Last decade has seen a plethora of data science courses and institutes flooding the market. Largely triggered by the demand for quality data science talent in the industry, these courses tend to have curated various formats for this delivery. Considering the scenario, both full-time and part-time data science courses have been designed for industry training; however, going for one or the other has its own pros and cons. After pondering on the issue, full-time has shown substantial merit against part-time courses. Moreover, it’s advisable to go full-time if you are entering the field and don’t have time constraints.
Full-time training courses provide a wide range of first-hand experience – a prerequisite to acquiring industry-relevant skills. This eventually makes you a better fit for the market. Constant exposure to real-life problems lays a strong foundation, thereby making you hard enough not to be cracked under pressure. When it comes to part-time, the short duration corresponds to lesser exposure – this is where the difference resides.
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“Full-time data science courses enable students to perform at the peak of their capacity since they can work with fellow like-minded students who are competing for the same jobs when they graduate from the course. This competition is healthy as it motivates many to do their best. For others, this competition may be less welcome. However, it still enables them to learn from others while collaborating in class homework or hackathons,” said Ram Seshadri, Google ML Program Manager. Before this, he was a data scientist at Morgan Stanley.
Full-time courses include theoretical principles, tests, and practical modules applied to real-world situations. The instructors are mostly senior data scientists who have worked in the industry and are best suited to provide job-ready skills in a practical classroom setting. People choose to develop their skills by enrolling in short-time courses, viewing free online videos or reading blogs and website tips. However, these free resources do not provide an organised learning strategy even if the knowledge is from reliable sources. Even if you already have some data science experience, having this structure is vital.
Part-time data science courses tend to be ineffective for learners when it comes to mastering techniques, the reason being, data science concepts are fast-tracked in a stipulated time period. As a result, learners fail to retain or recall things when required. This is where full-time courses come to the rescue. Students have the freedom to devote a good portion of their time learning new tools, understanding business problems, teamwork exposure, and most importantly, a critical thinking mindset.
In addition, the guidance and mentorship from regular teaching faculty help you embark on a suitable career path. Adding on, Ram said, “Students can dedicate themselves to mastering data science without worries about other commitments such as a job.”
Interaction with students & faculty
“Students can spend quality time with instructors to ask questions and clarify doubts every day while the course is being taught. This is very important since some statistical concepts are difficult to master unless one can ask questions in real-time and clarify doubts,” said Ram. It’s not easy to get the hang of data science. Regular sessions, follow-ups, one on one interaction helps solve real-life problems in a much efficient manner and get back in case of doubt. This allows you to keep learning without interrupting your flow, which isn’t often the case in part-time data science courses.
Before getting their hands on fresh advancements, one must first catch up with the data science sector. It is achievable only if aspirants do not jump from one technique to the next in a short stretch of time. To stay current, learners must first understand what has transpired in the landscape over the last few years. This will eventually help aspirants to get hold of new learning advancements in the data science space. Simply put, short-term courses can make you a jack of all and master of none.
Employers look for Full-time certification
Full-time data science programme certifications bring more value than part-time course certificates. The longer duration of the course is designed to implement skills taught in the classroom to solve industry problems through internships and institute-industry collaborations. In addition, one will have job placement assistance, access to industry partners and contacts, certification and membership after graduation. Sandeep Goel, SVP, Strategy and Operations at Moglix, said to Analytics India Magazine that courses from colleges or other platforms matter when hiring.
Businesses rely on data to better their business outcomes. Data science is not only about technology. From the manufacturing sector to aviation, pharma to retail, every sector today captures user data for analysis and making the right business decisions. Data science, we must remember, is an umbrella term with a broad domain and not just a specific skill-set.