If you are thinking of joining the data science industry, but aren’t quite sure what skills and capabilities you will need, then read on.
The mass digitalisation of the workplace over the course of the last decade has resulted in the effective elimination of some roles, the creation of others and in the case of data science, there has been a boom. Often described as one of the fastest-growing careers, data science is a much sought-after role in 2024.
With technologies, processes and industry demands changing seemingly all of the time, the data science profession requires someone who is highly technically skilled, but who also encompasses a wide array of soft skills. So, in a role that is always developing, what skills are absolutely essential for success?
AI and machine learning
It is hard to remember the last time a technology role didn’t involve the need to upskill or reskill in the area of AI and machine learning and data science is no different. Professionals who want to excel and innovate in the modern-day data science space are going to need significant ability in this field.
AI and machine learning technologies enable data scientists to use algorithms to identify patterns, analyse and learn from data, make predictions, and solve complex problems. This is definitely an area of learning that anyone considering a career in data science should prioritise.
Programming languages
Programming languages are another staple for data scientists. They are also an opportunity to upskill in an area that not everyone will have expertise in, particularly if you choose a powerful yet not as common language.
When it comes to programming languages, data scientists should definitely aim to learn the standard, popular ones, such as Python, R, JavaScript, SQL and Swift. Much like AI and machine learning, it’ll be hard to get by as a data scientist without sufficient skill in and knowledge of a programming language, so if this isn’t a talent you have in your wheelhouse, why not check out some online courses and upskill?
Stats and maths
If you were one of the people who, as a teenager in school, asked yourself why you would ever need maths in your adult career, then you likely didn’t plan for a career as a data scientist, where a decent proficiency in maths is a must. Anyone planning to join the profession requires knowledge of a broad range of maths skills, such as algebra, calculus and statistics.
Probability and statistics are crucial to widening your capabilities within this area and can be used for the visualisation of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation and more.
Soft skills
I have always been of the opinion that, while practical skill will always get you the interview and your foot in the door, soft skills are what keep you there. Strong ability in communication, organisation, critical thinking, adaptability and flexibility, are just some of the skills that can help a data scientist progress.
Gone are the days in which tech employees are considered an isolated group who operate independently. Nowadays, data science requires a high degree of teamwork to collaborate on cross-functional projects, meaning soft skills that focus on networking, interpersonal skills and cognitive diversity are of great use.
Cloud computing
Cloud computing is becoming increasingly important to data scientists as it is used for small- and large-scale data storage, processing and analysis. It is a useful resource for experts who need to utilise huge datasets and perform complex computations, but may not have extensive storage infrastructure at their disposal.
By providing access to the various tools needed as a data scientist, cloud computing enables the scaling of resources, while providing powerful computing capabilities. Cloud computing skills that a professional in the field should consider learning include data acquisition, data cleansing, data transformation, model training and testing, and data mining, among others.
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