All Answers
Undergrad student
Hey, it is very important to have a strong profile, when you are sitting for a job interview and especially if it is in the field of data science, then it becomes even more important because data science is a field where there is a high neck to neck competition. Some career gurus claim that this industry has a high demand for professionals, but let me tell you, this is only half the information. No doubt, data science has a high demand for individuals, but it is for skilled professionals.
So HackerRank is a platform where a candidate is given projects, coding challenges, and practice questions for data science that gives you the ability to prepare for your next data science interview. The projects and challenges can be solved by mostly using either SQL or python. In return, all of your information and results are sent to hiring teams to identify and assess top data scientists. On HackerRank, the projects and challenges assess data science skills such as data wrangling, building models, data visualization, and machine learning and test the skills of a candidate.
The skill set assessment of a candidate depends on the profile one is looking for. There are several languages and skill-boosting packages available for practice and testing. One more thing, it is important to know what are the skills that you want to build upon. There is a vast array of skills such as data analysis, experimentation, machine learning methods, data visualization, and many more.
Now, in my opinion, and experience, HackerRank can be very useful for building up your skills from scratch or even if you have very little to no knowledge on the topic. But when it comes to practising and solving more difficult and complex problems, you might find the platform to be insufficient. The challenges in HackerRank are structured ones, where there is a time consideration on top of accuracy. Here, the priority is given to the timely submission of the project over the projects that are more accurate.
So, let's conclude it. HackerRank can be a great tool to test out your coding skills, but it won't be enough to help you land a high-paying job at top MNCs. It is a great tool for software engineers, but to become more useful for data scientists, I think it still needs a lot of improvement.
Now you tell me yourself, in the world of data science, what is more important, accuracy or timely completion?
