What is Data Science and Why is it Important?

By Michelle Tran  |  September 7, 2020

How do businesses find success in their field? How do they keep atop current trends, consumer demands, and all while mitigating as much risk as possible?

Turns out all such key decisions come down to a science: data science, that is.

When navigating the challenges of financial budgeting, product development, and organisational growth – data science helps streamline the process through its management and analysis of all available, relevant information.

We break down the intricacies of the practice below, the average day of a data scientist, and notable current and upcoming trends in the industry.

What is Data Science?

The field of data science is dedicated to gathering and organising massive sets of raw data, then analysing the information for any concurrent patterns, trends, and other meaningful insights. In today’s modern, digitised realm of business – data scientists make use of complex tools and algorithms to mine and thoroughly examine the raw information they’re after.

Since most businesses can benefit from examining data on their target markets and industry trends; data science is currently in demand across a wide range of industries. Some of the highest-employing fields currently include the logistics, manufacturing, financial services, information technology (IT), and government sector.

Experts have outlined four pillars of expertise that comprise the practice: computer science and IT, math and statistics, business knowledge, and written/verbal communication. While having strong skills in all four areas is ideal, real-life data scientists have typically mastered one or two areas, while continuing to build their proficiency in the others.

Though sharing striking similarities with the statistics field – data science includes predictive analytics, as professionals view the data from multiple angles while determining potential future trends. The field additionally makes use of “prescriptive” analytics (where actions and related outcomes are not only predicted, but suggested), and machine learning technology to assist with these forecasts and discovering data patterns.

Why is Data Science Important for Business?

As mentioned, data science practices can offer varying business advantages.

For one, business leaders can make more accurate, well-informed decisions using the insights gathered by data scientists. It is estimated that roughly 80% of the world’s data is unstructured, requiring complex methods to organise and properly extract meaning from such information. Data scientists can ease this process through their skills in maths, statistics, and IT – making structured sense of raw data and creating predictive models to forecast a wide range of possibilities.

Organisations can then use these insights and predicted outcomes to pursue the best solutions possible. Leaders can better pinpoint areas of improvement and the most effective actions to take, helping spur business growth and efficiency.

Data science can also help companies analyse their market – gaining insights on competition, consumer demands, and industry trends. With more thorough examinations of their target audience, businesses can determine ways of improving product quality, services, and existing business processes – helping them improve customer service and satisfaction.

Furthermore, data scientists can ensure companies are hiring the best talents possible – using information gathered from social media, job sites, and corporate databases to verify the best-fitting candidates for available roles.

Not only will the process find potential employees who best fit the required skillset, but also those who may best match the company culture. Additionally, applying data procedures effectively streamlines and accelerates the recruitment process; benefiting a business when new or empty roles need to be filled quickly.

What Training Do Data Scientists Typically Have?

Most successful data scientists typically hold a post-graduate or graduate diploma, though plenty enter the industry with a minimum of a bachelor’s degree.

However, many can start exploring the field through related online courses. Since a wide breadth of programming skills is expected in this profession, aspiring data scientists may consider a programming or general IT training. Equipping oneself with the basics in foundational languages such as Python, R, and SQL is crucial. Coding bootcamps are also recommended for a faster, more intensive learning experience; as well as participation in online communities such as GitHub to network and learn from professionals around the globe.

Pursuing a course in mathematics, physics, and statistics is also recommended – as these areas possess key analytical skills to succeed in the field. For a more all-encompassing training experience that specifically focuses on a data science career pathway – online programs that concentrate on the profession (such as Lumify Learn’s Certified Data Science Professional course) are also available. However, experts suggest additional training in the aforementioned fields to build a stronger skillset that stands out in the job market.

Formal qualifications aren’t everything, however – as employers will still look to your soft skills and experience when seeking out ideal candidates. Having a robust portfolio of related projects (whether from personal pursuits, courses, internships, or volunteering opportunities) can thus come in handy with employment, as well as excellent skills in perceptiveness, communication, and creativity.

Current Trends in the Data Science Field

The field of data science shows massive promise for future employment, with 76% of businesses planning on increasing their analytics investments for the next two years (according to Deloitte statistics). SEEK also predicts a job growth of 12.9% in the coming five years, with a potential income of $130,176 per year for those with postgraduate qualifications.

There’s thus never been a better time to dive into data science – and with technology only set to progress, aspiring professionals can expect further growth in opportunities. Among other recent advancements – virtual reality, the Internet of Things, and social media have already made significant contributions to the growth of big data; further pushing demand for skills in data science.

In fact, 2018 LinkedIn statistics show that data mining and statistical analysis had ranked as the second-most in-demand skill among job advertisements posted on the platform.

Employers also value one’s commitment to lifelong learning, as the field will constantly need to keep up with the current rapid pace of technological change. This involves updating one’s skills in programming languages, data management tools, business intelligence, and machine learning to keep in line with evolving industry demands.

Finally, it’s important to note that data science practices aren’t exclusive to the role or title of a “data scientist”. According to the Australian and New Zealand Standard Classification of Occupations (ANZSCO), the following IT occupations currently have high job opportunity for those with skills in data science: ICT managers; actuaries, mathematicians, and statisticians; ICT business and systems analysts; software and applications programmers; database and systems administrators; ICT security specialists, and computer network professionals.

Aspiring data science professionals can thus find employment in a wide variety of roles and industries – opportunities that may only grow in time as business grows ever-more digitised.

Keen to Explore the World of Data Science?

As mentioned, those interested in the field of data science can start their career journey through exploring online courses.

Lumify Learn has a launched a brand new Certified Data Science Professional Course to get beginners started. Strengthen the analytical, statistical, and IT skills required for a successful career in the field – and all through a completely online training experience; helping you tailor your studies around personal needs and schedule.

Dive into the expansive world of data science today – and enquire with us on a course.

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