What should I learn first: Data Science or Machine Learning? This seems to be a common doubt in the minds of many people that want to make a career in any of the latest technologies that are becoming an integral part of the tech world, and with it, our lives. This is natural, considering that these two are among many highly and frequently quoted buzzwords in the technology circles today.
Data Science and Machine Learning are distinct in their own ways in terms of their content and their methodologies, but when it comes to their application and their derivation, they are not similar, but share a lot in common. As explained so convincingly in this paper, they both proceed on the basis of data analysis. Why data analysis, you may ask. This is the most fundamental element of both Data Science and Machine Learning. This is because our digital world emits just so much data that it is impossible to employ traditional databases or manual methods to assess them.
The next question that arises is this: why should companies employ all the possible means at their disposal to analyze this data? This is necessary because these gigantic amounts of data will in themselves come to nothing if they are not meaningfully analyzed. It is only when this is done that they can make perceptive inferences, interpretations and analyses that will help them arrive at crucial business decisions based on intelligence rather than through guesswork. This is where the disciplines of Machine Learning and Data Science are used.
Machine Learning is a means by which scientists get machines to perform a given task through observation and experience without having to implicitly program or supervise it. Machine Learning is a method by which errors can be prevented or rectified by building on statistical and mathematical models and bringing predictions closer to the reality. In order to do this, it assesses the suitability of data to a purpose, formulates a specific objective, implements a set of systems and processes, and finally, communicates its findings with the stakeholders.
Data Science makes Machine Learning work for businesses
What is interesting here is that these abilities do not connect with business objectives even remotely. So, there has to be a match between its capabilities of performing tasks more quickly and relatively accurately than through other methods for businesses to derive sense. This is exactly what Data Science enables Machine Learning to perform.
This points to a clear convergence and overlap between these two areas and their interdisciplinary nature. Machine Learning, data visualization, statistics, Artificial Intelligence, mathematics and related ones are used to help Data Science carry out its work.
Let us use this understanding to get back to the original question of this blog: what should I learn first, Data Science or Machine Learning? The answer is YOU! Yes, there is no set method or flow or order by which one should precede or succeed the other. It is entirely up to your aptitude and liking. Learning one before the other or after makes no major impact on your prospects, unless your company wants you to.
We have a large collection of courses on Data Science and Machine Learning that will help you crack the complexities of these two technologies with ease! Do visit these links and see how they can improve your prospects in a big way. Please also let us know what you think of this blog.
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