Data science is experiencing an unprecedented level of growth. According to LinkedIn’s 2023 Job Trends Survey, data scientist is the third most sought-after job in the United States and the seventh most sought-after job in the United Kingdom. Market projections indicate that this area of expertise is set to expand by 26% over the next five years.
The rapid emergence of data science can be attributed to a number of factors, chief among them being the increasing prevalence of a data-driven mindset. We have become accustomed to making decisions based on facts and figures, thus creating a demand for experts who are capable of collecting and interpreting data.
The expansion of data science may also be attributed to the following:
The state of technology is improving.The advancement of technology has enabled us to collect and analyze massive quantities of data. Machine learning, Artificial Intelligence and cloud computing are all advanced significantly by having a knowledgeable data scientist in charge.
Supplemental Information:The sheer quantity of data being created is staggering, with estimates suggesting that every human being on the planet generates an estimated 2MB of data per second. Such an immense volume of data is difficult to comprehend. An increasing number of organizations are collecting data, and this necessitates the employment of more data scientists to process it.
The availability of large amounts of data has been simplified.Data sharing has seen a marked rise in recent years due to the increased availability of data and the proliferation of advanced technological infrastructures. Investing in big data and the personnel to effectively utilize it is now accessible even to smaller companies.
It is widely accepted that data scientists should have a good command of both Python and R to be successful. While these languages are currently the most popular, there is a potential for Julia to become the primary language for data scientists if current trends continue.
It is clear that technical expertise and numerical aptitude are essential for a successful career in data science. However, to gain an advantage over competitors, is there anything else a data scientist should be on the lookout for when looking to hire prospective workers that could provide a valuable complement to their existing skills?
Structured Query Language Data Sources
It is essential for a Data Scientist to have an in-depth understanding of databases, particularly relational databases, which are the norm within the industry. Whilst it is not necessary for them to be a Database Engineer, they should be competent enough in relation to schemas, tables and queries to be able to complete basic data management duties.
Database administration and design are two distinct areas of expertise. Having a clear understanding of entity-relationship diagrams at the outset can be critical to the success of a database project. Data scientists who have a good knowledge of SQL are more likely to take a measured approach, thus making the most efficient use of time and resources.
As the IT sector increasingly adopts service solutions such as SaaS, organizations operating in the cloud can benefit greatly from employing a cloud engineer. Furthermore, data scientists should have a thorough understanding of the basics of cloud computing.
Cloud platforms such as Amazon Redshift from AWS and H20.ai are enabling automation of the most complex data science and machine learning processes.
Data scientists who are looking to leverage the cloud for their work must be knowledgeable not only of the tools they will be using to analyze data stored there, but also of the associated costs and benefits of making changes to their computing infrastructure, such as increasing memory or processing power.
Storytelling via data visualisation
In the past, it was thought that data scientists were akin to wizards or wise hermits, spending their days immersed in data before presenting their findings. What was most important was that the end results were useful to those making the decisions, regardless of the process used to reach them.
Now data scientists are no longer seen as solitary figures with unique skills. Data visualization and storytelling become essential when the majority of those working with data must develop sound conclusions and present them to their audience.
For those with a mathematical background, numerical data in a table can provide sufficient insight into the underlying meaning. However, for the majority of us, it can be helpful to supplement this information with alternative representations in order to gain a more comprehensive understanding.
Data visualization is the practice of designing visual representations (e.g. charts and tables) to communicate information. In the past, pie charts were commonly used for this purpose; however, this has now changed.
When deciding how to present data, there is a unique challenge. A competent data scientist will take into account the mindset of their audience when making decisions about how to present information. Depending on the people being addressed, the same piece of data could be framed in different ways.
As a data scientist, it is important to be able to visualize the story that the data is telling. Their role is comparable to that of a storyteller, in that they are responsible for incorporating the insights from empirical data into the company’s wider narrative. This often involves questions such as, “How can their work help our organization reach its goals and move closer to its vision?” and “What is the best way to present the data to align with the language of the business?
At the beginning of this discussion, we highlighted how the increasing reliance on data-driven decision making has contributed to the heightened prominence of data science. In order to provide peace of mind to stakeholders, it is essential to have more than just a reliable model.
A data scientist needs to be aware of the wider context in which their work sits in order to ensure that the data they evaluate, the models they develop and the conclusions they draw are useful and applicable in reality. Merely achieving accuracy is not enough; their work must be practically useful.
It is of no benefit to identify a variable that predicts customer behavior if there is no way to modify it. For a data scientist to be able to make informed decisions concerning their tasks and present solutions that meet the requirements of those without technical expertise, they must first have a good knowledge of the business strategy.
Efforts to inspire a desire to learn
The field of data science is expanding rapidly, with newer alternatives being adopted and older ones becoming obsolete. It is therefore essential for data scientists to remain aware of the most recent methods, software and hardware that could enhance their analysis.
There has been an increase in the number of master’s degrees and data science concentrations available, yet their quality can vary greatly. Recently, we have seen an influx of scientists from various backgrounds entering the field of data science and universities have been accommodating this trend.
Hundreds of books have been published on the topic, alongside conferences, exhibitions, websites, online forums and social media platforms. As such, data scientists have no excuse for not staying up-to-date with the latest literature.
When recruiting data scientists or considering outsourcing this work, it is essential to look beyond the qualifications and skills listed on a CV. The value of a team member is greatly enhanced if they have additional abilities that make them stand out from the crowd.