How is learning Data Science different from learning Machine Learning?

Whenever you think of machine learning and data science, do the two terms cause blurring together, like Drand and Sturm or Ives and Currier? If that happens, then you are just at the right place. This article will make clear some of the important and often not deeply looked distinctions between the two for helping you in better focusing your hiring and learning.

Machine learning compared to data science

Most journalists are not always very careful about their terminology and this has lead machine learning 

seeing so much hype. In popular sermons, machine learning has taken on a wide band and implications which are well beyond its scope if you ask the practitioners. Machine learning actually refers to a very specific system of optimization of mathematics: making a computer to do better at some tasks, with the help of training the data or through experience, without undertaking any explicit programming tasks. This does often take on the form of creating a model which is based on past cases that are also called outcomes. Then the model is applied to make predictions to see what the future cases will be like. It also refers to finding methods so they lead to the minimization of a numerical cost or error function that represents by how much the reality is mismatched to predictions.

It is worthwhile to note that there are some important business actions that happen nowhere when we define machine learning:

· Access whether data that is collected is good for a given purpose

· Formulation of a fitting objective

· Implementation of processes and systems

· Communication with various stakeholders.

The requirements for these kinds of functions is what led to the acknowledgement of the field of data science. Having skills in machine learning is important for people in the area of data science

, though it is just one of the many. If you think of machine learning as the complete data science, it will be almost like thinking that accounting is same as running a profit-making company.

If you wish to be a data scientist, get interdisciplinary education

There is no hiding the fact that the demand for data scientists is high and is increasing in demand. In spite of this, a lot of the hyped educational programs in data science tend to pay more attention to classes that actually teach machine learning. This should be seen as a very significant problem. Many students who undergo such data science programs pay more focus and far too heavily towards education in machine learning rather than getting a curriculum that is balanced. This has resulted in, unfortunately to say, a large number of under prepared early career people seeking roles in data science. This is a complaint from a lot of people who are hiring for jobs in data science. They end up interviewing a large number of candidates who respond to advertisements for data science and can say little about bias and variance, basic statistics and data quality. They are not really able to present a project proposal that is coherent in achieving a business objective.

Resources

Now that you know the difference in machine learning and data science, pick your course wisely so you get the right skills to be a data scientist. So go ahead and pick the right data science course and put your career on fast track.