Welcome back everyone!
In this blog I will tell you how I first encountered the world of Data Science and provide a deeper explanation of my initial path I have taken to get into data science along with my initial thoughts and experience.
Up until 3 years ago I can confidently say I hadn’t a clue what the words ‘Data Science’ or ‘ Machine Learning’ really meant It wasn’t until I got some industry experience in advanced analytics within my degrees placement year that I even thought about the industry of data analytics. At the time I had no real idea of what Artificial intelligence could do or how it could be harnessed relatively simply only using a few basic tools. Very early in my new role I was getting to see projects which were being worked on and it got me thinking how they could directly be transferred into everyday tasks as well as to business to completely optimise all of its time and resources, this straight away sparked an interest and really set me on the path to where I find myself today.
My next step after my placement had ended was to continue my learning, so I turned to self-teaching myself everything Data Science. I knew I needed to keep learning and stay up to date with constant emerging technologies in the field. I wanted to learn and continue to expand what I had first encountered within my placement. Following in the advice given to me by many engineers who I worked with that self-teaching was of upmost importance regarding learning the skills required for machine learning and data science. I undertook several introductory courses in R, Python and SQL to help get me up to speed to the point where I could start to tackle data science projects of my own and help feed my curiosity to learn. I decided once my degree had finished that I would like to look at pursuing an MSc in Data Science to allow me to sharpen up my skills and expand my learning to the large area of Data Science which would encompass everything from visualisations, machine learning, data warehousing, data cleaning, statistics and business intelligence. Each of these topics are a core foundation to Data Science and all work with each other.
Due to my self-teaching and continued education I am not able to apply it to my current role within industry. I am equipped with this expertise which allows me to be constantly looking for areas of improvement as well as situations where machine learning or data management systems can be implemented to benefit others around me.
In my next blog I will cover resources for self-learning and methods which I have found very helpful when I comes to progressing in fine tuning the vast array of skills utilized in the field Data Science.