Looking to switch to Data Science. Here you go!
In today’s world, AI is the new revolution, and every company, be it an enterprise or startup, wants to get its hands dirty with the application of AI. Due to this, definitely, there is a rise in the demand for AI professionals for a variety of roles. Personally, folks reaching out to me with questions like “How to switch jobs to DS?”, “Which course/ high degree to pursue?”, “How to learn python for DS work?”, etc, have increased.
To me, it seems like, most people are just trying to ride the wave rather than exploring if it’s really the field of their interest wherein they can learn, grow and create impact. Hence, I thought of helping my readers declutter and truly explore their interest in the field and further identify what’s important for them.
I have worn multiple hats in the field of Data Science over the past 7 years, beginning my journey in 2015 when everything was just booming up. I have observed how skills, tooling, roles, talent, and user perception have changed and evolved over time in this field. Hopefully, this blog adds real value to your time :)
Think about — Why do I aspire to pursue Data Science?
I am mentioning certain reasons below to which you can map yours -
- I don’t know! I just heard about it from someone. Seems like everyone is getting a good raise by becoming one!
- I have 2–3 years of experience but I am fed up with my current job. So, looking for switching careers.
- Many of my friends are pursuing a degree in data science. I have FOMO!
- One of my friends is an experienced Data Scientist and she is doing cutting-edge research work.
- I read about really cool products like Alexa, Netflix recommendation, Face unlock, etc. which excites me!
- I have read a few blogs online and found them intriguing.
- I have done some small/quick projects in ML and I feel like doing more.
- I was part of an ML project in my last company and found it interesting.
- I have a prior experience in engineering and mathematics and I know python well which is required for building AI solutions.
- I have contributed/ participated in the Kaggle competitions during my college days but now I want to pursue it seriously.
The above mentioned are some of the reasons I often hear from people who reach out to me but by now you would have gotten the point!
If your reasons are around 1 to 4 — STOP!!! Explore and talk to more people.
There are high chances that you don’t know about the field and the intricacies of the work yet. Before investing your time and the money in joining a degree/ or online/ part-time courses, invest in learning more about the work professionals do in the Industry. Trust me, a lot of people actually realize that it’s not something they should be doing after they have paid the hefty fees :(
Why? — Simply put, it's not just writing 5 lines of code to train models or transform pandas data frame but there is a vast umbrella of things around Data Science that one has to learn — Coding, Mathematics, Research Mindset, Domain Expertise, Deployments, Business Impact, etc. You might not be interested in a lot of these things. So, explore more!
If your reasons are around 5 to 8 — PAUSE!!! Yes, you are fascinated but learn more.
I understand that you might know a bit about how things work, mostly from an outcome point of view. All of this might look exciting, especially when seen in conjunction with the “brand word Artificial Intelligence” but there is no harm to being 100% sure about your interests, especially before switching your stable career to some other field.
If your reasons are around 9 to 10 — Great!!! Go for it.
If you have contributed and participated in the development work on Kaggle or as an early stage Data Scientist, you would already know “how things work” and what’s in it for you. So make an informed decision about how to grow further.
Finally, there is no harm in exploring and learning about ML/DS and there are tons of open source contributions and online courses freely available for you to understand the intricacies before you become a product of hundreds of paid certifications and courses available today. Instead, learn and implement a few things and go for an internship and see how things actually work and then get a suitable certification or degree only if you are 100% sure.
Once you are sure of your interest — What options do you have?
- Go for a free online course and get a project done on your own — You should implement this project “No matter what” and literally, figure out every issue you face as it can be a really solid learning experience. Finally, get an internship and prove your skills to get a full-time job. That’s how I did it and after working for 4 years, I went for a formal master’s degree in Data Science. But I understand that the time has changed now :)
- Go for a professional degree or certification with a year-long course — This might be a good option if you are willing to work hard and make a place for yourself. Look for industry exposure with a bit of core understanding. Start with analytics work if that interests you, as it requires less core understanding and more technical skills.
- Go for a part-time Masters/ MS — This needs good time commitment along with work but can be super beneficial for folks who have spent a few years in this field or are highly interested in switching careers. This would provide in-depth understanding and help them excel in their career further.
- Go for a full-time Master’s/ MS Degree/ Ph.D.— Great option, especially, if you want to be part of core research and academia eventually. But you will have to consider other external factors before taking such a decision.
There are other factors also into play like finances, personal issues, aspiration for studying in a foreign university, etc which will affect such decisions but those who really want to create an impact in the space will find a way out. At the end of the day, it all depends on your “SKILLS” and how you acquire them.
Thanks for reading. Hopefully, I was able to add value to your time :)
Do reach out to me if you have any further queries or concerns or if you would want to share your experiences and learning.