3 answers
Yuvika
Student
Chandausi, Uttar Pradesh, India
2
Questions
Asked
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How can i become a data scientist from non technical nd non maths in class 12?
I am in class 12 with biology nd started developing my interest in data science but i dont know the correct career path because i am from non computer background please guide me so that i can become a data scientist
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3 answers
Jessica Kafor
1
Answer
Atlanta, Georgia
Updated
Jessica’s Answer
While math and computer science are valuable fields, they aren’t always essential for a career in data science. Most data scientist roles do not require creating new algorithms from scratch—a skill typically needed only at top-tier tech companies like Meta or Google. Instead, the majority of positions focus on applying existing machine learning models. What matters most is strong programming ability, particularly in languages such as Python, R, and SQL.
In addition, many data science roles emphasize practical data analytics skills and familiarity with platforms like Databricks, Power BI, and Tableau. In my experience, I also needed to learn geospatial mapping with ArcGIS. While a computer science background is helpful for developing software or web applications, data scientists are primarily focused on extracting insights from data, building models, and providing results often through dashboards or notebooks. The fields are related, but the day-to-day work and required skill sets are quite different which is why I preferred to major in data science.
In addition, many data science roles emphasize practical data analytics skills and familiarity with platforms like Databricks, Power BI, and Tableau. In my experience, I also needed to learn geospatial mapping with ArcGIS. While a computer science background is helpful for developing software or web applications, data scientists are primarily focused on extracting insights from data, building models, and providing results often through dashboards or notebooks. The fields are related, but the day-to-day work and required skill sets are quite different which is why I preferred to major in data science.
Jitender Singh
1
Answer
Updated
Jitender’s Answer
Take a moment to think about whether this path suits you. You can try a simple SWOT analysis to look at your strengths, weaknesses, opportunities, and threats, or write about your interests and long-term goals. This reflection can help you see if Data Engineering aligns with your skills and career desires.
If you're just starting, consider pursuing a Bachelor of Computer Applications (BCA) or a Bachelor of Science in Information Technology (BSc IT) from a reputable college or university. This will give you a solid base to grow from.
As you learn, focus on building practical skills. Begin with an easy programming language like Python and get familiar with SQL basics. Once you're comfortable, consider a structured Data Science course to deepen your knowledge and prepare for more advanced work.
Look into the role of Data Engineer through reliable online sources. This will help you confirm your interest and give you a clearer idea of what to expect, boosting your confidence as you progress.
If you're just starting, consider pursuing a Bachelor of Computer Applications (BCA) or a Bachelor of Science in Information Technology (BSc IT) from a reputable college or university. This will give you a solid base to grow from.
As you learn, focus on building practical skills. Begin with an easy programming language like Python and get familiar with SQL basics. Once you're comfortable, consider a structured Data Science course to deepen your knowledge and prepare for more advanced work.
Look into the role of Data Engineer through reliable online sources. This will help you confirm your interest and give you a clearer idea of what to expect, boosting your confidence as you progress.
Chinyere Okafor
Educationist and Counseling Psychologist
1186
Answers
Port Harcourt, Rivers, Nigeria
Updated
Chinyere’s Answer
Hello again Yuvika!
Just like I mentioned before, indeed, a person with a non-technical or biological background can work as a data scientist. Nowadays, a lot of people in the field are much like you: inquisitive but unclear of how to close the gap.
Here’s a simple path you can follow from where you are right now:
- Start small and stay curious: Start with free or inexpensive classes in Excel, Python, and fundamental statistics. For this, sites like Khan Academy, Kaggle, and Coursera are great. If it feels unfamiliar, don't panic; consistency is more important than speed.
- After Class 12, choose a flexible degree: Look for programs such as a Bachelor of Science in Data Science, a Bachelor of Computer Applications, or even a Bachelor of Science in Mathematics or Statistics. Before applying, make sure you have a background in math or computers. Some colleges do admit students without these skills.
- Build real-world exposure early: Explore basic datasets, such as environmental or public health data (you can relate to that because of your experience in biology). It enables you to understand material in a familiar setting.
- Add layers as you go: After you're comfortable, move on to SQL, machine learning, and data visualization programs like Tableau or Power BI. You don't have to learn everything at once. See it as building a skill tree, one branch at a time.
It's completely fine to begin from nothing. The most important thing is that you have the interest to start. Curiosity is rewarded in data science, not just in coding. You'll be surprised at how far you can go if you are consistent.
Best wishes!
Just like I mentioned before, indeed, a person with a non-technical or biological background can work as a data scientist. Nowadays, a lot of people in the field are much like you: inquisitive but unclear of how to close the gap.
Here’s a simple path you can follow from where you are right now:
- Start small and stay curious: Start with free or inexpensive classes in Excel, Python, and fundamental statistics. For this, sites like Khan Academy, Kaggle, and Coursera are great. If it feels unfamiliar, don't panic; consistency is more important than speed.
- After Class 12, choose a flexible degree: Look for programs such as a Bachelor of Science in Data Science, a Bachelor of Computer Applications, or even a Bachelor of Science in Mathematics or Statistics. Before applying, make sure you have a background in math or computers. Some colleges do admit students without these skills.
- Build real-world exposure early: Explore basic datasets, such as environmental or public health data (you can relate to that because of your experience in biology). It enables you to understand material in a familiar setting.
- Add layers as you go: After you're comfortable, move on to SQL, machine learning, and data visualization programs like Tableau or Power BI. You don't have to learn everything at once. See it as building a skill tree, one branch at a time.
It's completely fine to begin from nothing. The most important thing is that you have the interest to start. Curiosity is rewarded in data science, not just in coding. You'll be surprised at how far you can go if you are consistent.
Best wishes!