This content originally appeared on DEV Community and was authored by Dion Nekesa
***My final lesson at Lux Academy was deeply enriching with knowledge. We started by learning how to write an ideal technical article on platforms such as 'Dev.to' and 'Coderlegion.com' to make the learning journey more captivating. I won't dwell much on this since the book _Technical Writing For Dummies*_ very well explains it.
Now onto the hands-on technical project. We were required to fetch data from weatherapi or openweathermap for different regions in Kenya, store it in a database of our own choice(relational or non-relational), then analyse weather patterns using Python.
I signed up for an account with 'weather_api' then fetched the data using an API key on Google 'Colab', which was my preferred Python notebook, stored my data in a non-relational database with MongoDB then proceeded to Tableau to draw insights from it(the data) using different visualisation features. I chose 'Colab' because it makes it easier to use Python libraries such as 'Pandas' and 'Numpy' by simply using the command 'import' ,besides the fact that you can efficiently save the code to a github repository at the tap of a button. I didn't have to clone the repo on my machine then start running the 'add' ,'commit' and 'push' commands. In my books the visualisation part was supposed to be the simplest but I ended up spending the most time on it, moving from tutorial to tutorial just to understand how I can manipulate my dashboard to look better and meaningful.
I am happy to have completed this three-week Data Analysis and Analytics journey at Lux Tech Academy Kenya and gained so much knowledge. Shout out to my cool tutors: Peter Gatitu, Doreen Wanyama, Elvis Wangari and Harun Mbaabu for the great job done.
The numbers were tough but I was definitely tougher!
This content originally appeared on DEV Community and was authored by Dion Nekesa
Dion Nekesa | Sciencx (2024-10-16T17:22:22+00:00) DATA MINING AND VISUALISATION.. Retrieved from https://www.scien.cx/2024/10/16/data-mining-and-visualisation/
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