Finance and Investment Banking Training Institute

‘First impression is the last impression’, this age-old adage, holds true in today’s world of appearances. Nowadays, appearances are all that count in the professional sphere, just having the desired skill set for a particular job is never enough. Professionals are required to have proper office etiquette and there is a lot of emphasis laid on possessing the perfect soft skills. Especially when it comes to recruiting, a near perfect C. V is something that is not only expected, but also given more importance than the technical knowledge. The Curriculum Vitae or its more popular short form, C.V is said to be the most indispensable tool one could have when applying for a particular job. Resumes are the virtual first impression of a candidate, so if one’s C.V is full of grammatical errors, spelling mistakes or poorly formatted; there are high chances of getting rejected in spite of possessing the required technical skills. Many a times recruiters have admitted to having selected candidates, with C.Vs, formatted with excellence, crisp yet fitting the job description perfectly. This holds extremely true when it comes to hiring freshers, most companies take interviews fully expecting that these newly graduated candidates would be like blank canvases.

The field of Data Science is a result of the massive digitalization, which has taken our world by storm in the recent years. In simple terms, this field majorly deals with predictive analysis of the virtual data records, present all around us. There are three very important things that one must keep in mind, while drawing up a C.V for roles in Data Science.

Perfecting The Basics

One of the most important things to keep in mind while drafting a C.V is the language. Never make the mistake of using SMS jargon or any kind of short forms, as well as ensure that it does not contain very flowery, highbrow language. Typos and grammatical errors are to be strictly avoided, as they can result in to direct rejection. The key to a good C.V is to keep it at a length where you don’t come across as someone who hasn’t done enough, or someone cannot help but drone on. Keep it simple, crisp and clear, while at the same time tone down on the excessive use of complex terminologies. Always take second opinions, or third opinions as to how your C.V looks like to professionals from the same field before sending it across. It is essential to mention the relevance of your skill set and how your experience can contribute to this position.

A Role-Centric C.V Is A Must  

Once you have a basic framework ready, try and hash out the irrelevant things and add more technical details to your C.V. Today, almost everyone has done some sort of C.V enhancing certification course, or has completed projects during degree. It is very important to highlight the more important of the projects and the skills acquired through them, which can prove very useful for the job. The technical side of data science roles mostly requires for you actually work with large data sets, slice and dice them, analyse them and then produce this analysis to your clients. When applying for these roles, ensure that your C.V is a portfolio of all the important certifications you have, highlighting your technical know-how and so on. Whereas, the non-technical jobs involve client facing roles, where you wouldn’t be expected to know one particular language in details. When applying for such roles, let your C.V reflect your understanding of data science as a field from the business development perspective.

Try To Be Different

A plain, typed out C.V no makes an impression on the recruiter. There are various way with which you can ensure that your C.V looks a class apart. There are many institutes today which offer training in perfecting your resume and groom you for interviews as well. While you can go ahead with the ongoing trend of making infographics, you can also use your data analytics tools to use by making a C.V into a dashboard, similar to Google analytics and so on.

One more very innovative way to get you the job would be, trying to develop a script of one of the programming languages, for your own C.V. Perfect language, some great experiences will set you off on a lucrative career path in the field of Data Science