So you want to get hired as a data scientist, but just what skills do you need to qualify for the job?
At DataSoc, we care about you and your career opportunities, and we want you to get your dream job in your dream field (assuming it’s data science. This might not be the blog post for you if you’re an aspiring screenwriter). So, today, we present a checklist of essential skills for every data scientists.
Programming
Unsurprisingly, programming is rather important in the trade. Experience in programming will help a data scientist understand, plan, and organise data. It is also crucial in the analysis and visualisation of data.
Python holds a unique place of importance, being ubiquitous in almost every level of work with data. It is an easy, flexible language that offers a lot of utilities in deep learning within its libraries. Following in importance are R, SQL. Having knowledge of MATLAB, and other prominent programming languages such as Java, C++, Perl are very useful as well.
When it comes to programming, remember that it isn’t just about building something, but rather having your program be easily maintainable, scalable, and tested. Abide by the software design principles and you’ll be on a fast track to becoming the most popular programmer on the block.
Machine Learning
Now that humans have tricked computers into thinking for us, the next essential skill you will need is familiarising yourself with machine learning methods. This is especially true for companies that deal with insurmountable amounts of data: you wouldn’t want to be sorting through every user’s data at Netflix to handpick their next recommendations yourself.
Visualisation Tools
Being able to visualise data is crucial in the understanding and analysis of it. Images can communicate complex ideas and patterns quickly and concisely. Understanding data visualisation software such as Tableau, Qlikview could be crucial to breaking into the industry.
Maths and Statistics
Data science, like most contemporary sciences, is built upon the foundation of mathematics. All of the above skills are deeply rooted in maths: the basis of statistics will help you understand the data you see. Linear algebra and calculus are the backbone of machine learning. Discrete mathematics are the heart of computer systems.
While it is not necessary for you to become a mathematician, you should be able to grasp and utilise the foundational maths behind the tools and techniques you will be using on a daily basis.
Communication
Communication might not be a technical skill, but its importance cannot be overlooked. Having clear communication is key to teamwork, and you will be working in a team very, very often. Being able to establish effective client communication could save you and your team a lot of time and effort. Even the most brilliant, revolutionary project can go to waste when paired with poor pitching. A good presentation can be the difference between success and failure.
We hope the checklist has proved useful to you as a directional guide on which skills you’ll want to invest yourself in. If you have read the list and found yourself confident in your abilities, remember that DataSoc also has a job board which we try to update whenever we learn of new openings:
Good luck and have fun!