With Data Science being an attractive field, students look for internship opportunities. An internship for college students provides them a big advantage to bolster their CV strong and land a job in data science. College students or newcomers in the field often lack guidance and need tips to bag their first data science internship.
We list some tips for the beginners in data science to grab their first ever data science internship.
1.Know the company:
Before applying, make sure that you study all about what the company does and what its goals are. Research a little to know about the company, its products and services and make sure that the job they are offering is the job that interests you. Applying for an internship without knowing the organisation well could be very negative when asked about the company during the interview.
2.Know your ML fundamentals and don’t stuff up resume with keywords:
At the end of the day, it is a machine learning internship and recruiters are looking for some basic level of expertise in machine learning. You are of course not expected to be an expert with all the deep technical understanding. But before you go for the interview, make sure you have mastered at least one of the basic algorithms and are at least to a good level awareness of the introductory machine learning algorithms like linear regression, K means, SVMs, random forest.
3.Active GitHub account:
A GitHub account definitely enhances the chances of landing an internship in data science or machine learning. The account must be active and have useful content. It is a clear proof to the people in the interview panel that you have knowledge and leaves behind a great deal of a good impression.
4.Data science blogs:
Probably not as impactful as a GitHub account or a project done by you, but an own technical blog on data science depicting your knowledge will add to your positives giving the organisation an impression that you are truly interested in data science and. Although this is not a necessity, it is sure a plus.
5.Do ML and DS projects:
Machine learning courses that you had taken up online and academics that you are involved in are the things that all the candidates applying for the interview will have. But what not everyone has is a practical machine learning project. Recruiters are looking for not just good coding and machine learning skills, but also problem-solving skills. Projects show that you have applied the theoretical knowledge that you have learnt into practical applications, which is in turn a proof of you being able to handle real-time organisation issues and goals.
If you cannot come up with a project yourself, do it under a professor under your college or participate in online hackathons are online competitions. It is also important to showcase how you function in a team. Working in an organisation requires collaborating with different people and it is important to show that you can work in a group and offer value to the team.
7.Don’t overcrowd your CV:
Don’t fill your resume with all the popular programming languages that you know and don’t claim that you know all those popular machine learning algorithms. Specially since you are joining an internship as a fresher, there is no way that you could’ve mastered every programming language that there is. Proactive and eager-to-learn attitude is what will get applicants noticed, even if one might not have an extensive portfolio or industry experience. Familiarity with every aspect, or an extensive experience in the industry is not necessary, but it is important for recruiters know that you are up for taking up a research and work towards the goal.
Ms Veronica Puah, Deputy Director of Talent Networking at SGInnovate said in a panel discussion, “It’s OK if you don’t know or are not too familiar with certain things. But at the end of the day, we want someone who takes the initiative to do their own research to close the gap.” Make sure that the project that you are presenting, if you are, has everything that you know so that you can answer any level of technical questions restricted to that area and slay the interview. Be ready to answer questions related to anything that is mentioned in your CV because interviewers pay special attention to that. Revise all the theory related to the projects and courses that you had done, before the interview day.
