Finding a balance between technical and soft skills in data science

Everywhere you look, the demand for data scientists and analysts is high. This demand is driven by a revolution in computational statistics combined with massive amounts of data which is collected, stored and archived. Traditional methods or technologies were unable to deal with this level of data complexity. This complexity requires new approaches and specialised training.  

The 2017 Skills and Salary Survey Report found that analytics jobs were privy to the highest pay increases within key roles and emerging talent. LinkedIn reported that statistical analysis and data mining were the most in-demand skills that employees were on the hunt for. Furthermore, Glassdoor stated that “data scientist” was the best job in terms of job satisfaction, salary and job opportunities. Business is waking up to the fact that you can do a better job with algorithms and data than just guessing.

With such strong demand, it would stand to reason that anyone with analytical abilities should get on board, right? Well, not quite. It’s true that there are fantastic opportunities and salary benefits to be found within the world of data science and analysis. But that doesn’t necessarily mean it’s the playground for every professional (or, at least, not without the correct training).

The skills required for data scientists to successfully navigate the world of data is twofold. It requires a balance between both technical and soft skills – a balance that can prove quite tricky for many to strike. Without the right balance, professionals working in this field are far less likely to advance their careers past a certain level. You can do a better job with algorithms and data than just guessing.

Data scientists: the key skills

There are particular technical skills that are crucial for any data analytics professional to possess; ones you may already be aware of. These include:

Coding and programming

When you’re dealing with big data sets, the ability to understand distributed computing, coding and statistical learning are essential. You may not need to be fluent in every programming language, like Python (which is fortunate, since that would be near impossible), but investing in learning the most important and common ones is definitely a good move.

Quantitative analysis skills

By using objective reasoning and numerical skills, quantitative analysis enables you to competently interpret and understand data sets to ask the interesting questions.

Multilingual across technologies

Big data analysis can be incredibly diverse. It’s important to possess the ability to work across various technologies, including different software, hardware, tools and platforms.

Business savviness

Being able to understand a business and their desired outcomes is another incredibly important skill for data analysis. Data can be interpreted, stripped down and understood until the end of time, but without the ability to relate it back to the relevant business, that analysis is essentially pointless.

Combining soft skills with technical skills

The technical skills outlined above are all more commonly known among data professionals. For a long time, they’ve been the only focus of data scientists and analysts; but it’s becoming more and more evident that soft skills, such as communication skills, are just as integral for success.

This gap emerged as businesses rapidly adopted data analysts into their teams. It was a necessary move, particularly with the ever-growing trend in data generation. However, with data science now becoming a more actualised and settled industry field, what’s becoming clear is the need for improved interpersonal skills.

Jodie Sangster, CEO at the Institute of Analytics Professionals of Australia, explains it like this: “while many more organisations now have an analytics team, it is soft skills, like communication, influencing and advocacy, that will ensure the business can take action on analytics-driven insights. These are the essential skills for analytics professionals in 2018.”

By improving the soft skills of your analytics team, your business increases its ability to act on insights and chances of success.

How can you develop soft skills?

There are a number of ways to improve your soft skills, but here are a handful of our tips:

1. Look out for group opportunities

You may have cringed at group assignments at school or university, but in the quest to develop better soft skills, they can really be your friend. Group participation enables you to build skills in navigating multiple (and sometimes conflicting) personalities, deliver compromise solutions and help resolve disagreements.

2. Get a mentor and meet regularly

Another way to boost your communication skills is to find a mentor that you trust and respect, then meet up with them regularly. The latter is integral; if you simply check in with your mentor through emails, you’ll miss out on an opportunity to further practice those face-to-face skills.

3. Be flexible

Nobody likes working with someone who is rigid in their approach. Be open to change and try to adapt to new situations (a valuable skill in any industry) and you’ll find your interpersonal capabilities are bound to grow.

4. Don’t take things too seriously

As inflexible people create a less-than-ideal work environment, people who lack a good-natured approach to themselves and their work can have a similar effect. This doesn’t necessarily mean you have to crack jokes, but being light-hearted (particularly on topics that aren’t too serious) and letting things go when appropriate is a valuable skill to develop.

5. Improve your skills with further study

There are courses specifically designed for improving your skills in data analysis, even for those working within the field already. Contemporary courses recognise the importance of professionals needing to strike that balance between their technical and soft skills and incorporate such learnings into their curriculum. 

The benefits of study to build data science skills

Further study can help to develop the skills needed to navigate the complex world of data science, both from an analytical and interpersonal perspective. In the report The future of work: Occupational and education trends in data science in Australia by Deloitte Access Economics, it highlights two key benefits:

• Increased earning potential, and
• Broadening career pathways.

With so many fantastic potential benefits to gain, completing a postgraduate degree in a field that shows no sign of slowing down seems like a worthwhile investment. Online study in data science makes it even easier for you to broaden your education, skill set and knowledge, and become a powerhouse player in the data science world. Learn more by contacting our Student Enrolment team on 1300 535 919.