Foundation Pillars & Skillsets Required for a Successful Data Scientist

With the ever-growing demand for data scientists, there is a sudden rise in data science aspirants. Be it at any career level, many working professionals and students are trying to transition into the data science domain.

But what is the skillset required to be a successful data scientist is the most common question in every budding data science enthusiast’s mind? Every organization is looking to expand its data science arsenal; hence the need for a good data scientist increases day by day. Here we have tried to highlight a few significant foundations pillar that makes a data scientist stand out from the crowd or in Data science language, be that outlier that everyone is looking for: 

  •  Mathematics /Linear Algebra / Statistics – This is the backbone for any Data scientist; having sound knowledge of linear algebra, Calculus, Mathematics, graphs, statistics, etc., is essential as they are the building blocks of any machine learning solution. Without the knowledge of these, ML solutions become a black box, and a Data scientist will be at the mercy of the models. These concepts are fundamental to understanding the trends, patterns, and results of your models.
  • Machine Learning / Deep Learning Algorithms – This is where the magic happens; hence it is an essential skill required. A Data scientist should understand the complete working of an ML/DL algorithm to make an educated call of which algorithm to apply where, how to tweak the existing algorithm as per the data given, and write their own algorithm. Existing algorithms often don’t perform well on our real-world data; hence, the need to write a customized algorithm arises, which can only be possible if you know in and out of existing ML/DL algorithms. Typically, this phase is what we colloquially call data science.
  • Knowledge of Programming Language – Having learned all the mathematical/statistical concepts, it is critical to know at least one programming language to implement and solve the problem. Hence having knowledge of languages like Python, R is essential. These languages have a lot of predefined packages and functions that help in various phases of deciding a data science solution, especially packages like NumPy, Pandas in python are the go-to tools for the data analysis phase.
  • Business Acumen /Domain Knowledge– Machines only understand numbers, not the meaning behind those numbers, e.g., 100 $ salary of a person is the same as the age of a man who is 100 years old. It’s the domain knowledge that puts meaning to these numbers, and hence to effectively model your solution, a data scientist should have a sound understanding of the data (not just the statistical part of it but the business side of it as well).
  • Story Telling – Last but not least – the 5th pillar is storytelling. This means that a data scientist should be able to explain the most complicated/sophisticated models to a layperson or the stakeholders without compromising on the information shared. This is a very underappreciated skill, but one of the most important as you will have to convince both statistically and functionally that your model solves the business problem in question.

Developing these fundamental skills and upgrading them as per the ever-evolving technology will be crucial in ensuring that you are relevant in this market as a data scientist. On the bright side, ample content is being developed every day and readily available, making this field even more interesting to pursue.


~ Nishkam Shivam, Data scientist @ Bristlecone | Ex- Walmart | Ex- Accenture

Data science has established itself as a pre-requisite for developing effective data-driven decisions and insightful business strategies. Join this up-and-coming domain by enrolling in a data science course with Emeritus India. Learn via case studies and real-world examples taught by faculty at top business schools.

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