Niche down ASAP in Data Science!
Tweets to think about
People don’t enter the field anymore, because they think AI will take away DS jobs since it writes relatively good code.
As Santiago points out coding is just one part (smaller part) of the job. If you want to be the one who codes 24/7 DS is not your field.
DS is about the analysis and the takeaways. You need some statistics, mathematics, communication, presentation, data ethics, domain, and so much more skills.
AI is good at the basics, but when domain knowledge enters the chat AI leaves. Try to niche down early in your career and you will leave behind AI soon.
A related tweet:
The more you know the more you realize you don't know. That’s so true about Machine Learning and Data Science in general. It’s a broad field with all the mathematics, statistics, and coding.
As a beginner, it’s tempting to learn a lot of new things and try to be as fast as the technology around us. It’s impossible, so the sooner you realize, the better.
Again, try to niche down asap!
How can I compete with this?
I graduated 2 years ago, and have 2 years of experience. Just started applying to new roles.
I keep finding that nearly every role has a disproportionate number of applicants with master degrees, whereas I only have a BS.
I feel like experience matters more, but hiring managers will just decide to give the people with the masters more attention. Is this true?
Our view
While having a master’s degree can be beneficial, experience is also highly valued in the field of data science.
Practical experience, especially when it can be directly applied to the job at hand, can often outweigh the theoretical knowledge gained from a degree.
Some advice
If you’re currently unemployed, the first step is to get a job, even if it’s not directly related to Data Science. If you’re already employed, try to find ways to apply data science in your current role and then include it in your resume.
It’s highly beneficial if you can attribute a dollar value to your contribution in your current role.
There are data scientist resume templates available that you can edit and use depending on where you’re at in your data science career (entry-level, senior, or looking for a manager role). This will allow you to stand out by having a tailored resume.
A strong portfolio can be built for any type of role, including non-technical roles like product and marketing. The best portfolio projects share a few themes that can impress any hiring manager, no matter the field. Don’t forget the deployment part! This can make you stand out with respect to other candidates.
It could also be beneficial to learn multiple programming languages. For instance, learning both R and Python can be advantageous as both have their strengths. Also acquiring complementary skills such as marketing or financial knowledge, could be highly beneficial.
It’s important to understand the data science interview process and make sure you get prepared for each step.
Also learn soft skills, especially communication! The interview and partly a DS role is about communication. You need to sell yourself in a short amount of time in an interview. You cannot do that without good communication skills. Communication is also an important part of the job. Presentation, code explanation, educating others, etc. All requires good soft skills.
(Cover image created with Dall-E)