Top Data Science Skills That Will Get You Hired

Data Science is taking over the business world. That is why so many young students want to add data science skills to their resume. As businesses continue to produce tons of data every day, they are in desperate need of professionals capable of converting this data into insights and generating profit.

With over 2.5 quintillion bytes (2.5 e+9 GB) of the data created every day, Data Scientists are in huge demand right now. Businesses are encountering complex problems that can be resolved only through efficient data analysis. That is why, data science has managed to become a core component that allows businesses to make informed decisions on the basis of numbers, trends, and statistical data. 

Regardless of your current skills and experience, there is a path you can take to get hired as a data science professional. Here are the top skills that you need to develop:

• Programming

It doesn’t matter what company you want to work for or what position you are applying for, it is important that you should know to program. The most popular languages used in the field of data science are Python and R. Apart from these, you should also know a database querying language such as SQL.

• Statistics

As a data scientist, having a solid foundation in statistics is crucial. You should have an understanding of statistical tests, maximum likelihood estimators, distributions, etc. The same goes for machine learning as well because the knowledge of statistics will help you know which of the several techniques are or aren’t the right approach. Statistics play an important role in all companies, but especially in data-driven companies where management and stakeholders rely on your skills to design/evaluate experiments and make decisions.

• Machine Learning

If you are working at a company with a data-driven product like Uber, Netflix, or Google Maps or at a company that deals with large volumes of data, then you must be familiar with different machine learning methods. This includes random forests, k-nearest neighbours, ensemble methods, and others. Even though many of these techniques can be implemented through Python or R libraries, and you don’t need to become an expert on the workings of the algorithms, it is important to have an understanding of the broad strokes. Only then will you know which technique should be used when.

• Linear Algebra & Multivariable Calculus

If you want to work at a company with a data-defined product, you need to have an understanding of these concepts. Even small improvements in algorithm optimization or predictive performance can result in huge wins for the organization. During the interview for a position like this, you might be asked to derive statistics or machine learning results you employ elsewhere. They might also ask you about some basic linear algebra or multivariable quotations as they are the foundation for many such techniques. You might question the importance of these concepts when there are tons of implementations in R and Python. The reason is that at a certain point, it is better for a data science team to create an in-house implementation to get the best results.

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• Data Wrangling

More often than not, the data you are working with will be messy, and working with it will be extremely difficult. Because of this, you need to know to deal with data imperfections. A few examples of such imperfections in data include inconsistent formatting (e.g., ‘South Korea’ versus ‘south korea), date formatting (timestamps vs. UNIX time, ‘01/01/2021’ vs ‘2021-01-01’, etc.), or missing values. This work is especially important in small companies when you are working as a fresher or a data-driven company with no data-related product. It is an important skill for a data science professional to have.

• Data Communication & Visualization

Communication and visualizing data is an important part of the job of a data science professional, especially if you are working in a young company that is making decisions with the help of data for the first time. It is also important if you are working in a company where data scientists help the management make data-driven decisions. Communication in the field of data science means describing the findings and the used techniques to the technical as well as the non-technical audience. In terms of visualization, it will be helpful if you are familiar with common data visualization tools such as ggplot, matplotlib, and d3.js. Tableau is a popular dashboarding and data visualization tool that you can learn. Apart from just familiarizing yourself with the tools required for visualizing data, it is important that you also get an understanding of the principles that work behind communicating information and encoding data visually.

• Software Engineering

If you are planning to work at a small company that is still getting started in the field of data science, having a strong background in software engineering will help you land the job. This is because, at a company like this, you will be handling the development of data-driven products and data logging.

• Data Intuition

Companies want data science professionals who can solve data-driven problems. During the interview, you might be asked a high-level problem like about a test company that wants to run or about a data-driven product they want to develop. To answer questions like this, you must know what is important and what is not. This includes how a data scientist should interact with the product managers and engineers, when are approximations important, and what techniques should be used. 

These are just some of the skills that you might need to land a job as a data science professional. It is important to note that the field of data science is continuously evolving and you need to update your skills regularly to stay relevant in the field and become an expert. Simplilearn offers you this opportunity to upskill yourself through an online data science course in Hyderabad that is suited for freshers as well as professionals.