# Why Most Data Science Learners in India Drop Out

There is now a lot of demand for data science professionals, so many Indian professionals, particularly mid-career, are choosing to study AI and data science. Still, while more people are choosing online education, a lot of students still drop out. Based on data collected by industry and surveys on the platforms themselves, 50% to 80% of online technical courses in India are abandoned. The article goes into the main reasons for these results and shares actions you can take to stay on course.
## Setting Off on the Wrong Path: Starting Motivated by the Wrong Things
Some individuals decide to take [AI data science course](https://www.analytixlabs.co.in/artificial-intelligence-certification-courses-online) out of fear of missing out (FOMO) rather than a genuine interest in their studies. At this time in their careers, professionals might overestimate how quickly data science can help with salary growth. Facing the initial tough task such as learning Python, linear algebra or analyzing real data, pushes motivation down very quickly.

Before signing up for a program, try to get a realistic picture of the role a data scientist plays. Check real job ads, join LinkedIn groups of data scientists and do some free quizzes to see if you enjoy data science. Having a good AI data science course will allow you to assess your starting abilities before starting more advanced work.
## Failing to Manage Time Well and Having Unrealistic Goals
Managing work, family and learning is very challenging. Many Indian professionals don’t realize how much hard work and effort is needed to finish a proper data science program. There are announcements about "only ten hours a week are needed to study" online, but this does not consider that everyone learns differently.
Some learners believe that success should be seen quickly through getting an interview or a promotion after taking a few classes. Should learning not be engaging, students’ faith in the subject starts to weaken and they might end up dropping out.

**Strategy:** See learning as something you are building over the years. Set a timetable that fits your needs and makes targets you can achieve such as completing one project every month or learning one new idea each week. Find a group of people to learn with or team up with friends so you don’t get easily bored.
## Issues with Putting The Theory into Practice
Many learners say that what they are expected to learn is not practical enough. Many people working in the mid-career stage have a difficult time relating the content to what they do at work. These types of courses which exclude practicing or studying real-life projects and case studies, often lose their audience’s interest.

Try to pick programs that offer a lot of practical activities and concentrate on real-world skills. A course designed well for AI data science should include major projects, case studies from each industry and chances for students to practice with industrial tools. Even if you don’t find these, try joining online projects, taking part in machine learning contests or offering freelance work to use what you know.
## Not Having Proper Support or Feedback
It can feel very lonely doing online learning when there are not many mentors or places to get your doubts answered. Learners encountering trouble often cannot reach out for help instantly which ends up making them feel dissatisfied and eventually giving up. Because many people learn tech in India without formal guidance, this can determine if they succeed.
Choose courses that give you regular input such as through online sessions, message boards or appointments with tutors. While your own AI training may not be in a classroom, use online groups or experts on Discord, GitHub or Reddit to support your learning. Working with others helps you remember more.
## Inaccurate Progress Reporting
Much of what learners think about their progress is limited to getting through courses and doing well in quizzes. In data science, being good at evaluation, engineering features or data visualization takes a lot of practice—not only acing a test. Not seeing their own progress makes learners feel empty and less motivated over time.
Solve this by collecting actual projects in your portfolio. Assess your learning process not by what you get, but by the things you are able to solve. If you can build applications that solve a lot of problems, those are proof of your skills more than any badge on your profile.
## Conclusion
If you choose to drop out of a course, this means something should be adjusted in your learning environment or your learning strategy. Indian professionals who decide to take an AI data science course stand to gain new job opportunities and ensure their skills stay relevant in the future. It takes more effort than just signing up to do well. To be successful, you need a clear goal, stick to a schedule, practice skills in the real world and have useful helpers. If you use these tips, you’re much more able to finish your learning project and see what it pays off.