How and Why I started my data science journey with Udacity — Bertelsmann
Can anyone from a non-computer science or programming background be a data scientist?
I would say “YES, IT IS POSSIBLE”. And I say this with conviction because I have seen myself transforming from a complete novice to someone who is now able to break down and solve problems by writing codes in Python and SQL.
Note: By this I do not imply that the learning process and the subject matter is an easy one. But with determination and perseverance, anything is possible.
So what drove me into data science?
Two months ago, my mentor — a young and passionate female software developer encouraged me to apply to the Udacity — Bertelsmann Data Science Challenge Scholarship. As a diligent mentee, I applied with the least expectation of receiving the scholarship. However, this did not deter me from writing a convincing application as to why pursuing this program was important to me and how I could leverage this skill to solve problems in the natural resources management sector. As a natural resources professional with interest in forest, agriculture and water sector, I could see the immense power of data science in producing insights to issues such as water scarcity, wildfire, climate change mitigation and adaptation and so much more.
Before this, I had already taken an interest in Python to use it in Geographic Information Systems. To get the feel of it, I participated in Python workshops and other online courses. Here I must admit that it was not the easiest and the best way to learn concepts such as data types, operators, functions and methods. I even borrowed a book on how to learn Python to immerse myself into it. But there were challenges with learning all these concepts by myself.
A week after my application, I received a notification email that I got selected for the scholarship program. I was thrilled and excited to learn but this time with a community of data science enthusiasts and experts across the globe. I am not the best when it comes to learning online programs at your own pace kind. But this time, I did very well.
Why?
Because I had other fellows studying along with me spread across different parts of the world, virtually connected through the forum and slack channels. I had a go to place — the forum, where I could find answers to my questions and help answer other’s questions too. Collaboration, support, appreciation and group work were some of the elements that made this entire learning process enjoyable and kept me motivated. So much enthusiasm for this challenge that I volunteered to be a student leader for data beginners group. I connected with other student leaders (from Nigeria, Bangkok and Angola) recently over a Skype call to discuss ways to keep data beginners group members motivated and assist them with useful resources.
To conclude, am I a data scientist already? Not yet, because ‘Rome was not built in a day’. I am a motivated data scientist in the making and this is the start of my journey towards perfecting the art of talking with data.
