Data science, no degree

Everyone and their dogs are trying to enter the technology industry, or by learning to program, enter product management or in a different direction. I am completely up-to-date in the technology industry, with only 5 years of experience, but when I talk to more people, some are worried about the foot in the door due to a 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 scientist of data without a degree cs

Five years ago I was in the marinade. I recently gave up a pharmaceutical diploma to continue my career as a technological specialist. I had a choice of return to the university to study computer science or find another route. Being British, the university was exorbitant and as I have already done two years a pharmacy, I would have only two additional years of government support. I had to pay for the other two years. It did not look attractive, considering that it was 9000 pounds a year.

I started looking for courses on the Internet, which were a fraction of the price and came across a bootcamp scientist who looked great: 9 months full -time hours, which worked perfectly with my full -time role. I spent the day working and returned to college until 23:00.

Nine months of learning were much more attractive than four years of knowledge and 36,000 pounds of debt. The best part is that I had to pay off the percentage of my salary when I got a job.

It seemed that this dream … until it was not. Here’s why.

Bootcamps are not for everyone

The goal of Bootcamps is that you have little time to learn everything you can. It can be simple for some people, for example those who have time for extra hours from the side or those who gather quickly.

However, this was not the case for me. I worked full -time and spent evenings trying to learn about Python models and machine learning. It didn’t work. I passed, but I couldn’t say that I was an expert scientist.

Here’s why:

• Learning programming language requires time and patience. It requires a lot of practice and is a process that cannot be hurried.
• Bootcamps do not provide all the knowledge needed to succeed the data scientist. Can you press in 4 years of university knowledge within 9 months? Probably not. But to be expert, you want to make sure you know everything and you understand it well. For example, in my bootcamp we rarely touched on the importance of mathematics and statistics, which is bread and data butter.
• Tips and support are necessary when you learn something up-to-date; That’s why you want to make sure you don’t feel that you spend your educational material and you can ask for aid when you need it before moving to the next step.

Recommendations for learning data

Now you understand the attempts and anguish that I went on on my journey to study data, here are my best tips:

1. Set realistic goals

The first thing you should do is to set realistic goals. They will be unique to you based on your personal obligations, free time, etc. You want to start traveling with learning data with realistic expectations that are consistent with you and only you. Do not compare yourself with others and do what works for you.

For example, you can be a full -time mother and you can only give 10 hours a week to study. This is completely fine. Do not compare yourself with the 19-year-old, whose only goal is to learn about data.

2. Assume a data learning plan

After setting your goals, you should create a data learning plan. This is your journey on data and it will consist of all elements of data learning that you need to learn. The main points you want to focus on are the programming language (ideally Python), knowledge about data learning and machine learning, mathematics and statistics, and then share it to expert knowledge in the field of data learning, machine learning and artificial intelligence.

If you are not sure how to build a road map, check the article Complete road map Study Science.

Let me give you an example schedule for your scientific road map:

• Learn Python fluent: 3-6 months
• Learn knowledge about learning and machine learning: 2-3 months
• Learn mathematics and statistics: 2-3 months
• Expert knowledge in a specific area (e.g. data learning, machine learning or AI): 3-6 months

Looking at the above example, you probably think: “It’s almost a year and a half?!?” Yes, you are right. This timeline can be ideal for someone who can only commit part -time education in their journey to learning data or someone who wants to patiently undertake a process. Not in terms of. It is better to be expert in all these technical skills than to stay behind because you decided to hurry the trial.

3. Exercise what you learn

After completing the road learning map, the next thing you want to do is to apply your knowledge. Some people may immediately apply for a job, assuming that they are ready, but in fact you are not ready until you work on various projects to test your skills.

Projects allow you to find pain points and work on them. They are also valuable in the interview process, because it gives the future employer the opportunity to see their skill set.

If you are not sure how to approach the aspect of your data learning project, look at these articles:

4. Write about your journey

People underestimate the value of content, regardless of whether they are blogs or social media posts. This is the best way to get there, make contacts with other data professionals and possibly get a job.

If I could start again, I would actively publish on LinkedIn and Medium to present my network, ups and downs of the data industry. This will allow others to review my work, and also receive tips on what I can do to improve my skills, projects and chances of finding employment.

Many data specialists found mentors in this way to improve their skills.

Wrapping

I hope that this article brought some peace to those who want to start their journey with learning data. Starting something up-to-date is not simple, but the best advice I can give someone is that if you are going to do it, do it the first time not to go back to yourself.

Nisha Arya He is a scientist of data, an independent technical writer, as well as an editor and community manager for KDNuggets. She is particularly interested in providing advice on career or tutorials and knowledge based on theory on data learning. Nisha covers a wide range of topics and wants to discover different ways in which artificial intelligence can bring the benefits of longevity of human life. Nisha, an avid student, tries to broaden his technological knowledge and writing skills, while helping in running others.