The Ultimate Guide to Data Science in 2022 | Optymize

What is Data Science

The problem faced by many beginners in Data Science, especially those looking for data science jobs is the fact that the field has only recently received clear classification in regards to other disciplines. In the past, it has been suggested as an alternative name to computer science and statistics but it wasn’t until the 21st century that there were any agreed upon parameters for it. However, it still lacks any universally agreed upon definition and as such, sceptics have at times referred to it as nothing more than a buzzword.

However, it is generally agreed that data science utilises and unifies theories from multiple fields including but not limited to mathematics, statistics, information science, computer science, etc. It shares a lot of commonalities with the latter two while still having significant differences. Outside of this, to get an idea about the relevance of data science, it has been termed as the “fourth paradigm” science (outside of the pre-existing theoretical, empirical and computational) by Jim Gray, a Turing award winning computer scientist.

This article will give a basic walkthrough about the different aspects of data science, ranging from the various roles available to students of data science, the commonalities it has with related fields like information science and computer science, prerequisites for data science, applications of data science and other topics.

Prerequisites for Data Science

The prerequisites for Data Science vary in specificity depending upon the role in question. As such, listed below are the prerequisites for Data Science at the beginner level.

• A working knowledge of statistics. Statistics is arguably the base foundational subject that forms the basis for data science. As such, those seeking a career in data science must possess knowledge about different methodologies that are utilised in data science ranging from bayes theorem to probability theorem.
• A data science career possesses the prerequisite of a working knowledge of artificial intelligence, machine learning and neural networks. AI is the branch of computer science that is used to enable machines to mimic human behaviour and a superset of machine and deep learning and neural networks. Machine learning and its algorithm focuses on instructing machines how to learn from data. Machine Learning can be broadly classified into three groups: supervised, unsupervised and reinforcement learning. Neural networks are a processing unit architecture inspired from their biological equivalent. This is a component of AI, ML and DL and it is used to perform data processing through multiple layers of the neurons.

• Knowledge of programming skills in high level languages like R and Python along with the associated libraries. They are useful for tasks such as data visualisation and statistical analysis and unlike computer science, only a working knowledge of the higher-level languages instead of the entire range of languages expected for developers.

Conclusion

Now, despite the confusion of Data Science’s definition in the early stage, that hasn’t stopped it from growing and with a better idea of what comes under it and what applications it may have, As such, as shown from the points above, there is now a clear idea of how it works, some of the common tools it may have and what it offers for those in the field and what prerequisites are there for data science.