If you learn Data Science and don’t get a job, the reason is not that there are no openings or that it is tough. The real reason is that while learning Data Science, you make some mistakes. In this blog, we will discuss in detail what mistakes you should not make while learning Data Science.
Nowadays, everywhere you look in the market, many students are learning courses like Data Science with AI, Generative AI, Full Stack Development, Cloud Computing, Cyber Security, and so on.
Among these, when learning Data Science with AI, many B. Tech students, as well as those already working in IT, consider becoming a Data Scientist as one of the craziest options in the market now. But many people are not learning Data Science in the right way. So, when you want to become a Data Scientist and achieve your dream, don’t make these 10 mistakes while doing the course.
1. Ignoring Fundamentals of Python
Without a strong basis in Python, don’t jump into advanced topics. Many students directly talk about TensorFlow and Deep Learning, but when asked to write a simple reverse number program in Python, they can’t. Yet, they discuss advanced topics like TensorFlow.
The first step when you want to become a Data Scientist is to go step by step. Only if your fundamentals are strong, move on to advanced topics. Don’t ignore the fundamentals and directly try to learn advanced topics.
2. Depending Only on YouTube
Many students say they know Data Analyst, Data Science, and all that. But when we look at how they learned, many just randomly watched a few YouTube videos.
Using YouTube as a learning resource is good, but if you want to become a Data Scientist and get the best package in a good company, it’s better to learn in an organized way.
Example
When we’re not healthy, we should go to a doctor, not just watch YouTube and take medicine.
Depending only on YouTube resources will cause problems like:
- Lack of real datasets
- Not knowing what datasets to work on
- Missing real-time data
Selecting a mentor or an expert trainer in Data Science is very important. And even when selecting that trainer:
- If you’re a topper, you need one kind of trainer.
- If you’re a mid-level student, you need another.
A trainer must explain everything clearly, from fundamentals to advanced level, in real time. Yes, a few people can learn on their own and get jobs, but 90% of students end up learning only half the knowledge, mainly because they depend only on YouTube.
3. Ignoring Mathematics and Statistics
When you want to become a Data Scientist, knowledge of Mathematics and Statistics plays a key role. Without knowing the fundamentals of Mathematics and Statistics, people try to learn algorithms and advanced topics.
If you know the fundamentals of Mathematics, Statistics, and Python programming well, you’ve already cleared about 50% of the interview.
4. Weak SQL and Database Skills
SQL and Database knowledge are crucial.
Example
When HCL went to a university for interviews, many students there had done multiple certifications in Data Science, but they couldn’t answer basic SQL queries or database concepts. The interview panel gave negative feedback, like:
“These students don’t even know the basics. They can’t write SQL queries. They lack database knowledge.”
If you want to become a Data Scientist, you must know the fundamentals of SQL queries and database concepts.
5. No Real-Time Projects or Kaggle Work
For Data Science students, there’s a platform called Kaggle. Kaggle is the world’s largest online platform for Data Science and Machine Learning competitions, owned by Google. Students, professionals, and researchers can practice skills, solve real-world datasets, and showcase their projects.
Example
On Kaggle, you’ll find:
- Uber Data Analytics Dashboard
- Multilingual Mobile App Review datasets
- Global EV Charging Stations
Simply put, Kaggle is like a cricket ground for a Data Scientist. Tomorrow, when you go for an interview, if you showcase your Kaggle portfolio, your job opportunities will increase a lot. Don’t copy-paste code using ChatGPT just to pass internally. Practice Data Science or AI concepts on your own for better results.
6. No Internship Experience
Do an internship in Data Science. Even as a fresher, even with low or no salary, try to work as an intern in a startup. Learning work is important, not salary. An internship is the best option. Make sure it’s a genuine Data Science internship, not a fake one.
7. Ignoring Latest Trends
When you want to become a Data Scientist, also learn the latest industry trends.
Along with Data Science, learn:
- Generative AI
- LLMs
- MLOps
- LangChains
Do a mini project in Generative AI too.
8. Learning Alone Without a Community
Don’t learn alone. Learn within a community and with a mentor while doing projects.
Example
If you’re learning from a Data Scientist working in Accenture, you’ll know what projects they’re doing, what new trends are coming, what challenges they face, and what exactly a Data Scientist does in the office. Learn only from a real Data Scientist working in the industry.
9. Unrealistic Package Expectations
Data Science with AI, without in-depth knowledge of Generative AI, LLMs, or MLOps, don’t expect high packages.
Step by step:
- Even if you don’t get 15 to 20 LPA immediately, you might get 3 to 5 LPA.
- Within 3 years, you’ll be in a good position.
Don’t focus only on packages; focus on skills first.
10. Lack of Long-Term Vision
Be willing to join even a small company in the beginning. Skills bring high packages, not luck or just paying money to a big university. For now, keep packages aside; just getting an opportunity in any company is important.
Summary of Mistakes in Data Science: 10 Things You Should Avoid While Learning
| Mistake | Why It Hurts | What To Do Instead |
|---|---|---|
| Skipping Python basics | Fail in simple coding | Build fundamentals first |
| Depending only on YouTube | Incomplete knowledge | Learn in a structured way |
| Ignoring Math/Stats | Fail interviews | Master fundamentals |
| Weak SQL/database | Rejected by companies | Practice queries |
| No Kaggle projects | No portfolio | Showcase real projects |
| No internship | No industry exposure | Do genuine internships |
| Ignoring trends | Outdated skills | Learn LLMs, MLOps, etc. |
| Learning alone | Limited growth | Join community/mentor |
| High package obsession | Unrealistic goals | Focus on skills first |
| No long-term vision | Career stagnation | Take step-by-step growth |
Conclusion
In Data Science, even freshers can get jobs, career gap candidates can switch, IT professionals can upskill, and even B. Tech students can do internships. This is a futuristic platform. Already in the US, many companies are hiring in this domain. So, Data Science is definitely good for your future. With AI concepts and by continuously adding advanced concepts, it’s very beneficial. So those who complain they’re not getting jobs or that there are no openings are all false. Data Science is definitely a futuristic subject, but to sustain here, avoid these 10 mistakes. For more tips, guides, and resources, explore BuzzIndie and stay ahead in your journey.