Upskilling yourself as a Data Scientist

An interview with Applied Data Science Bootcamp graduate, Paulina Figol, on her journey of upskilling herself as a Data Scientist to landing two job offers as a result of the bootcamp.

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What were you doing before you enrolled in the bootcamp?

Before joining the bootcamp, I was already a data scientist, working first in IT consulting followed by a credit risk and analytics team in the lending sector. My job included developing new dual bureau predictive models to minimise credit risk, automating model monitoring or deriving insight from existing and any new data. I already used some machine learning like logistic and linear regression, principal component analysis and random forest.

My biggest challenge at that time was trying to understand and learn too many things at the same time.

What challenges or friction did you encounter with your existing skillset?

My biggest challenge at that time was trying to understand and learn too many things at the same time. I had read a lot about machine learning concepts, looked through case studies and blog posts which made me realise how much there was to discover. At times, I felt overwhelmed with the knowledge and realized I needed to structure what I was learning, identify blanks and prioritise areas in which I could improve.

 

Why did you choose Cambridge Spark as your learning provider?

Following my confusion, I started looking at long term courses that I could undertake, rather than only relying on conferences and self-learning. I also searched for people in a similar position to me to learn from their experiences.

Since the Data Science field is relatively new and is considered as a hybrid of multiple disciplines, I soon found that there was a lack of worldwide recognised certifications/courses or chamber-like organisations. The options were between undertaking a masters in Data science or an alternative certification.

As I greatly enjoy the business environment and wouldn’t want to lose touch with it, I decided in favour of a long-term course. After a few weeks of research, comparisons and gathering opinions of others, I realised that Cambridge Spark had the most comprehensive offer, with a solid teaching background, networking opportunities and - the cherry on top of the cake – a capstone project with partner companies. What matters most to me is applying the knowledge rather than just learning theory.  

There was a lot of knowledge, various backgrounds and support coming not only from the teachers, but also from the students.

What did you enjoy most about the bootcamp?

Along with the extremely hands-on curriculum, I definitely enjoyed studying alongside other people on the course. There was a lot of knowledge, various backgrounds and support coming not only from the teachers, but also from the students.

Despite living in Nottingham, I was always keen to travel to London and genuinely felt sad leaving the course after six months! I’m hoping to attend some of the networking events organised by Cambridge Spark to stay in touch with alumni and current students.

 

How would you sum up your overall experience of Cambridge Spark?

My overall experience with Cambridge Spark was very positive. I liked the challenges which were present throughout the bootcamp, the dedication of the teachers and organisers as well as the practicality of the course.

 

What advice would you give for those considering the bootcamp?

For me, the best investment is investing in your own skills. Data science is a mixture of skills and disciplines, including around statistics, maths, IT and business. This makes it a complex, but extremely exciting trade to be in.

If you feel passionate about it and are dedicated to learning this is definitely the right course for you. Once you start, stay calm! There is plenty to cover and you might think you’re falling behind, but the concepts reappear as the course progresses. Taking notes during and after sessions definitely pays off. The definitions will self-perpetuate in your head and, with time, you will feel more confident.

I considered the bootcamp as time well spent with a reward that I did not expect to see so early – I landed two Data Science job offers (Data Scientist and Data Science Product Lead)!

What are you doing now? 

I considered the bootcamp as time well spent with a reward that I did not expect to see so early – I landed two data science job offers (Data Scientist and Data Science Product Lead)! The company I chose – Mudano – is a London based startup specialising in AI projects in financial services. I am extremely excited to start!

 

What does the future hold for your career?

I am an optimist so I think the future looks bright. I feel that I am close to the stage where I get to decide which direction I would like to go and what to specialise in. The options are unlimited which actually makes it more difficult! I am, however, always looking forward to meeting other fellow data scientists to share opinions and experience!

 

Could you sum up your perspective on the data science field?

Data science is a constantly evolving trade so trying to predict the unpredicted (even as a data scientist) is indeed difficult! On a serious note, defining the scope of data science (both in terms of methodologies and industries to apply them) is limitless.

What this probably means for businesses is to embrace capabilities that arise with the exponential amount of new data and start implementing advanced analytics solutions. The need arises primarily around automation, transparency and data management. For scientists – it translates into continuous learning, presenting companies with explainable models/solutions, and thinking broad!

 

Many people consider this field too complex and have no idea where to start. A few years ago, I felt the same. Now, I see there is no single path to get to the goal and what matters most is doing what you can with what you have. Just start, be persistent and you will reap the rewards. Sometimes sooner than you think!

Looking to kick-start your Data Science career?

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