What are Data Analytics, Data Science and Machine Learning and What Does your Organisation Need?
Data analytics, data science and machine learning are hot topics and companies are looking for individuals to spearhead the adoption of these roles into business operations. In fact, in a report conducted by IBM, the company predicts that as the New Year rolls around, there will be around 2,720,000 job openings compared to just 364,000 in 2019 within these fields. With immense growth opportunities in all three sectors, it’s first important to break down what these fields really entail, how recruitment within them will shift and why businesses should care.
Firstly, what exactly are data analytics, science and machine learning and what are the differences? Data science, in layman terms, is the meeting point between math and statistical knowledge, substantive expertise and hacking skills. Therefore, strong skills in Python, SAS, R and such, including SQL familiarity and experience in working with unstructured data make up the majority of what data science is all about. On the flip side, data analysts structure their work around statistics and visualise data and make sense of numbers. Data analytics requires individuals to be able to view numbers and possess sense-making abilities by working on databases. Data analytics and science go hand in hand as data scientists are required to have a standard level of data analytical capabilities. Lastly, machine learning is the process of utilising algorithms to learn from data and unearth predictions through it. Machine learning requires a concrete understanding of statistical-based learning as well as a sense of predictive analysis to spot patterns and make data-backed assumptions. Massive data-deriving platforms such as Facebook and Netflix are great examples of machine learning that change what users see based on behaviours on the platform. For any individual wanting to excel in understanding machine learning capabilities, having a strong understanding of programming skills, data modelling, statistics and mathematical prowess as well as computer fundamentals is crucial.
With a 4.3/5 job satisfaction and a starting salary at USD108,000, data scientist professions rank as the best job on GlassDoor for 2019. Data science professionals are becoming the bloodline of many organisations given that data drives some of the biggest decisions. Data scientists don’t merely recognise patterns in numbers. Rather, they are tasked with unearthing creative findings and observations and add value to data. Data scientists are incredibly sought after because they are required to find unique conclusions and effectively communicate them, as well. These decisions shape the business and how it will scale in the future. A data scientist’s skill set should ideally be split 60% hard skills, 20% soft skills and 20% with the ability to apply their knowledge on a per-situation basis.
If your organisation is hiring for data scientists, make sure your talent has the three C’s: curiosity, creativity and critical thinking. Data science is as much an artful profession as say, a graphic designer. Finding business-changing solutions starts with curiosity as it is the starting point to ask relevant questions that need addressing. This is followed by critical thinking to assess the problem and creativity to find innovative solutions and communicating that to the wider business.
Data scientists shouldn’t only be versed in tech-speak. Instead, they should be able to break down the complex solutions into understandable concepts to the rest of the team. As such, the creativity comes to play again because data scientists should be able to tell a story with the answers they have found. These “stories” then have to be turned into actionable points because they have to be the driver of change. These can be in the form of graphs, illustrations and relationships within different operations of the business.
Just as it’s important for data scientists to be able to look at massive data sets, they should also have the technical skills to look at the right type of numbers. Big data sets are quite common across businesses and it’s easy to get lost in them. As such, it’s important for data science professionals to be able to have the capacity to critically look at large sets of numbers and pick the data that would make the most sense to evaluate.
Data scientists are vital to the success of businesses because they have the power to illustrate company projections and offer solutions for growth. In saying that, data science roles must be carefully curated and the hiring process must be just as meticulous. If you’re looking to invest in data scientists, seeking advice from professional recruiting agencies who understand the requirement of such a technical field is imperative. Don’t go in blind – trust in the experts who can understand your business needs and can connect you to the right professionals to realise the growth potential you are after.
Interested in joining the industry? Reach out to the recruitment specialists at Ai Group Talent Solutions. The Talent Solutions team provides practical advice to organisations around their hiring and onboarding needs as well as career transition services.