Today, it is evident that data science is almost everywhere as we see it benefitting various companies across all industry verticals.
Whether there is a need to improve the process of product development, enhance customer retention or when there is an opportunity to gain profits from social media streams, credit data, consumer research or third-party data sets, organizations feel the requirement to hire a data scientist to grow and outsmart their competitors.
According to a report by McKinsey, there will be a 50 percent gap in the supply of data scientists versus demand, by 2018. As various industries are predicted to have a lot many data science job vacancies to be filled in by candidates that are well versed in data science, there has been a rise of curiosity in the minds of people about the requirements to become a data scientist.
This article intends to discuss the data scientist skills that a person should inherit in order to become a successful data scientist. It is not only beneficial for those who seek a data science job but also for people who are looking to hire someone for their big data needs.
The most important technical skills that you expect every data scientist to know is statistical analysis and the knowledge of using the strength of computing frameworks to mine & process the value out of structured as well as the unstructured bulk of data. To carry out this function a person needs to have a strong academic background in mathematics, statistics, and programming as a part of it.
To attain a strong foundation to relate to technical points of data science, people usually go for a P.H.D or a master’s degree in computer science, engineering or statistics. Also, there are various schools these days that offer data science certifications to help people gain insights on big data.
Other technical skills required to have a satisfying career in data science
You need to be well versed in programming languages like Python, C/C++, Perl, Java, and SQL that can be helpful in cleaning, massaging and organizing an unstructured set of data.
Expertise in SAS and other analytical tools:
SAS, Hadoop, Spark, Hive are the most popular analytical tools that can help you abstract valuable insights from restored and organized data sets.
Proficient at working with unstructured data:
For someone to be an able data scientist, it becomes necessary for them to be incredibly comfortable in understanding and managing unstructured data coming from multiple channels.
Now let us focus on the non-technical skills that a data scientist should acquire
A solid business acumen:
Apart from having technical abilities, it is crucial for a data scientist to have business intuition and how to make a successful business model in order to be productive at their job.
Good communication skills:
To be beneficial to an organization, data scientists need to communicate their understanding of bid data to non- technical data users.
Excellent data intuition:
This means perceiving patterns where they are not observable on the surface and knowing the existence of where the value lies in an unexplored pile of data bits.