From being a subject of keen scientific interest and a part strictly of technical erudition to becoming an effective weapon of mainstream business, Machine Learning has come a long way through this decade. Fortunately it is far from its probable zenith and we can expect nothing else but thorough progress in the future. It is understandable that our goal here is to ponder upon the role and scope of machine learning in the field of data science as we are up for a kind of comparative study of the discipline with Statistical Modelling. It will not be wrong at least from a general point of view to equate machine learning and statistical modelling because both of these basically serve the same purpose i.e. learning from the data. Yet, there is a world of differences between them in terms of approach, human involvement, results and opportunities. Anyways, let us take a look at where machine learning seems to be going today.
The world of automation
Machine learning algorithms have supplanted human efforts in many fields. Automation is replacing human staff in the routine jobs. From banks to super markets all greet their customers with machines that support the customers with utmost precision and efficiency with no or very less human involvement. Fast food shops and call centres around Europe and the USA are cutting down on human staff in considerable numbers. These effects of the development of machine learning, however, sound detrimental. It seems as if we are walking into a world run by machines. The obvious future of machine learning seems to be that of all pervading supremacy which It will soon make us feel in education, healthcare, public transports, banking, manufacturing and more fields.
From specific general problem solving
Machine learning algorithms have become extremely powerful. They use deep and sophisticated math to solve hard problems pretty much like magic. But it is moving rather toward general problem solving rather than focusing on a specific issue. This clearly shows the inclination toward being more suited for industrial purposes than for scientific research.
Machine learning and advanced analytics
With the continuous fear of the disruptive powers of machine learning algorithms we often tend to forget the real purpose of a tool that is helping the people not replacing them. Machine learning as is used in data analysis showcases a great example of empowerment of human endeavour. Machine learning algorithms feed on data which makes them perfect tools for data analysis. More data you put through the algorithms the better it learns to identify the important data and shows better results. Localized advanced analytics becomes really simple with the help of machine learning algorithms. It can be greatly helpful in identifying the potential consumers and targeting them with specified marketing efforts. As a whole it makes a huge difference in data analytics and so has become a much-loved technology among data scientists.
Now, it is time to come back to the major question – Whether machine learning offers better career opportunities than statistical modelling.
Difference of approach
Without delving too deep into the intricate details we may very simply say that statistical modelling focuses upon explainibility of a solution while machine learning is more concerned with the results. ML algorithms are the new thing and they work like magic – you cannot always tell how the results are found but the results are mostly accurate and achieved without much human intervention.
As far as jobs are concerned
Statistical modelling has been used for quite a while. One can say that it is the traditional method of predictive modelling. A lot of companies rely on this system that formalizes the relationship between variables in the collected data in the form of mathematical equations. Statistical skills can find you a lot of openings. And it is a highly valued skill to have for any data scientist. But as we look at the present trend and anticipate the future scenario machine learning is coming up fast to perform more accurately the tasks previously performed by statistical modelling. So, the future of machine learners looks really bright. Presently both the techniques are used for pattern recognition, knowledge discovery and data mining. Both share the same goal that of drawing insights from data and both are usually 85% accurate.
Together they shine brighter
In the case of machine learning all you have to do is train the computer to learn from data and then throw in all the data. The algorithms will process the data and recognize the patterns. At the other hand statistical modelling requires the statistician to have a detailed knowledge of the data to come up with predictive parameters. This might tempt us to deduce that machine learning is here to replace the statistical approach and encourage the data science enthusiasts to learn machine learning. The truth although is different. There are problems that require a statistical solution and others that may be solved better with machine learning algorithms. The integration of both can really give the data science endeavours wings.