Going Beyond the Code: Essential Competencies for Data Scientists

The field of data science is currently witnessing an unparalleled surge in growth. LinkedIn’s 2023 Job Trends Survey ranks data scientist as the third most in-demand job in the US and the seventh in the UK. Furthermore, market estimates have projected a 26% growth in this specialised domain over the course of the next five years.

The swift rise of data science can be attributed to multiple factors, with the growing prevalence of a data-centric mindset being the most significant among them. In contemporary times, we rely on data-driven insights to make informed decisions, thereby generating a demand for professionals who possess data collection and interpretation skills.

The growth of data science can also be ascribed to the following factors:

  • The current technology landscape is continuously evolving.

    Technological advancements have allowed us to gather and analyse substantial volumes of data. Having a proficient data scientist in charge, the fields of machine learning, artificial intelligence, and cloud computing have all progressed significantly.
  • Additional Context:

    The quantity of data being produced is simply overwhelming, with projections indicating that each individual on the planet is responsible for generating approximately 2MB of data every second. This tremendous influx of data is hard to fathom. As an increasing number of entities gather data, it becomes necessary to engage more data scientists to manage and analyse it.
  • Data accessibility has been enhanced by modern technology.

    The sharing of data has grown substantially in recent times due to the widespread accessibility of data and the emergence of sophisticated technological infrastructures. Big data investments and the necessary workforce to efficiently leverage it are now within the reach of even smaller businesses.

It is commonly acknowledged that a working knowledge of both Python and R is crucial for data scientists to succeed. Despite the fact that these programmes are presently the most prevalent, there is a prospect for Julia to become the de facto programming language for data scientists if current patterns persist.

While it is evident that technical proficiency and numerical aptitude are vital to excel in data science, are there other traits a data scientist should keep an eye out for when searching for potential employees who could bring added value to their skills?

Data Sources for Structured Query Language

For a Data Scientist, a thorough comprehension of databases, particularly relational databases that are standard within the industry, is crucial. Although being a Database Engineer is not a prerequisite, data scientists should possess the necessary skills to navigate schemas, tables, and queries to accomplish essential data management tasks.

Database administration and design are two separate areas of expertise. Familiarity with entity-relationship diagrams at the outset can be critical to the success of a database initiative. Data scientists equipped with knowledge of SQL are more likely to approach challenges methodically, helping them to make the most efficacious use of time and resources.


With the IT industry progressively embracing service solutions like SaaS, companies operating in the cloud can significantly gain from engaging a cloud engineer. Additionally, data scientists should have a comprehensive grasp of the fundamentals of cloud computing.

Cloud providers such as AWS’s Amazon Redshift and H20.ai are facilitating automation of the most intricate data science and machine learning procedures.

Data scientists who plan to harness the cloud for their tasks should possess an understanding not only of the tools they will employ to examine data stored there, but also of the advantages and costs involved in altering their computational infrastructure, such as enhancing memory or processing power.

Data Visualization as Narrative

Previously, data scientists were regarded as magicians or sages, spending their days exploring data before presenting their discoveries. The crucial factor was that the finales assisted decision-makers, regardless of the approach employed to arrive at them.

Data scientists are no longer considered to be solitary figures with exclusive abilities. Data visualization and storytelling have become crucial skills when most individuals working with data are required to formulate sound conclusions and communicate them to their audience.

Individuals with a mathematical background might find that numerical data in a table provides enough comprehension about the underlying meaning. Nonetheless, for the majority of people, it can be useful to supplement this information with other depictions to achieve a more all-inclusive understanding.

Data visualization involves producing visual depictions (such as charts and tables) that impart information. Previously, pie charts were commonly employed for this intention; nonetheless, the situation has now changed.

There exists a distinct challenge when determining how to portray data. A proficient data scientist will consider the perspective of their audience when deciding how to convey information. Depending on the individuals being addressed, the identical piece of data might be framed in various ways.

For a data scientist, the ability to envision the story that the data is communicating is crucial. Their function is analogous to that of a storyteller, since they are accountable for integrating the findings from empirical data into the broader narrative of the company. This frequently necessitates inquiries such as, “How can their work assist our organisation in achieving its objectives and coming closer to its vision?” and “What is the most effective approach for presenting the data to match the language of the business?”

Marketing Strategy

At the outset of this conversation, we underscored how the growing dependence on data-driven decision making has resulted in the increased significance of data science. Ensuring peace of mind among stakeholders necessitates more than just a dependable model.

To ensure that the data being assessed, the models being developed, and the conclusions being drawn are useful and applicable in reality, a data scientist must be conscious of the broader context within which their work exists. Mere accuracy is insufficient; their work must also be pragmatically beneficial.

Identifying a variable that predicts customer behavior is of no use if it cannot be changed. To enable a data scientist to make informed decisions regarding their tasks and propose solutions that fulfil the needs of those without technical know-how, they must first possess a sound understanding of the business strategy.

Initiatives to foster a passion for learning

The realm of data science is advancing swiftly, with newer options being embraced and older ones becoming outdated. As a result, it is imperative for data scientists to remain up-to-date on the latest methodologies, software, and hardware that might enhance their analysis.

There has been a surge in the quantity of master’s degrees and data science concentrations available, but their caliber can differ significantly. Lately, we have observed a rise of scientists from diverse fields moving into the field of data science, and universities have been accommodating this trend.

Numerous books have been written on the subject, accompanied by conferences, exhibitions, websites, online forums, and social media platforms. Thus, data scientists have no justification for not keeping themselves informed on the most recent literature.

When searching for data scientists or contemplating outsourcing this work, it is crucial to look beyond the qualifications and skills outlined on a CV. The worth of a team member is significantly increased if they possess additional proficiencies that differentiate them from the competition.

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