Critical skills required for data scientist 
A list of critical skill sets that can make or break a Data Scientist

Ever since data took over the corporate world, data scientists have been in demand. What further increases the attractiveness of this job is the shortage of skilled experts. Companies are willing to pour their revenue into the pockets of data scientists who have the right skills to put an organization’s data at work.
However, that does not mean it is easy for candidates to grab a job at renowned organizations. If you’ve been wanting to establish a career in data science, know that it takes the right set of skills to be considered worthy of the position.
What exactly then do you need to become an in-demand data scientist?
Here are a few valuable skills required for data scientist to inculcate before hitting the marketplace looking for your ideal job.

Programming or Software Development Skills

Data scientists need to toy with several programming languages and software packages. They need to use multiple software to extract, clean, analyze, and visualize data. Therefore, an aspiring data scientist needs to be well-versed with:
– Python – Python was not formally designed for data science. But, now that data analytics and processing libraries have been developed for Python, giants such as Facebook and Bank of America are using the language to further their data science journeys. This high-level programming language is powerful, friendly, open-source, easy to learn, and fast.
– R – R was once used exclusively for academic purposes, but a number of financial institutions, social networking services, and media outlets now use this language for statistical analysis, predictive modelling, and data visualization. This is a reason why R is important for aspiring data scientists to get their hands on.
– SQL – Structured Query Language is a special-purpose language that helps manage data in relational database systems. SQL helps you in inserting, querying, updating, deleting, and modifying data held in database systems. 
– Hadoop – This is an open-source framework that allows distributed processing of large sets of data across computer clusters using simple programming models. Hadoop offers fault tolerance, computing power, flexibility, and scalability in processing data.

Problem Solving and Risk Analysis Skills

Data scientists need to maintain exceptional problem-solving skills. Organizations hire data scientists to work on real challenges and attempt to solve them with data and analytics. This needs an appetite to solve real-world problems and cope with complex situations. 
Additionally, aspiring data scientists also need to be a master at the art of calculating the risks associated with specific business models. Since you will be responsible for designing and installing new business models, you will also be in charge of assessing the risks that entail them. 

skills required for data scientist
Summary of critical skills required for data scientists

Process Improvement Skills

Most of the data science jobs in this era of digital transformation have to deal with improving legacy processes. As organizations move closer to transformation, they need data scientists to help them replace traditional with modern.
As a data scientist, it falls upon you to find out the best solution to a business problem and improve relevant processes or optimize them. 
It makes a lot of sense for data scientists to develop a personalized approach to improving processes. If you can show your potential employer that you can enhance their current business processes, you will significantly increase your chances of landing the job.

Mathematical Skills

Unlike many high-paying jobs in computer science, data science jobs need both practical and theoretical understanding of complex mathematical subjects. Here are a few skills you need to master under this set:
– Statistics – No points for guessing this one, but statistics is and will be one of the top data science skills for you to master. This branch of mathematics deals with the collection, analysis, organization, and interpretation of data. Among the vast range of topics you might have to deal with, you’ll need a strong grasp over probability distributions, statistical features, over and undersampling, Bayesian statistics, and dimensionality reduction. 
– Multivariable calculus and linear algebra – Without these technologies, it is hard to curate the modern-day business solutions. Linear algebra happens to be the language of computer algorithms, while multivariable calculus is the same for optimization problems. As a data scientist, you will be tasked with optimizing large-scale data and defining solutions for them in terms of programming languages. Therefore, it is essential for you to have a stronghold over these concepts.

Deep Learning, Machine Learning, Artificial Intelligence Skills

Did you know, as per PayScale, the data scientists equipped with the knowledge of AI/ML get paid up to INR 20,00,000 with an average of INR 7,00,000? Modern-day businesses need their data scientists to have a basic understanding, if not expertise, over these technologies. Since these areas of technology have to do a lot with data, it makes sense for you to have a foundational understanding of these concepts.
Learning the ins and outs of these concepts will highly increase your data science skills and help you stand out from other prospective employees.

Collaborative Skills

It is highly unlikely for a data scientist to work in solitude. Most companies today house a team of data science experts who work on specific classes of problems together. Even if not in a team of data scientists, you will definitely need to collaborate with business leaders and executives, software developers, and sales strategists among others.
Therefore, when putting all of the necessary skills in perspective, do not forget to inculcate teamwork and collaborative skills. Define the right ways of bringing issues in front of people and explaining your POV without exerting dominance.
It might also help you to be able to explain data science concepts and terminologies in a simple language to non-experts.
For the year 2019, the total number of analytics and data science job positions available are 97,000, which is more than 45% as compared to the last year. Trends like this act as a magnet to attract fresh graduates towards a career in Data Science. As a data scientist, you need to wear multiple hats and ace them all. Since the field is currently expanding and evolving, it is hard to predict everything that a data scientist needs to know. However, start by working on these preliminary skills required for data scientist and then move your way up.
If you are interested in moving ahead with a career in Data Science, then you should start inculcating the above-mentioned skills to improve your employability. Upskilling with Great Learning’s PG program in Data Science Engineering or M.Tech in Data Science and Machine Learning will do the most of it for you!



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