Not having any technical background shouldn’t stop you from chasing your dream. Data Science is not only about programming but more about solving a problem, hence curiosity to solve a problem is what is required to become a data scientist. You can definitely get into data science from any background.
Let’s get started and understand the steps you should take if you are willing to start your career in data science but are worried about your non-technical background.
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ToggleBeginning from the Scratch
In today’s tech world, your background hardly matters if you have the right skill sets. Hence, instead of worrying about what degree you have. You should rather be more curious about what you want to become and starts working towards it. Now the question comes, How?
You may be a fresher right now seeking to be in this amazing field of data science or you may be already working in some other domain. Whatever the case, just START from scratch and most important start small.
Learn the basics of each topic to get familiar with the field. Start with Python (only learn topics relevant to data science), basics of statistics and basic machine learning algorithms. Now that you have some understanding of this field. I suggest you again start from the beginning and this time include more advanced topics as well.
Relevant Read: How to Become a Data Scientist in 2022 – Complete Roadmap
Data Science Courses
This is a mostly asked question, whether you should learn data science on your own or should you take a course? The answer is don’t lie to yourself. If you feel you won’t be able to learn it on your own, go ahead with a course. Now the next question is which course should you consider?
A course doesn’t have to be paid. There is a lot of good free content available on youtube. StatQuest with Josh Starmer and Krish Naik are good to start with. Even Simplilearn, Edureka, and GreatLearning also have related videos on youtube. You can also join the free master class from the Scaler Academy for a specific topic.
Relevant Read: How Long Does it Take To Learn Data Science From Scratch?
Identify Your Interest
Instead of chasing the data science profile, it would be better if you identify your actual interests and the kind of projects you want to be involved in. The analytics field has a variety of job profiles which are similar to data scientists.
The different job profiles have different responsibilities and therefore the skills required for each of them are also different. Below are different job roles you can choose from:
- Data Analyst
- Business Analyst
- Machine Learning Engineer
- NLP Engineer
- Computer Vision Engineer
- Data Engineer
- MLOps Engineer
Relevant Read: How to Become an MLOps Engineer in 2022? and Complete Roadmap to Become a Data Analyst in 2022- Career Guide
Upskill Yourself
The real fact is that you cannot learn everything at ones. But after learning basic skills and finding your area of interest, you can focus more on the skills required for that particular role.
If you are not able to find a job of your interest. Try to enter into the analytics domain for a simpler role and upskill yourself for the one you are interested in.
Do Real Life Projects
You should work on a number of projects while preparing for data science. But doing a real-life project can change the game. It gives you more confidence in your skills and makes the process of getting your first data science job smooth. But how to do a real-life project?
You can try to get an internship if you are a fresher or you can try to implement machine learning in your current project.
Relevant Read:Â How to Get a Data Science Internship With No Experience
Join Data Science Communities
Joining a community in a domain helps you stay updated with the field and find any opportunity for you. But which communities to join? I would suggest you follow people in the analytics domain on Linkedin.
If you were a part of any related course, you can stay active in their community.
Frequently Asked Questions
The below questions are specifically for those who are from a non-technical background.
- Can I Learn Data Science on My Own?
You should definitely give it a try to learn data science on your own through free blog articles and youtube videos. But be honest with yourself. If you are not able to prepare consistently or facing a lot of challenges, go for a course. - Will I Get an Interview Call for Data Science?
If you have relevant skills and projects in your resume, you will definitely be considered for the profile whatever your background may be. - Should I Invest in Costly Data Science Courses?
You can invest in these courses if you are completely from an off background and have no knowledge of coding and statistics. But if you have some prior understanding of the topics, then you should first try to learn by yourself and if that is not working go for a course. Getting a course is also a good choice if you are actively busy in your current job. - What is the Advantage of taking a Data Science Course?
There are a few advantages of taking a course:
~ All the topics are taught in a relevant order.
~ When you take a course, there are high chances that you will be consistent in your preparation.
~ Some of the courses also provide post-course placement opportunities.
~ You get into a good data science community.
Final Thoughts
My final words would be data science is very hyped about the fact that it is difficult to learn. But one of the most interesting things while learning it is that you will always have real-life things to relate with. For example, the statistics you will learn for data science is the same as you have done in your school days. If you are interested in the stock market, the same concepts are applied there.
So instead of believing it as a hard subject, you should rather consider it as the most interesting field you can be in. This makes it easy to get familiar with the concepts.
An idea is what helps you start but consistency is what keeps you going. You have to be very patient while learning data science. Just follow the process and you will definitely reach there.
Happy Learning!