Technical and Non-Technical Skills

Technical skills will require both analytics and computer science. The majority of data scientists will have a Master’s degree and nearly half have a doctorate. Backgrounds in the fields of mathematics/statistics (32%), computer science (19%), and engineering (16%) are common to nearly 70% of data scientists. All data scientists must also have a level of coding proficiency.

Data by itself can be quite useless. Those in the field must have the non-technical skills to find value in a sea of data. Some of the more useful, but less apparent, skills include harboring an intellectual curiosity with a profound acumen for business. Communicating one’s finding might be just as important. While this will benefit timely decision making, it will also go a long way toward inter-departmental information dissemination.

Information is About More than Just Numbers

As artificial intelligence (AI) and machine learning become more prevalent, data becomes increasingly dynamic. Scientists in data related fields will need to look at how information flows in new ways. Network isolation is a primary concern in a world that is embracing cloud-based technologies more and more.

Issues that arise from network isolation can be solved with passive monitoring. The addition of a passive network TAP (test access point) enables the dual monitoring of both inbound and outbound data. This will satisfy the majority of sniffer software that necessitates two network cards. In this way, a network can be more efficiently analyzed and debugged. For instance, a passive optical TAP provides a copy of all traffic without disturbing a system. It also does not need additional power or management.

7 Skills Every Data Scientist Needs

Forbes lists five skills that are considered to be a fundamental part of all data scientists skill sets. These and two others are described here.

1. Quantitative Analysis

The job of a data scientist is to understand the behavior behind complex systems. This is achieved by analyzing data. Findings need to be quantified to be useful.


One of the unavoidable technical skills is programming. Python and R are the most common. Others include Hadoop, SQL, and proficiency with unstructured data.


As mentioned previously, finding the value in the data is as essential as any other skill. Useless data equates to wasted time. All data scientists need non-technical skills in terms of the intuition to find value and communicate it. Engineers and product managers are reliant upon what a data scientist deems important. Their methods will depend on these findings. Intuition will also be integral to making approximations and where they make sense.


Without proper communication, potentially useful data can go to waste or lose some of its time sensitivity.


Just like it is the job of a data scientist to understand the behavior of systems, one must also have perspective. Knowing the team that will implement the recommended changes may, in the end, determine success or failure.

In addition to these five skills, data scientists will need to expand their talents to incorporate AI and the diverse applications of modern data streams.

6.Machine Learning / AI

While both AI and machine learning are inexplicably tied, there is a difference between them. Innovations in the types of interfaces for AI continue to be explored. Augmented reality, in particular, can help extract better value from network data.

7.Education/Willingness to Learn

Because of the amount of information flow, most data scientists are highly educated. Constant increases in data and its applications make it difficult to predict how data will be used in the future. This is a bit ironic since data scientists themselves are in the business of forecasting.