Data science has become increasingly popular over the last few years with most companies insisting they use it. But what does it actually entail? Here are some of the things I have come to realise after working as a data scientist for the past six months…
It’s not all about building fancy machine learning models.
You probably imagine data science projects involve performing statistical analysis, code development and building predictive models. Yes this may be true, but have you considered just how much time is spent collecting and cleaning relevant datasets? I’ll be honest, I spend more hours than I care to admit sifting through hundreds of datasets on the Office for National Statistics website. However, this is a crucial part of the analysis, and must be carried out thoroughly in order to produce the best possible results further on in the analysis.
It is impossible to know everything in the field of data science.
Being a data scientist offers the opportunity to learn something new every day – every project brings different challenges and a different branch of data science to learn. Of course, the fundamental knowledge behind data science is necessary, (maths, statistics) but machine learning algorithms are continually developing. It is important to keep up with training courses and find new areas to expand your knowledge of the subject.
Don’t underestimate the importance of non-technical skills.
Obviously the technical and coding skills are important. However, this will only get you so far in this area of work. There is no use in finding interesting insights in your client’s data if you are unable to explain the results in a clear and accurate way. Having the ability to prioritise to meet deadlines, knowing the strength of each team member and building strong relationships are also important interpersonal skills to possess.
Networking and involving yourself in conferences is important.
Attending conferences and sitting on committees is a valuable way to build relationships and network with people in a similar field. It can be helpful to communicate ideas and discover people out there who share your frustration over some python code. Networking will open doors for collaborations, as well as provide a chance to hear about exciting future opportunities.
All data science experiences are different.
As there are so many areas of data science, the role varies massively; from specialising in machine learning and building predictive models to data engineering in which you are creating data pipelines. I have found that working in a small company is providing the valuable opportunity to experience all areas of data science. I couldn’t recommend it more.
By Holly Jones