How to Get the Best Out of Your Data Science Course

Data Science is the talk of the town and with good reason. It is one skill that has seen a rising demand in the last few years, and no scope of slowing down anytime soon. The reason is obvious – data driven decision making is here to stay and hence so are data scientists.

Now no one said picking up Data Science skills is easy – it involves a considerable time and effort. Many people find it difficult to finish their course work as they tend to lose interest after the initial sheen wears off. But if you want to be a successful Data Scientist, perseverance and planning is what will get you there.

Siddhartha Jana echoes a similar opinion. He is Investment management professional with over 11 years of experience alternative investments and asset management ranging from portfolio analytics, liquidity/collateral management, hedge fund accounting and investment/due diligence risk.  He is an alumnus of Jigsaw Academy, having been enrolled for our Data Science Specialization which he was able to complete in just under 4 months – by no means an easy feat.

In conversation with us, he says,” Even before I enrolled for Jigsaw’s program, I did my due diligence and research about all available programs that fit my learning needs. I in fact prepared my own Excel sheet and performed my own analysis in comparing and contrasting the institutes and the programs. Only after I was convinced did I enrol, and it has worked out for the best for me.”

Being a busy working professional himself, Siddhartha made game plan of how he was going to tackle both the course and his job parallelly. “At the beginning of the program itself I tried to figure out what would be the time needed self-study and I did my estimate of number of hours I would need in a week to complete the course in the time I wanted to. I then split and spread the hours over each week. My next step was to prepare my own schedule and timetable that I intended to strongly adhere to, as I believe having a proper plan helps in being able to take full benefit of the course.  One can expect a 5%-10% variance, but overall having a fair idea of how and what you’ll be studying greatly helps”, he says.

As Siddhartha rightly states, a well-structured plan helps in keeping a track of your progress. It is not enough that one enrols for a course. They need to have clear goals of how to accomplish their tasks and not lag. Of course, Jigsaw Academy does help with constant reminders and follow-ups to all its students, but this also means one needs to be regular with their course work.

Being consistent might not always be possible. For this, Siddhartha advises “When I was not able to achieve my weekly targets, I would cover it up in the following week. I always made sure there wasn’t too many gap days as I wanted to maintain continuity in my learning. One best thing you can do is utilize weekends, as it gives you a good enough time to get through the course content. Another thing that really helps is taking down notes along with the course, it makes it easy when you are doing your assignments or taking the certification exams.”

Data Science is all about practice. Be it assignments, contests, projects or anything else – hands-on application of theory gives a major boost to one’s capabilities and ease of using data science tools and techniques. One could even apply their learnings in their everyday job. Even if they don’t directly work in a data team, they can try to pull in concepts of their learnings into the day-to-day job – essentially align what they’ve learnt to their job role. These are things that will set one apart from the competition.

Siddhartha has been able to transition from a Finance professional to a Data Science practitioner successfully, and quite quickly might I add. He has shown consistency and made extra effort to find time to understand the concepts and apply those to real world problems. While this might seem tough at the beginning, with proper planning, you too can easily complete your course in record time – just ask Siddhartha!

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