Wondering what a typical day looks like for a data scientist? This article breaks down the various tasks and skills required for success in this field.
Have you ever wondered what it’s like to be a Data Scientist? Or what Data Scientists do on a day-to-day basis? The world of data science is a fascinating world where you can expect to dive deep into the vast ocean of data to extract meaningful insights and make data-driven decisions for your employer.
To shed light on the day-to-day realities of this captivating profession, we sat down with Pilar Rius Muñoz. A seasoned Data Scientist at the The European Commission for the Control of FMD And Similar Transboundary animal diseases, she shared her first-hand experience of working in her Data Science role. Here are some of the topics, that we discussed with Pilar:
- A typical day as a Data scientist
- The types of problems that a Data Scientist addresses
- The skills that makes successful Data Scientist
- The rewards and challenges of working as a Data Scientist
- The benefits of a bootcamp education in the journey of becoming a Data Scientist
CodeOp: Good afternoon Pilar! Thank you for joining us. Could you tell us what you did before becoming a Data Scientist?
Pilar: Thank you for having me! I have actually changed careers quite a few times. I started out as a dancer which was really fun. But then I started to think about building a career which can support me in my later years. This got me into the world of banking as I had some background experience in Economics. Despite this industry being very male-dominated and having less flexibility in terms of a work-life balance, I was very ambitious. With a lot of hard work, I managed to achieve my goal of being the COO of the organisation I worked with. It was very satisfying for a while to have achieved exactly what I set out to do.
CodeOp: So why and how did you transition into data science?
Pilar: After a few years, I was in a different place in my life having started a family. My priorities in life changed. I started looking out for opportunities that provided more flexibility to have a healthy work-life balance, that allowed me to come back to my home country to be closer to my extended family and that also allowed me to earn a healthy income to support my family. My research led me to understand that the world of technology and coding skills was where I could find all these things I was looking for.
The next natural question that I had was “How do I break into this industry?” Given where I was in my life, a full-time university degree was neither manageable nor financially feasible. That is when I came across the concept of a Bootcamp education. Now there were quite a few options out there which I explored but ultimately, I had three main criteria: It would help me transition into the Data Science industry, it has a supportive community and a high-quality educational curriculum. I found that CodeOp ticked all these boxes, and that’s where my journey as a Data Scientist started!
CodeOp: That is so inspiring to hear! Could you tell us about your current role and what a usual day looks like for you at work?
Pilar: Certainly! So I actually started out as a Data Analyst in my current organisation which is EuFMD, a division within the Food and Agricultural Organisation in the UN. But I soon realised that being the only one in the team who had coding skills meant that I was doing more than simply analysing data. I was supporting my team with various technology related questions which were beyond what you would expect a Data Analyst to be doing. So within a year, I got promoted and my job title changed to being a Technical solution and Data Scientist which had a broader scope and better reflected what I did on a day-to-day basis.
A typical day for me has various tasks that range from collecting relevant data, integrating different datasets, cleaning data and analysing the data. I also do tasks which you can expect a Data Engineer to perform such as designing and building systems for collecting, storing, and analysing data at scale. This is because in a small team where you are the only member with technical skills, you need to adapt and use these skills for tasks beyond simply analysing data. This can include developing apps and dashboards and developing data flows among other things.
CodeOp: That’s fascinating and congrats on your promotion too! It looks like you are a one-woman army in your team when it comes to technology. Could you share an example of a typical problem that you have solved or tend to solve in your organisation using your Data Science background?
Pilar: So one of the key tasks on which I spend considerable time in my job is collecting data from different sources, integrating them and getting it into a shape that can be analysed. This might seem trivial but is one of the most important tasks when working as a Data Scientist. Today we actually have data everywhere around us.
The issue is that data from different sources come in different forms (such as audio, text, video etc) and relate to different time periods (some are collected annually while others are collected daily). Sometimes what is recorded in one country can be recorded very differently in another country. To give an example specific to my job, the domestic animal population can have a different definition in Spain vs in the US.
When you do any course on Data Science, you usually end up doing one or more projects where you use some readily available datasets that someone has put together to then develop a machine learning model to predict something (such as the stock price of Google or number of bird species in Scotland). But when you start working in a job, the reality is very different. Data collection and data cleaning takes up a considerable amount of time in any Data Scientist’s list of responsibilities.
CodeOp: And what skills do you find yourself using the most in your day-to-day job as a Data Scientist?
Pilar: Let me start with the data cleaning tasks that I mentioned in my last answer. These require some skills which are taught in a typical Data Science Bootcamp such as SQL coding – a very helpful skill to have. And then there are certain skills such as using an API to collect data which you might not necessarily learn in a Bootcamp focussed on Data Science. But the requirements of such skills vary from organisation to organisation.
In my current role, I’m part of a small team so I work with diverse types of Big Data sets which require me to use cloud technology (such as AWS) to design automated data flows. So I need to understand the design and deployment of cloud-based data infrastructure. The good news is that the coding skills learnt from a Data Science Bootcamp along with online videos definitely helped me to hone these skills on the job.
Then there are soft skills which are often not emphasised much but are equally important. I would say communication skills are at the top of my list. You need to be able to communicate the technical parts of your job to a non-technical audience who will mostly be senior members of your organisation interested in knowing what the data says. For example, after analysing the data, you need to translate your findings of a complex machine learning model into the key conclusion that solves or answers the senior team members’ questions.
CodeOp: That’s very interesting to know! This actually prompted the next question – how much of being a Data Scientist is coding vs giving presentations vs collaborating with your team members/other teams?
Pilar: I would say that I spend around 50%-60% of my time on technical aspects (such as coding, developing data systems, maintaining dashboards etc) of my role with the rest focussed on non-technical aspects such as collaborating with teams. The coding itself actually doesn’t take up much of my time. This is especially true in the world of ChatGPT where if you know the question, you can use AI to write you the basics of the program you need. But the important first step is to ask the right question and this is where I spend most of my time. It requires me to understand the needs of my team and what problem we are trying to solve. As such, I do spend a fair amount of my time checking emails and presenting updates to teams.
CodeOp: That’s very helpful to know. So what would you say is the most rewarding aspect about being a Data Scientist?
Pilar: The most rewarding thing is when I stumble upon a new and challenging topic in my job and I persist long enough to conquer it. And in my job, I have many such instances of learning new things regularly given how rapidly the field of Data Science is developing.
For example, in the first weeks of my job, I found myself staring at new coding languages such as R and new programmes written by previous team members which I needed to take over. But over time, I have conquered each of these challenges! Another rewarding aspect of my work is the fact that I can help my team members do their jobs better, such as building a dashboard which which helps them learn more from the data. With just a few clicks. While imposter syndrome can be very real at first, you soon realise how much potential there is for you to learn and grow in the job.
CodeOp: And the challenging part?!
Pilar: I would actually say the same thing. Moving from being a student to a professional is challenging at first. For me personally, I had to take up tasks which are beyond what a typical Data Scientist role might ask for, such as working on Data Engineering tasks. But over time it gets easier and with each hurdle you become better at your job.
CodeOp: So how did your CodeOp experience help in getting to where you are now?
Pilar: For me, my CodeOp experience was fundamental in helping me make a transition to the tech sector. To put it simply – my life before CodeOp looks very different to my life after CodeOp! CodeOp helped me to get a job in the tech industry which was my main goal when starting the bootcamp. The CodeOp support team was extremely helpful in helping me identify how to go about the job application process even before we finished all the lectures. This meant I could actually secure a job even before I finished the bootcamp.
If it weren’t for CodeOp I wouldn’t be where I am today – in a flexible job which pays me four times the salary than my previous job while being able to spend time with my kid.
CodeOp: And finally: are there any tips you’d like to share with anyone looking to join the CodeOp Data Science Bootcamp?
Pilar: My advice would be to first understand what your strengths are and what you enjoy doing. Because sometimes you might enjoy doing something that isn’t really your strength. It’s good to align your strengths to your career. For example, if you are good at designing and interested in Data Science, you might be well-suited for visualising data and building effective dashboards. Then choose the right bootcamp to support you in the learning journey and in your career transition if you are looking for one.
Secondly, try your best to prepare yourself for the Bootcamp by doing your pre-readings and online courses. This will help you to follow the pace of the bootcamp which can be quite intensive.
Lastly, make the most of the community provided by the bootcamp. You never know who you’ll meet and who can help you get your new job or become your co-founder to develop the next new thing in tech!
Huge thanks to Pilar for sharing with us a deeper understanding of the dynamic world of Data Science. From working with large datasets to developing predictive models, data scientists like Pilar play a vital role in harnessing the power of data to drive innovation and solve different data problems. With a curious mind and a passion for uncovering hidden insights, they continue to shape the future of data-driven decision making.
CodeOp’s Data Science bootcamp was designed to provide women, trans and non-binary people with a background in STEM, Finance, Economics and Business Intelligence the skills they need to build a career in the analytics industry. If you are curious to learn more about our Data Science bootcamp, you can schedule a chat with out admissions team.