Machine Learning vs. Data Science
Machine learning has seen much hype from journalists who are not always careful with their terminology. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners. Machine learning refers to a specific form of mathematical optimization: getting a computer to perform better at some task, through training data or experience, without explicit programming. This often takes the form of building a model on the basis of past cases with known outcomes and applying the model to make predictions for future cases, finding ways to minimize a numerical error or cost function representing how much the predictions mismatch reality.
Notice that the following important business activities appear nowhere in this definition of machine learning:
- Assessing whether data is suitable for a purpose
- Formulating an appropriate objective
- Implementing systems and processes
- Communicating with disparate stakeholders
The need for these functions led to the recognition of data science as a field. The Harvard Business Review tells us that the “key skills for data scientists are not the abilities to build and use deep-learning infrastructures. Instead, they are the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders.” Other authors agree: “We feel that a defining feature of data scientists is the breadth of their skills—their ability to single-handedly do at least prototype-level versions of all the steps needed to derive new insights or build data products.” Another Harvard Business Review article affirms, “Getting value from machine learning isn’t about fancier algorithms—it’s about making it easier to use … . The gap for most companies isn’t that machine learning doesn’t work but that they struggle to actually use it.”
Machine learning is an important skill for data scientists, but it is one of many. Thinking of machine learning as the whole of data science is akin to thinking of accounting as the entirety of running a profitable company. Further, the skills gap in data science is largely in areas complementary to machine learning—business sensibility, statistics, problem framing, and communication.