Top Data Science Skills in Demand
We’ve been hiring data people for longer than most noughties SMEs have had AWS in place! We’ve learned what employers are looking for & while this evolves with the tech stack –below are some of the top data science skills employers look for! In no particular order…
Programming: Data scientists need to be proficient in coding using various programming languages. Some of the commonly used languages are Python, R and SQL. MATLAB is utilized in academic instances and often in bioinformatics. That said – general consensus is – Python is where it’s at!
Statistics: Statistical tools like distributions, statistical tests etc. are extensively used by data scientists. Therefore, a good understanding of statistics is essential. Most employers look for a base degree in maths, physics, statistics, or computer science. That’s not to say that other data intensive domains academic backgrounds won’t give you these skills however. Statistics is central to great data modelling & critically data preparedness!
Machine learning: Machine learning can be used either in classification or regression tasks. Both of these sub-specialties can be used by organizations to forecast or optimize or classify sensitive data and draw insight from it. Employers are using ML in most Data Science projects in some form and ML is becoming its own arena of data specialism.
Fundamentals of mathematics: The basics! Data scientists should be able to do basic number crunching, calculus and linear algebra. Machine learning algorithms are based on calculus and linear algebra, so a solid understanding is required of these mathematical tools.
Data interpretation skills: Data scientists need to visualize data; thus, skills are needed in Excel, SQL and Python… python 😉!
Data preprocessing skills: Data might be messy, noisy, filled with gaps and have outliers. Some level of pre-processing is required to clean the data. Data scientists should be able to pre-process data efficiently – this is one of the major gripes employers have with technical interview processes. They often see that candidates have “gone down the rabbit hole” creating amazing models – but their basis treatment of the data is the arguably as important as the modelling itself.
Problem solving skills: Data scientists need to be proficient problem solvers, think outside the box and have logical reasoning.
Communication skills: Data scientists need to be skilled communicators, be able to break down the problem into manageable tasks and need to drive project management from start to finish. Often the data science problem will require conflict resolution, and interface with other departments and external parties.
Business skills: So I’ve left this to the end because not all data roles require commercial skills. It’s more relevant to mid senior and upwards data roles where products are perhaps being created. That said – in consulting data science roles – being engaged and understanding the business needs is key. In general – employers like to see that data employees can evaluate the use of data in business processes, get to know workflows & suss out what provides commercial impact and not. RoI for data – is top of the agenda for CTOs & CDOs in 2020/21- so it’s not harm to sharpen your business skills & show an interest in departments other than your own!
Proficiency in the skills mentioned above will give you a surefire way to enter the data science market or for those hiring – these are some of the things you ought to be looking out for. The market is currently exploding with opportunities and will remain in-demand for some time to come.
Adrian Clarke is Associate Director at IT Search and focuses solely on the recruitment of data science, AI, VR, data analytics & data engineering talent in all industries and consulting with clients to solve their organizational challenges through the appointment of great data people.