance, marketing, and environmental policy.
Boston College centers this curriculum within a framework of people-centered, ethical, reflective decision-making grounded on the Jesuit, Ignatian tradition.
With a worldwide network of over 180,000 alumni, graduates of the MSAE program at Boston College are the tip of the spear that started with John Tukey and his peers pushing the boundaries of data science, data analytics, machine learning, and big data. Those who join its ranks help cultivate the practice of harnessing raw data to inform a better world.