Data Scientist – Decision and first difficulties

By | 18th October 2017

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As the title suggests, in this post I will try to talk about the starting point, what triggered my decision to become a data scientist and the first difficulties I face as a ‘newbie’ in the field.

Starting point – The Trigger

My starting point was a need to change my life in order to become happier.

My first degree was in Management and Marketing from RGU, before graduation, my friend Thomas and I, decided to start a project called Groupmates. We decided to try and make it into a business. In short, Groupmates was a real-time communication system for higher education with AI for smart conversations. If you wish to know more about my background check out my WebCv

Shuttered my world

That was back in the beginning of 2014, I was 21 years old. Last year, just before I turned 24 we decided to terminate our work at Groupmates. My world was shuttered, I started freelancing with marketing projects and started to experiment with multiple projects, but nothing made me feel like it was my next destination.

Eventually I realised that I was depressed so I had to find ways to help me heal.

The magic conversation

In spring of 2017 I had a phone conversation with a dear friend who knows me for many years now and she was an MSc Data Science for Business students at the University of Stirling at the time. I explained that I didn’t feel like I am moving forward and she asked me why I don’t study something new.

Long-story-short, I decided to follow my friend’s advice.

The universe is magical

After some basic research, I decided that I will apply only for the MSc Data Science for Business at Stirling University because of the programme’s focus on the business applications for data science. My reasoning was that if I apply to only one programme and institution and I get accepted that meant that’s what I had to do. It was up to the universe.

Look at the sky. We are not alone. The whole universe is friendly to us and conspires only to give the best to those who dream and work.
Quote by A. P. J. Abdul Kalam – Former president of India

I got accepted and this brings us to this blog.

The ‘hard’ skills

A data scientist needs to have an understanding of skills like statistics, analytics, process mapping, etc. We often like to categorise skills into ‘soft skills’ and ‘hard skills’, see the infographic below for a nice definition of hard and soft skills.

Infographic of hard and soft skills

Simple visual aid to define hard and soft skills

The problem is that I have the soft skills, but the hard skills required for the profession of a data scientist are a challenge to me. In future posts I will try to address those challenges and how I deal with them.

the technical skills

Having an understanding of statistics it is not enough to call yourself a data scientist. Data scientist work with large datasets usually, what we call big data. In contrast, statisticians work with a portion of a dataset or what you can call a dataset that represent the whole population.

Now, technology allows us to analyse the whole population. But you cannot do that with pen and paper, you need to use software and if you really want to be efficient it is suggested that you use tools like Python and R to help you analyse and visualise data.

No need to panic

There is no need to panic about technical skills though. You only need to understand the basics and there are a lot of free resources online to help you start.

In future posts I will write about what I use and how I learn in more detail.

Don’t NEGLECT the soft skills

Although hard skills are essential to have, soft skills are also very important. The job of a data scientist doesn’t end at collecting, organising and visualising the data. Our job is to understand the data, make sense of them from a business point of view, create what we call data products and communicate the value of everything to multiple (if not all) departments of an organisation.

To create data products and communicate your ideas and understandings efficiently you need soft skills.

Conclusion

Congratulations! You have made it to the end of this introductory post of my data science journey. Now you know that if you choose to become a data scientist and you have no background in data you will have to learn a lot in a short amount of time. You also know that you don’t need to panic because you can learn almost everything you need to know as a data scientist for free.

If you are considering to become a data scientist and you have questions, concerns, etc. please don’t hesitate to contact me via email or via a comment.

My goal is to publish once a week and sometimes more. Stay tuned.

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