Learn How Katya - A Petroleum Expert - Became A Data Scientist

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Black Cat Data had a chat to Katya 12 months after making her career move from petroleum to data science. Here's what she had to say:

Can you explain your career before changing to Data Science?
"I was working as a petroleum engineer after my graduation and doing only engineering back then. I moved to Australia and started thinking of what I was interested in. I looked at Data Science and watched some courses where I realised that my skills can actually be useful in general analytics like data science, and so I decided to get into data science."

Why did you choose to change your career to Data Science?
"Petroleum engineering, to some extent, is also a very analytical job. I realised there are so many different types of data sources outside of geological type of data. Petroleum engineering also involves making predictions because you have to predict performance of the reservoir so that is how I developed my analytical skills. I’ve always been interested in mining data so data science just seemed to be the field perfect for me. Also, recent advances in the computational capacity has given the ability for many fields to be pushed towards data science."

What was your plan to change your career to Data Science?
"In the beginning, I just started learning python and R but after a while I realised that I needed help. I had a lot of questions and required help or some sort of guidance which I found through Data Science Full Time Program which was a really good choice as it provided me support for my learning."

Why did you choose this practical in classroom education programme over other options that you may have had?
"Well the first reason is that, if you are learning by yourself, you don’t have anyone to ask questions to which is the biggest problem with online education. I needed experienced mentors and tutors around me, so being surrounded by peers and tutors is why I chose that path. I already did my bachelor in engineering physics at university and I did my masters in astronomy, so university always gives calculus, linear algebra and statistic which I’ve already done. All I needed was machine learning algorithms, python and cloud computing databases. For me, a classical university degree wasn’t the solution in terms of providing this."

How difficult was it for you not being from an IT background, to learn Data Science?
"I believe that python and R are the simplest languages to learn. Although it was a little bit challenging and frustrating at times, practice really made a difference for me to move quickly to the point where I could write a code and solve any problems I encountered."

How did you get into the Industry after completing your training?
"The program helped us do some workshops that provided industry exposure, we experienced a couple of real interviews with companies such as Commonwealth Bank which gave us the idea of exactly how the industry operates.  I gained an idea of what these companies need and how we need to tune our skills. This all made it pretty easy for me to get my first job. I specifically chose start-up cultures because I had mentors who told me that if you want to learn quickly, start-ups will expose you to a lot more things."

Tell me about what you’re doing in your current role?
"My current role is a Data Scientist at Earth A.I. Basically what I’m doing is building an artificial intelligence platform for exploration mining companies trying to help them find potential deposits of minerals. For this purpose, we use machine learning algorithms in unsupervised and supervised learning."

How has your career progressed since entering the industry?
"In terms of job title, I am a Data Scientist at the moment. In terms of knowledge and skills I have progressed a lot in the use of databases. I have so much experience with SQL, python and machine learning in general. The past year working with start-ups has made such a massive difference for my skill set."

Now that you have worked for more than twelve months in industry, what is your outlook on the growth of A.I related jobs?
"Interesting question. I can see a tendency that before it was just about the development of algorithms itself. Whereas now, A.I is moving towards all industry applications. For example, my industry is in the mining industry, whereas in my previous company it was in the music industry. So, I think A.I is now spread across all industries finding particular applications in every industry. Some algorithms work for particular kinds of industries and programmes, they’re all different."

What domains have you worked in since joining the industry. Secondly, what domains are you going to focus on for your career?
"Well I was working with my first company in the music industry: it was meta data, it had a programme that was all Big Data. The second was mining exploration fields. At the moment, I am joining a company that is case by case which I am very excited about where basically you can have one kind of problem where you need to apply a solution and then you might get another problem that’s completely different. All different industries, different problems, different solutions and different technologies which is why I am so excited."

How do you feel now after successfully making that change from the petroleum industry to Data Science?
"I feel like I like my job, it is something I really like. As much as I liked my previous work, petroleum engineering is amazing and all about uncertainties and is challenging. Data Science too has its challenges and uncertainties. You basically have a problem and you need to solve it and I feel great. I’m always excited about work so overall this was just a really good move for me."

How do you see Data Science being used in the Petroleum Industry, the industry you worked in previously?
"I was looking at what people write on LinkedIn and what they thought and what experts thought. However, the problem with petroleum engineering for data science is you need a lot of data. In the petroleum industry when you drill in the well you basically have an amount of data that is just around that well which is very limited and frankly speaking I would say that there is some application which can slightly improve. But we need to understand the petroleum engineering is huge so if we were to talk about the Internet Of Things (IOT) and Big Data, you can monitor that this would be the best application."

 

If you are interested to learn more about up-skilling and landing a job in the Data Science & AI industry, you can book a consultation with a Big Data Career Consultant.

Peter Harpur