Thanks to mass digitisation, the demand for emerging disciplines like data science, machine learning, and AI is on the rise. Educational institutions and companies across the globe are offering attractive courses and programmes to meet this growing demand.
India has about 450 colleges that offer AI, robotics, and machine learning courses. Of these, 90% are private, 8% are public, and 2% are public-private partnerships. Today, a lot of students and business professionals are interested in these cutting-edge technologies because of the endless opportunity and career growth the domain offers.
However, analytics education is different. Work experience doesn’t matter much when enrolling for the data science and analytics programmes. They can be fresh college grads or a senior process engineer with 23 years of work experience in manufacturing. This is why it is essential to match the students with the right course.
The process/system will affect them in two ways:
- The skills they will acquire
- The job they get
The magic quadrant of analytics courses
When choosing analytics and data science courses, several factors come into play: cost, experience (complexity), duration (the time it takes to complete the course) and real-time projects/applications.
Let’s discuss experience.
For example, a part-time ISB or IIM B course will be a good fit for a java developer with ten years of work experience since more often than not, he/she can’t afford to leave the job. Meanwhile, BTech or BE graduates can enrol for a Praxis full-time course.
The critical factors to take into consideration while choosing the course include duration, targeted audience (students/professionals), cost, experience and real-time projects and job opportunities.
What to expect?
Though data science has been dubbed the sexiest job of the 21st century with outsised salaries, it should not be the only reason behind choosing the domain. According to AIM Research, the salary of data analytics professionals in 2021 was 44% higher than a software engineer, 36% higher than the salary of an IT developer, and 50% higher than the salaries of Java developers.
Students should not take up data science for the glamour and monetary benefits alone but to enhance their skills and to achieve excellence in the field. Here are the essential skills required to build a career in data science.
Also, perseverance is key–I have seen people disappointed after a couple of months/years when they do not get the desired results.
Most people get into data science and analytics for the adventure. But, in reality, it is quite different. Sometimes, it can also get boring and mundane (data cleaning, sorting, etc.). The candidates have to spend their time doing technical work and dealing with less exciting things like reporting, documenting, writing, delivering presentations, etc. In a few cases, you have to repeatedly explain the basics of your models or techniques, project management, data processing techniques, etc., to stakeholders.
Next is the timeline. Candidates from a research background are used to a flexible deadline to solve problems. However, most companies have time constraints, and such candidates will struggle to fit in.
A final thought
As businesses are becoming more and more tech and data-savvy, it is high time you upgraded your skills and selected a course that adds value to your career trajectory. So, when choosing the right analytics and data science programme – be it full time, part-time, or post-doc, think about the result; what value does it add to your career growth? Blindly following the trends in data science and analytics will lead to disappointment.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.