Data is used widely at all levels of industries and at all verticals. Organisations are increasingly relying on data and its expertise to solve business and consumer concerns. Consequently, as the demand for data increases, the demand for scientists who can engineer and analyse the data also increases. Let’s have a look at the important skills needed to become a data scientist and a good one at that!
This is one of the most crucial skill to possess for a data scientist. It requires more than a basic statistics degree. The candidate should be proficient in the various programming languages like Python, R, SAS, Hadoop, SQL, etc. These languages will help you to clean and organise datasets. It’s not just about coding, but also about being comfortable with using different programming methods to analyse and interpret data. Good command over various programming languages is an essential skill to possess in this world of advancing technology and data science being valued very much. You need to have the ability to use multiple languages as it will help a company grow and improve your chances of being a successful data scientist.
Without statistical knowledge, a data scientist is like a city without bridges, i.e. of no use. Hypothesis testing, probability and other advanced statistical techniques are the backbones of data science. You need to be good at all these techniques to get a hold of data. Having theoretical knowledge isn’t a big deal, but laying it all out in a business-friendly environment to people who’ve never dealt with statistics before, is the challenge. It will also help to learn certain techniques like linear regression, time series forecasting, etc.
This is the essence of the data scientist job role. A whole lot of data science deals with understanding and interpreting the behaviour of data and how it works. Creating prototypes and assumptions to see how the data will translate into actual business solutions is the data scientist’s job and requires deep quantitative analysis. The knowledge of how to build regression models, build algorithms, data reduction techniques, etc. are a must-have.
Humans can process information faster if it’s in the form of visuals and that’s a known fact. How many times do you find yourself remembering instruction pamphlets because of directions in images than text? Similarly, for a data scientist, it becomes very important to present the complex data in a digestible visual format, to an audience that’s both technical and non-technical. Therefore, knowledge of data visualisation tools like Tableau, Sisense, Plotly etc. is needed to present the data and convince the audience of the purpose.
Being able to communicate is of paramount importance. To be able to clearly present your concepts and translate the statistical output to actionable insights and to be a great team member, communication skills are a must!