Data Science is the coveted new career around the block but not many can define the exact role of a data scientist. Being a relatively new field of work with people signing up for the role from different backgrounds, data science as a discipline requires a very broad skill set. Data mining, data analysis, machine learning, business analysis, data visualization, A/B testing are some of the skills a data scientist should have.
Machine learning is a large discipline in itself, with companies like Facebook relying on machine learning algorithms to sift through user behaviour patterns on a daily basis. Machine learning also involves a lot of data analysis, A/B testing and data visualization. More often than not machine learning and data science are used as mutually exclusive terms but they shouldn’t be.
If we were to explain data science and machine learning through a venn diagram, machine learning would be a subset of data science. To understand the differences in a simpler way, it would be better to start with what is data science and machine learning. Once we are thorough with the basic differences, we can delve deeper into understanding the overlap and the distinction between these two fields.
What is data science?
Data science is behind deriving actionable inputs from raw data. It is used to derive insights from the chaos of big data though predictive modelling, data analytics and machine learning. Data science is behind pattern recognition, structuring big data and finally advising the top management on critical outcomes that is possible. It is decision science.
Data science is multidisciplinary. Apart from having technical knowledge in statistics, data mining, machine learning, databases, data processes, visualizations, pattern recognition and AI, a data scientist also needs to have domain knowledge, expertise in business strategy, inquisitiveness and good communication and presentation skills.
What is machine learning?
Machine learning, when explained in simple terms, means the use of software programs with the application of artificial intelligence to learn to detect patterns in data by itself without being specifically programmed. It begins with observations in data patterns and mapping them to earlier run programs. The aim is to allow computers to run programs without explicit human intervention.
We inadvertently use machine learning in our daily lives without realising it. Effective web search is a prime example of machine learning and now it is being used in self driving cars and speech recognition.
Data Science vs Machine Learning
As explained earlier, machine learning is but a subset of data science. Machine learning can be an analysis that maybe used in data science but it is not a condition for data science, unlike statistics. While machine learning is mostly used in pattern recognition, data science is used for find answers to the questions. For example, if the supply managers at say Amazon wanted to find out if they needed to source more blue jackets than red jackets this winter - they would ask a data scientist.
The main difference between data science and machine learning is this - data science is used for predictive and prescriptive analysis usually to answer critical business questions. Machine learning algorithms are used for predictions - eg. predicting the future trends of an event and for pattern recognition. Data science is a bigger field of study than machine learning. These two terms are not interchangeable.
Data Science / applied analytics is certainly in and is here to stay and thrive. Be a Data Scientist, what Harvard Business Review calls the “Sexiest job of the 21st Century”. Take the leap - get into intensive data science bootcamps and work on live projects. Sooner the better, to make most out this wave !
