Half the term has passed, and you’re scrambling to watch the week two lectures at 2x speed, contemplating your life choices because you’re struggling to keep up with the boring burden of your uni degree. Or maybe you’re that top HD student who goes exploring beyond the scope of the course outline, with plenty of time for work, socialising, and making yourself competitive careers-wise.
Regardless, data science is a field that everyone should consider in this day and age.
Why you should be interested in Data Science
Don’t get me wrong – by no means am I telling you to switch degrees (totally unbiased, as I don’t study data science myself). But learning some related skills would be incredibly useful for your future aspirations. You might argue: “why would I bother doing that if it’s not even relevant to my degree?”. Well, data science has penetrated into all industries as everything is data-driven in this digital world. With ever-increasing amounts of data around us, there’s a high demand for people who can interpret this raw data and generate meaningful insights.
If you don’t believe me, just Google “why study data science”, and you’ll discover numerous reasons why it’s definitively labelled ‘the sexiest job of the 21st century’. Sounds too good to be true? Overhyped? It may be challenging, but certainly not mundane, as this relatively new field is 2nd in Glassdoor’s 2021 rankings of the top jobs in the US. Consequently, many jobs out there require some extent of data literacy, and the reality is that employers won’t really care about your degree much. Instead, you will stand out amongst other candidates if you’ve upskilled for your desired role, and of course, demonstrate your passion.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” (Geoffrey Moore)
What are some skills that you should know
I’m no expert, so I’d highly recommend reading this article here, which is a complete beginner’s guide to data science. Although this field is an amalgamation of computing, mathematics and business, the career pathways do not require the same level of expertise. Having said that, it would be worthwhile to have some fundamental knowledge in:
- Programming (Python and R, with their included libraries, are most commonly used for data wrangling and analysis)
- Statistics (maths forms the foundation of building various models and performing quantitative analysis)
- Data Visualisation (representing data in a graphical format is necessary to easily identify and understand trends)
All this might sound overwhelming at first, especially if you’re trying to learn it independently – but it’s all part of the learning process. There are several free online courses (such as Coursera, Udemy, Udacity) where you can go at your own pace. Once you’ve gained proficiency in an area, try to build a few personal projects. The cool thing is that you can apply your skills and draw conclusions from a dataset of your own liking.
At the same time, it’s redundant getting carried away with the whole technical skills aspect. Many students undermine the significance of effective communication, yet it is important to be able to clearly articulate your findings in a concise manner to both technical and non-technical audiences. Also, while processing databases and visualising dashboards is ultimately the purview of a data scientist, it’s not something that we should drill into initially. For the layman, the focus would be more on intellectual curiosity, since the quality of one’s insights to discover those underlying truths is often motivated by their mindset of constantly asking the question “why”.
Feeling scared at the workload? Again, don’t be let down, particularly if you’re not studying a data science related degree – it’s an extraordinary step you’re taking just to obtain a glimpse into the fascinations of this broad profession. You’d be surprised at the number of people who are data illiterate and fail to interpret the most basic graph. However, the next time you procrastinate (which is probably multiple times a day for the majority of us…) just remember one thing: it’s never too late to start getting involved with a job of the future!
If you want to know more about the diverse opportunities of data science, be sure to check out DataSoc’s Careers Guide!