6 Data Science Skills You Need to Get Hired

Are you aspiring to be a future data scientist? Thankfully, you are on the right career path.

Owing to unprecedented significance of Data Science to businesses today, the job of a data scientist is believed to have a huge growth potential. In fact, according to a recent article published by Thomas Davenport and D.J. Patil in the Harvard Business Review, “data scientist is the sexiest job of the 21^st century.”

However, the roles and responsibilities in this field are diverse and complex, and skills required for them may vary considerably. Additionally, with Big Data analytics evolving to be one of the secret weapons for businesses today, they are focusing more on hiring someone who has specialized knowledge and specific skill sets required to excel in the field of big data.

Discussed here are 6 skill sets that a data scientist should have to get hired.

1.  Statistics:

At the minimum, basic knowledge in Statistics is crucial to be a data scientist. You should be familiar with certain statistical concepts like distributions, statistical tests, p-value, maximum likelihood estimators, etc. Having basic statistical skills help to determine whether certain techniques are a valid approach or not. It also helps in machine learning, which is an integral part of data science roles and responsibilities. Knowledge in Statistics is vital for all companies, but particularly to data-driven organizations where the product or service is not data-focused, and stakeholders would depend on you for major business decisions.   

3. Linear Algebra and Calculus:

You may expect your interviewer to ask questions on linear algebra and multivariable calculus since they form a part of various big data analysis techniques, machine learning, and statistical results. Knowledge in this field may help when you have to build implementations in house. Having expertise in these mathematical concepts is vital for organizations where the product or service is defined by the data, and minor improvements in algorithm optimization and predictive performance may lead to big wins.

4. Machine Learning:

Machine learning skills of a data scientist becomes crucial for a company that handles large volumes of data or where the product is data-driven. So, you need to have knowledge in basic things like random forests, k-nearest neighbors, ensemble methods, etc. While these techniques can also be implemented using Python or R libraries, even a data scientist needs to have basic knowledge in machine learning can be used for different techniques.     

5. Software Engineering:

A smaller company hiring a data scientist for the first time may require you to have a strong background in software engineering. Your role might be to develop data-driven products or services, and handle large volumes of data logging. For this, being a proficient software engineer is often required.

6. Data Visualization and Communication:

For companies that are making data-driven decisions for the first time or consider data scientists as people who help make data-driven decisions, data visualization and communication is of utmost importance. For effective data visualization, it is vital to be familiar with tools like d3.js and ggplot. For communication, you need to clearly describe your predictions and findings in both technical and non-technical manner. You should also know the tools important to visualize data as well as the principles that help to visually encode data and communicate information.

Developing these basic skills would help you to get a lucrative job as a data scientist, and grow as the business grows.