Data Science is among the IT sector’s fastest-growing disciplines. If you’re seeking a career as a Data Scientist (DS) or Machine Learning Engineer (MLE) right after your graduation, you should be aware of certain typical interview blunders. Here are eight common DS interview blunders to avoid:
1. Making your resume too complex:
Make your resume brief and straightforward. Do not include any more difficult or technical terminology in your CV that does not accurately represent your overall abilities. You may well have a lot of talents and abilities, but putting them all in one place isn’t going to aid you much. Rather, explain how you came up with an answer while attempting to solve an issue.
2. Fragmented and non-working hyperlinks on the resume!
This appears to be a standard check that everyone does, yet we’ve noticed a handful of resumes with faulty hyperlinks. You don’t intend to make a bad first impact on your recruiter by seeing Page Not Found 404, do you?
3. Bringing a ‘sloppy’ code
In a data science interview, you can be sure that you’re programming and analytic abilities will be called into question. The technological value you will provide to the firm will be demonstrated immediately through an algorithm coding exam. Creating careless code or code with far too many errors is probably among the last things you would want to do as a candidate! Recruiters frequently raise the issue of badly written computer code.
4. GitHub repositories with unfinished README.md
Most entry-level DS applicants believe that releasing their Jupiter notebook on GitHub will boost their status significantly! The HR/Non-Tech Scout, on the other hand, is likely to have no idea what a Jupiter notebook document is or how to access one. Therefore, make sure that all your GitHub repositories have a README file with a simple-worded overview.
5. Not realizing if they want to offer the job to a scientist or a developer
In many situations, they are looking for a programmer or coder who is also a scientist — in other words, a unicorn. You might well be capable to persuade them that you’re strong at both by highlighting your financial sense, which is supported by concrete facts and simple to measure with yield statistics.
6. Not ensuring a robust foundational knowledge
Many applicants skip through the fundamentals and go on to more sophisticated ideas. For example, instead of focusing on Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) designs, focus only on Transformers! Always refrain from doing so since the fundamentals will assist you in laying the groundwork for more complex concepts.
7. Not practicing common queries about your projects
Despite the fact that “could you tell me a little bit about this project?” is among the most popular interview topics, many applicants focus on the project presentation and stats, but only a few address the effect and problems they overcome!
8. Your Machine Learning prototype is not organized
Machine Learning’s purpose is to address an issue. And we’re able to do so after the algorithm is live and the consumer is using the forecasts. As a result, preparing to implement an ML model in a real-world scenario is worthwhile.
