While we do not promise college credits, we do promise five short lessons to bring you up to speed on this powerful discipline, called data science. Read on to ingest your first lesson.
While business objectives (and job seekers, and training programs) desire a simple and straightforward answer to the question of “What is Data Science?”, reality is complicated by the fact that the answer is highly nuanced and somewhat esoteric. I would posit that this comes from the fact that to define the field of data science we must combine the skills, insight, and problem-solving in a business context. The exact mixture of these depends on the individuals involved and the questions that must be answered, or more to the point the questions that must be asked. Indeed, there are many ways to define data science as a domain; likely as many ways as there are people to whom this question is posed at the moment. In the less-than-immortal words of Wikipedia:
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
As a modern scientist, the above words could, in a loose fashion, describe the work I did before transitioning into this field. Indeed, as an astrophysicist, I had diverse flavors of data from which I extracted knowledge and insights that could only be quantified and justified in by using statistical data analytics and comparison to theories. Furthermore, all science uses data. It is only in the realms of pseudoscience, and perhaps string theory (physics burn), where data is replaced by the esoteric dreams based on vapid hopes.
Thus, we could say that data science is merely the application of the scientific method to questions in that do not fit into academic categories of traditional science such as business applications. Indeed, many professionals often interchange the terms data science, business analytics, business intelligence, predictive modeling, and statistics without a thought to the differences between those practices. Sometimes, scoundrels even rebrand these previous approaches and solutions as ”data science” to be more attractive.
So how do you define data science as a leader in your company? Focus on the skills needed for the successful application of the scientific method: critical thinking, problem-solving skills, ability to design an experiment, the ability to carry out an investigation that is unbiased in its approach. Simply put, data science is problem-solving in a digital environment.
So how do you succeed at data science? Well, first you hire a data scientist. To help you know what to look for in that look to Lesson 2: What Makes a Data Scientist, which will help you hunt down these rare creatures.
Kevin Croxall is Director of Data Science for Expeed Software. He is a data and research scientist with more than a decade of comprehensive experience in data science project design and implementation. He has a broad range of experience in software development geared toward pipeline development, statistical analysis, and data visualization and presentation.