7 Must-Have Skills to Get a Job as a Data Scientist
Everybody and their mother wants to learn data science. And there’s no reason not to – the job you do is interesting 95% of the time, the salaries are excellent, and most likely you can get the job done from the comfort of your bed.
Today we’ll go over seven essential skills every data scientist should have. Here’s the complete list:
Tasks in a day-to-day data science job are often vaguely defined – at least at the beginning of the project. More often than not, to provide any benefit from a data science solution, the data scientist must have a lot of domain knowledge.
For example, how could you possibly develop credit risk models if you don’t know anything about the subject? Sure, you could do your best and follow well-established data science principles, but that can only get you so far. As a result, your models won’t work optimally, and you won’t know what to do about it.
That’s where creativity and critical thinking come into play. Data scientists have to distill a lot of information in a short time frame. Having a team of highly creative people might expose solutions that no one thought of before.
Critical thinking will help you dig deeper and always ask the right questions, and spot potential biases in the responses.
How much math you’ll use daily depends on your role. These four areas come up most often when looking at data science prerequisites:
It’s definitely not something you can pick up in a week, as every listed subject falls into a category of college-level math.
It doesn’t mean you should spend the next year or so learning these subjects in depth, but you should know the basics. If you’re after a junior-level position, basic intuition and understanding of the applicability in data science should do. If you’re after a lead researcher position, it’s expected these topics are second nature to you.
There’s at least a several year gap between junior and senior data science positions, so you’ll always have the time to learn and explore further. The best part is – you can learn everything entirely for free! Here’s a complete reference for beginners:
All of the math, stats, and critical thinking skills in the world won’t help if you don’t know how to express your knowledge through code. Let’s take a look at the most widely used languages in data science:
In a nutshell – Python and R are industry leaders. SQL is supposedly used more than R, but that’s likely for another reason, covered later in this article.
If you’re entirely new to programming, there are some great news – both Python and R are easy to learn. On the other hand, if you’re coming from languages such as C or Java, these two shouldn’t be a problem to pick up.
After all, Python was designed for teaching programming concepts to kids, so how complicated can it be for well-educated professionals?
As for R, here’s what you can do with it (assuming a basic knowledge of programming concepts):
To become an efficient data scientist, your data analysis and visualization skills have to be top-notch. Your results are here to tell a story, and nobody wants to read an incomplete and poorly presented one.
There’s a whole suite of data analysis and visualization packages available for both R and Python. R’s most popular analysis package is dplyr, and for Python, that’s pandas.
When it comes to data visualization, a lot will argue that R takes a point here. The visualizations look better, especially with the default stylings. It’s most popular visualization library is ggplot2, and we have an entire series to get you started:
To conclude – proper analysis and visualization skills are a must. It’s not enough to know how to write code, but also to ask the right question. That’s why creativity and critical thinking are so important.
