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Everyone and their dogs are trying to enter the tech industry, whether by learning to program, entering product management, or some other direction. I am pretty new to the tech industry, with only 5 years of experience, but as I speak to more individuals, some are worried about getting their foot in the door due to the lack of high-level education.
In this article, I will discuss my journey and explain what to do and what to avoid.
How I Became a Data Scientist Without a CS Degree
Five years ago, I was in a pickle. I had recently dropped out of my pharmacy degree to pursue a career as a tech professional. I had the choice of returning to university to study computer science or finding another route. Being British, university was expensive, and as I had already done two years of pharmacy, I would have only had two extra years of government support. The remaining two years, I would have had to pay for myself. This did not look attractive, considering it was £9000 a year.
I started searching online for courses that were a fraction of the price and came across a data scientist bootcamp, which looked great: 9 months of full-time learning part time, which worked perfectly with my full time role. I spent my day working and came back to study until 11 pm.
Nine months of learning was way more attractive than four years of knowledge and £36,000 in debt. The best part is that I only had to pay back a percentage of my salary once I got a job.
It seemed like a dream… until it wasn’t. And here’s why.
Bootcamps Are Not For Everyone
The whole purpose of bootcamps is that you have little time to learn everything you can. This can be a breeze for some people, for example those who have the time to do the extra hours on the side or those who pick things up quickly.
However, that was not the case for me. I was working full time and spending my evenings trying to learn about Python and machine learning models. It did not work. I passed, but I could not confidently say I was a proficient data scientist.
Here is why:
- Learning a programming language takes time and patience. It requires a lot of practice and is a process you cannot rush.
- Bootcamps do not provide all the knowledge you need to be a successful data scientist. Is it possible to cram in 4 years of university knowledge in 9 months? Probably not. But to be proficient, you want to ensure you know everything and understand it well. For example, in my bootcamp, we rarely touched on the importance of maths and statistics, which is the bread and butter of data science.
- Guidance and support are essential when you are learning something new; therefore, you want to make sure you don’t feel like you are rushing through the learning material, and you can ask for help when you need it before moving on to the next step.
Data Science Learning Recommendations
Now you have an understanding of the trials and tribulations that I went through on my data science journey, here are my top tips:
1. Set Realistic Goals
The first thing you should do is set realistic goals. These will be unique to you based on your personal commitments, free time, etc. You want to start your data science journey with realistic expectations that align with you and only you. Do not compare yourself to others, and do what works for you.
For example, you could be a full time mother and only be able to give 10 hours a week to learning. That is completely fine. Do not compare yourself with a 19-year-old whose only goal is to learn data science.
2. Put Together a Data Science Plan
Once you have set your goals, you should create a data science plan. This is your data science journey and will consist of all the elements of data science that you need to learn. The main points you want to focus on are a programming language (ideally Python), data science and machine learning knowledge, mathematics and statistics, then refine it further into expert knowledge in data science, machine learning, and artificial intelligence.
If you are unsure of how to build your roadmap, check out the article The Complete Data Science Study Roadmap.
Let me give you an example timeline for your data science roadmap:
- Learn Python proficiently: 3-6 months
- Learn data science and machine learning knowledge: 2-3 months
- Learn maths and statistics: 2-3 months
- Expert knowledge in specific area (e.g. data science, machine learning or AI): 3-6 months
Looking at the example above, you’re probably thinking “that’s nearly a year and a half?!?” Yes, you’re right. This timeline may be ideal for somebody who can only commit part time learning to their data science journey or someone who wants to take the process patiently. There is no harm in taking your time. It is better to be proficient in all of these technical skills than fall behind because you chose to rush the process.
3. Practise What You Learn
Once you have completed your data science learning roadmap, the next thing you want to do is apply your knowledge. Some people may go straight into applying for jobs, assuming that they are ready, but the reality is that you’re not ready until you have worked on a variety of projects to test your skills.
Projects allow you to find your pain points and work on them. They are also valuable in the interview process as it gives your future employer the opportunity to see your skillset.
If you are unsure on how to approach the project aspect of your data science learning, have a look at these articles:
4. Write About Your Journey
People underestimate the value of content, whether it is blogs or social media posts. This is the best way to get yourself out there, network with other fellow data professionals and possibly land yourself a job.
If I could start over again, I would actively be posting on LinkedIn and Medium to showcase my network and my ups and downs of the data science industry. This will allow for others to peer review my work as well as to receive guidance on what I can do to improve my skills, projects, and chances at finding employment.
Many data professionals have found mentors this way to refine their skills.
Wrapping Up
I hope this article has brought some peace to those who are looking to start their data science journey. Starting something new isn’t easy, but the best advice I can give somebody is if you’re going to do it, do it right the first time so you don’t find yourself going back on yourself.
Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.