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Everyone and their dogs are trying to enter the tech industry, whether to learn the program, enter the product management, or in another direction. I am new to the tech industry with only 5 years experience, but when I talk to more people, some are worried about getting foot at the door due to lack of high level education.
In this article, I will discuss my journey and explain what to do and what to avoid.
How did I become a data scientist without a CS degree
Five years ago, I was in a pickle. I recently withdrew my pharmacy degree to pursue my career as a tech professional. I had the choice to go back to university to study computer science or find another way. As British, the university was expensive, and as I had already done a two -year pharmacy, I only got two additional years of official support. The remaining two years, I had to pay for myself. It did not look attractive, considering that it was 000 9000 in a year.
I started looking online for courses that were a part of the price and a data scientist came to the boot camp, 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 returned to study till 11pm.
Nine months more than four years of knowledge and more than 000 36,000 debt. The best thing is that when I got a job, I just had to return a percentage of my salary.
It looked like a dream … unless it happened. And why is it here
Boot campuses are not for everyone
The full purpose of the boot campus is that you have little time to learn everything possible. This may be a breeze for some people, for example those who have time to go for extra hours or those who pick things quickly.
However, it wasn’t for me. I was working all the time and in the evening trying to find out about the Azgar and machine learning model. It didn’t work. I passed, but I couldn’t say with confidence that I was a data scientist.
Why here:
- Time and patience in learning the language of programming. It requires a lot of exercise and this is a process that you can’t quickly do.
- Boat campuses don’t provide you all the knowledge needed to be a successful data scientist. Is it possible to do 4 years of knowledge of the university in 9 months? Maybe not. But to be an expert, you want to make sure you know everything and understand it well. For example, in my boot camp, we rarely touched the importance of mathematics and statistics, which is data science bread and butter.
- Guidance and help is necessary when you are learning something new; Therefore, you want to make sure you don’t feel that you are going through learning content, and you can ask for help when you need it before going to the next step.
Data Science Learning recommendations
Now you understand the trials and temptations that I spent on my data science journey, these are my first indications:
1. Determine realistic goals
The first thing you should do is to determine realistic goals. These will be unique to you on the basis of your personal promises, free time, etc. You want to start your data science journey with realistic expectations that are associated with you and just with you. Do not compare yourself to others, and do the work that works for you.
For example, you can become a full -time mother and give only 10 hours a week to learn. That’s all right. Don’t compare yourself to a 19 -year -old child whose sole purpose is to learn data science.
2. Keep the data science plan together
Once you set your goals, you should plan a data science plan. This is your data science journey and will contain all the elements of data science you need to learn. The key points you want to focus on is a programming language (ideal), data science and machine learning knowledge, mathematics and statistics, then improve the expert knowledge in data science, machine learning, and artificial intelligence.
If you are not sure about the construction of your roadmap, see the full data science study roadmap to the article.
I offer you an example timeline for my Data Science Roadmap:
- Learn from the skills to Azigar: 3-6 months
- Learn Knowledge of Data Science and Machine Learning: 2-3 Months
- Learn Mathematics and Statistics: 2-3 Months
- Expert Knowledge (such as data science, machine learning or AI) in a specified area: 3-6 months
Given the above example, you are probably thinking “It’s almost a year and a half years old?!?” Yes, you’re fine. This timeline may be ideal for someone who can only commit his data science travel or to learn someone who wants to take this process patiently. There is no problem in taking your time. It is better to master all these technical skills because you have chosen to hurry in this process.
3. Follow what you learn
Once you complete your data science learning road map, the next thing you want to do is apply to your knowledge. Some people can go to apply for straight jobs, assuming that they are ready, but the fact is that you are not ready until you work on numerous projects to test your abilities.
Projects allow you to find and work on your pain points. They are also valuable in the interview process as it gives your future employer an opportunity to see your skills.
If you are not sure about approaching your data science learning plan, take a look at these articles:
4. Write about your journey
People reduce the value of content, whether they are blogs or social media posts. This is the best way to get yourself out there, network with other fellow data professionals and possibly get yourself on the job.
If I can resume, I will actively post on LinkedIn and Medium to display the fluctuations of my network and data science industry. This will facilitate others to seek guidance on what I can do to improve my work, plans and jobs in search of employment.
Many data professionals have found such guardians to improve their abilities.
Wrap
I hope this article has brought some comfort to those who want to start their data science journey. It is not easy to start something new, but the best advice I can give to someone is that if you are going to do this, fix it for the first time so that you do not go back to yourself.
Nisha Arya A data scientist, Freelance Technical Author, and Editor of KD Nogetts and Community Manager. She is particularly interested in providing data science career advice or theory of lessons and data around science. Nisha has covered the topics widespread and a desire to find different ways that can benefit the longevity of human life. A deep learning, anxious, seeks to expand his tech knowledge and written abilities by helping others guide others.