Hello guys, I know it’s been a really long time since I have posted on the blog. I was preparing for various exams and tests for pursuing a masters degree in USA. I am really sorry and in order to compensate for that, I shall post at least once or more every week from now on.
I have gone through the curriculum and resources offered by various universities – public and private, in order to shortlist a few to apply for masters degree finally. During this process, I have really gained a “lot” of information about many wonderful universities. Also, I had the chance of exploring some great business analytics programs offered in the United States too. I know, that there are millions of students aspiring to be data scientists, data analysts, business analysts etc some day. The one thing, most of them would think of, in order to achieve their aspirations, is to pursue a masters degree in that respective field. It is not a bad idea if the person really knows what he / she is doing, but if at all the person is doing this just by ‘following the herd’, then it could cause problems in the future. One example would be financial conditions could worsen because masters degree in data science is pretty expensive and quite a few college offer scholarships to international students, even in public colleges, So if one wants to choose this path , a brief thought process should surely be employed before taking the path.
Now, I am going to explain what I genuinely feel about the masters degree in data science. In my view, it totally depends upon the candidate. If the person is someone who really cannot study online courses and requires personal attention, and is willing to pay extra money for that would be the one who should get on to a masters program at a reputed university. Coming to Bootcamps and MOOC’s, bootcamps are ideal for people who are intrested in experential learning rather than theoretical learning, also these people are willing to spend extra money than they have spent on MOOC’s. Finally, MOOC’s are for people who want to learn things at the cheapest cost possible, also these kind of people can easily adapt to online teaching and make the best use of it. Lets dig deeper into each of these learning methods now.
” Masters is really theoretical ” , is the thing of past , especially in data science. Because, the universities offering masters in data science are not many, because of which there is a lot of demand for those universities. Now, why I said that – masters in data science is not fully theoretical, is because, universities conduct internships and practicums like capstone projects in which the students have to work on real time projects, in collaboration with industry partners. No wonder, why data science programs are expensive!! . Due to which the students gain actual experience which cannot be gained by a MOOC or a Bootcamp. Because, students have to go 2 days a week to work at the company and the other days they can study for the program, so the students are “practically learning” . Again, such a cool curriculum is offered by only the above par universities in USA. Another advantage could be time constraint , in bootcamps, the students are expected to learn and implement things with in 1 – 3 months, which could prove to be disastrous sometimes. As students who are not really quick learners, could have a hard time understanding the concepts and methodologies. But masters is completely flexible, students usually have more than an year or so to complete the program. Also, there is a “part time” option offered by majority of the universities. The only disadvantage is the tuition fee, which is really expensive for a few top universities. But, the universities offer campus placements , alumni meetups, workshops and orientations by industry experts , job assistance programs etc. Outcomes of a master degree would be a Diploma. Ideal for people that enjoy immersing themselves in campus life and want to take time to let the new material absorb while learning in a structured environment with the full credentials of a University degree
Bootcamps, sure do have a lot of extra ordinary features, like in a bootcamp, the students are actually taught by data scientists and data science experts. This is surely an advantage as the students get to learn from the real data scientists everyday. Bootcamps are also short compared to masters, spanning from 1 – 3 months roughly. Some of the great bootcamps offered are by “Metis” , “NYC data science academy” . The students are eventually offered with a portfolio of projects they have done while in the training period. Tution fee is comparatively less compared to masters at 15K -20K USD on average. It is really expensive for a short span, but you know , it has quite many benefits too. Finally students are allowed to participate in hiring days organized by the bootcamp , and also people called talent placement manager guide the students in finding a job.The ideal person for bootcamps would be people who are from backgrounds related to data science, people who are quick learners , definitely not working professionals, because of the pace of the program. Also, people that want to switch or accelerate careers ASAP with the confidence that the switch will result in a job they will like while learning in a structured environment and this is not a good option for absolute beginners.
Massive online open courses , as the name suggests, are open to everyone, but essentially one has to pay for gaining a certificate at the end of the course. Many universities started offering MOOC’s related to data science, for instance, John Hopkins University offers a 10 course bundle and Michigan University offers courses related to Python for data science etc. Coursera is one platform where the courses are offered by top universities. Also, some universities like MIT , offer on their own website. The greatest advantage is, there is no time limit in learning the course contents. It is the most flexible out of the three and the cheapest of the lot at 500 USD maximum. But the networking part is very weak as there is no one one can actually connect with. Also there is no practical learning employed in most of the MOOC’s, which is quite a drawback. Students have to search for jobs without any assistance which could be a really tough task sometimes. Ideal for people that thrive with ambiguity and self-guided environments and are motivated enough to design a curriculum around their own strengths and weaknesses.
The upcoming chapters would be about the universities offering MS in Data science and why I feel they are an extraordinary place for budding data scientists to grow.