There’s quite literally nothing more potent in power than a company that not only understands its data, but also knows how to utilise it to its full effect. Understandably, this has created a significant demand for personnel that are armed with the skills to keep up with the evolving world of data science.
So, if you’re thinking of becoming a data scientist, you’d be right to think it’s a profession that requires you to develop your expertise. While there is an enormous amount of data science skills you can pick up, it’s vital that you get the fundamentals under your belt before delving too deep too soon.
To give you the best chance of standing out above your data science peers, we’ve compiled 6 of the most essential data science skills for you to get your head around. Take a look through our list to learn more or visit our Career page to find out all kinds of other useful tips for your career development path.
It probably comes as no surprise to you that programming is at the top of our data scientist skills list. No matter what company you’re applying to work for, if you don’t have a strong understanding of programming then you’ll find things an uphill struggle.
It’s important to remember that as a data scientist, you’re not just a mere statistician; you are, in fact, much more than that. More often than not, you’ll be expected by employers to have a firm grasp over at least one statistical programming language, including: R, Python, SAS, and SQL.
Once clued up with a programming language, you’ll be able to confidently analyse large data sets and enhance the methodology your company follows. Additionally, you will be able to create specific tools that allow you to improve the way you and your company analyse data.
Are you someone that likes to tackle a problem head on, no matter how challenging? Well, if you are, you’re going to get on well with data science and most prospective employers. Data science can be very mentally demanding and requires a systematic approach at all times. You may even find that you are tested with a high-level problem during an interview just to see how you respond.
Should you end up in a situation, for example, where you company is looking to run a test or develop a data-driven product, it’s vital that you take the time to really think about what’s important and what isn’t. While this may sound obvious, as a data scientist you need to forensically examine everything, at every stage of any process. Overlooking one thing could undermine a lot of work, time, and financial investment.
Machine learning falls under the umbrella of quantitative analysis and its value shouldn’t ever be underestimated. Not every data scientist will be responsible for implementing machine learning models themselves, but knowing how to is an obvious advantage in terms of your career development.
By understanding the intricacies of machine learning, you’ll be able to create prototypes in order to test data assumptions, create new and innovative features, and accurately identify the weakest and strongest areas of current machine learning technology.
Defining metrics is a traditional analytical skill set that has a substantial purpose in the world of data science. Essentially, honing your skills in this way will allow you to define primary and secondary metrics, which translates into accurately tracking the success of various company objectives and data driven products.
There’s nothing more dangerous for a company than following the trail of misleading data; especially if these false data results amount to investment of any kind. So, what’s the solution? Well, if you end up looking at some fairly incredible results, they’re probably too good to be true. Having a good understanding of product knowledge will allow you to quickly make sanity checks and back-of-the-end calculations that could be lead to you identifying any inaccurate results.
Data visualisation and communication
Knowing how to visualise and communicate data is incredibly important, even more so if the company you’re working for is still new to making data-driven decisions. Communicating your work will require you to be clear with any descriptions on your findings and methods in a way that both technical and non-technical personnel can understand. As far as visualisation goes, get to know the tools of the industry, such as matplotlib, ggplot, and d3.js. But, you’ll only become competent in this area if you know and fully understand both components.
Are you interested in becoming a data scientist or do you have further questions about the data science skills we’ve listed here? To find out more, please contact a member of our team now.