The five-point litmus test to picking the right data science program and institute.
The education space is crammed with universities and upskilling institutes offering a plethora of data science programs that promise accredited certification and a higher pay upon completion. Navigating this puzzling landscape can be exhausting and confusing. This blog, being a saviour, will guide you through evaluating institutes before entirely leaning on them. If you miss out any of the following factors before applying at the academy, you are possibly wasting your effort, time, and hard-earned money. Here are the five factors that every applicant must take into consideration so that he/she gains entry into an appropriate institute.
There are some key questions that you need to ask when you are evaluating program curriculum.
- How is this curriculum going to help me out with achieving my goals?
(This helps you set your program expectations and find out whether the program is compatible with your goals.)
- What was the underpinning principle/s upon which the curriculum was designed?
(This gives you insights into whether the curriculum is relevant to industry and whether it prepares you for an actual job. This is paramount for a practice-based discipline like data science.)
- Does the curriculum go into the depth of industry practices that are critical while looking for a job?
(This will determine your value in the job market. For data science hiring, practical skills matter a lot more than an accredited certification.)
2. Instructor Profile
You must ask about the screening procedures and eligibility requirements that are employed while hiring instructors. A data science instructor must have domain expertise and should be an active practitioner. Instructors who are practitioners not only ensure thorough learning, but they also advise you choosing the right career path.he ideal thing is to ask the institute/university for their benchmarks for hiring data science instructors.
3. Exposure to the Industry
Data science is an application-based discipline that thrives in the industry than in academia. Until you do not get your hands on real problem statements and datasets, you cannot come to grips with real tasks in your career. In addition, networking with knowledgeable people is vital while you are working on the course. Therefore, it becomes extremely important to carry out comprehensive research on the institute to examine whether the institute is providing you real data sets to work on.
4. Placement Assistance
This is pivotal in landing a job. Please note that placement assistance is different from a job guarantee. The former is empowering the learner to develop requisite know-how to find a job; the latter is spoon-feeding that produces an intellectual handicap. Placement assistance involves knowledge-sharing on how to build a LinkedIn profile, craft a resume, look for hiring challenges, and build professional network. Make sure that the program you sign up for provides the full range of placement assistance.
5. Validated Learning
Learning must be accompanied by validation and assistance. It is important that the university or institute you enroll with provides enough resources for resolving problems. Also, there needs to be dedicated instructor-learner review sessions where students can ask questions and resolve doubts. Finally, the learning platform should have communication outlets or forums where students could raise their queries.
So to sum it up, a data science program must have an industry relevant curriculum, real data sets and problem statements, instructors who are practitioners, placement assistance, and student-instructor touch-points. These five criteria heavily influence the learning outcomes and job-worthiness of the learner. Do GreyAtom’s datas science programs pass the litmus test? Check out our programs and find out for yourself.