It’s been literally like more than 1 year since I’ve been in Data Science field either it’s working at university as an Assistant Data Scientist or doing Data Science projects with some companies globally. From my experience while being in field and doing stuff, I’ve seen dramatic changes happening in industry. For example – Earlier it used to be a great practice to make reports at the end of Data Science Lifecycle, summarising results of project; but that’s not the case anymore many companies are shifting from using reports to online interactive dashboards.
Let’s see how advancements in other domains of technology would impact Data Science over the next few years –
Truth be told at the first point Data Science demand is growing like a wild fire with thousands of job roles coming up every month across the globe. Owing to this demand there’re many people trying to get into it and as quoted by Harvard Business Review Data Scientist is sexiest job of the 21st century.
Developments in Machine Learning
Every week there are a lot of articles being published in reputed newspapers about ‘Developments in Machine Learning’. Literally there are a number of new techniques/algorithms are coming up in Machine Learning Modelling. As everybody knows Modelling is quite important part of Data Science Lifecycle and as there are new ways coming up for modelling, it would be largely impacting how Data Science Models are made or trained.
Development of tools for Data Science
Data Science Life cycle typically involved following steps: –
At each step of this cycle there is some potential to develop a tool for doing that process. For example – ‘Data Understanding’ is quite an important step, as nowadays this is typically done by using Python Programming Language. Fun fact is that programming aspect in this process can be abstracted out and a tool for that can be made.
So in the next few years as these tools emerge either out of existing companies or may be some new startups then it would hugely impact the Data Science Field, pushing people to learn these how to use these tools.
Risk of Automation
Every one is freaking about AI nowadays and most of people fear that they would probably loose their job to automation. May be it’s true to some extent but the Analysis part of Data Science Lifecycle would be still there. Moreover even if there would be automation in Data Science, who will be helping to implement these automation stuff. It will be definitely be people. So do not worry and jump into Data Science straightforward.
Despite the fact that there is a lot of hip hops going into the Data Science industry, it’s still worth it to learn Data Science considering the fact that average salaries across the globe are quite higher. So the best possible scenario is learn Data Science. Plus point here is my guide to learn Data Science –
It’s quite import to learn through reading, whilst I have been just 1 year into Data Science. Still I would like to share how I personally started in Data Science through reading books.
Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic provides a great insight into how to translate data into words, ultimately curating a kind of ferry story out of data.
Data Visualisation: A Handbook for Data Driven Design by Andy Kirk helped me a lot to understand the capabilities of showing data in the form of visuals. Would be worth reading it, if your just on the brink of jumping into Data Science.
Podcasts are a great way to learn new stuff nowadays, it’s emerging like Data Science. May be Harvard Business Review should say Podcasts are sexiest way to learn new things in 21st century. Anyway fun aside the following is the list of podcasts to follow for learning data science –
O’Reilly Data Show – My favourite one
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