Skip to main content

The complete GCI Journey


A summary of my experience this year...


The GCI 2017 - 18 opened up arms for teenagers on 27th of November, 2017 for students to attempt tasks encompassing of coding, outreach, quality assurance and user interface problems. The competition comprised of organizations who had an open - source structure.

I started my journey a month late, on 27th December, but I still managed to attempt a fair few number of tasks. Many of them for a great organization like LibreHealth, Mifos and Liquid Galaxy Project. I spent the highest fraction of my time, working with a great Health IT company called LibreHealth. My main goal was to expand on my sector of design. This included wireframe generation, web component designs, presentation, and artworks. I really enjoyed it, I never thought I would design mockups or dashboards for a proper company :)







There were parts where things did not seem too good … Over my GCI participation was also a load for exam preparation. A rigorous schedule had to be maintained in order to balance them both but, to be honest, I spent more time working on GCI tasks :)

A big chunk of the GCI experience has been the conversations with the Mentors. To be able to converse with an experienced programmer or designer across the globe transcends the participant's capabilities to a different level. The prizes were also something to thrive for :P and because I am only 14, it’s a great deal of excitement having competed in this competition.

Another Great point was the streamlined and easy experience google had provided us with. The whole UI of the site was appealing and life was made easy by conversing directly with the mentors and the ease of project uploads. The community was really great and helpful. I was a member of the LibreHealth community and the whole interaction was fun and VERY HELPFUL. Any problem with the task could be asked on the chat group and within minutes meaningful replies from both contestants and mentors chipped in. It was like you’re a part of this big family where each member helps out the other.

After a fun and great learning affair, the GCI is slowly concluding and closing it’s curtains for the year, leaving a good taste in the mouth. After what I have experienced this year, I would surely come back to it every year till I am eligible to compete. I really want to thank Google and LibreHealth for the opportunities they have given us. See you next year GCI!

Comments

Post a Comment

Popular posts from this blog

Impact of Chatbots on Human Driven sectors of business

Chatbots are Machine Learning and Deep Learning powered programs that can conduct meaningful conversations. Most of them use technical attributes like Natural Language Processing and Context Analysis to converse with a human or another chatbot. The bot is programmed to self-learn as it is introduced to supplementary dialogues and words. In consequence, as a chatbot receives new voice or textual dialogues, the number of inquiries that it can reply and the accuracy of each response it gives increases. A Chabot can converse with the user through self-embedded messaging platforms like Facebook Messenger or through various flagship Assistants such as Google Assistant, Siri or Amazon Alexa. Nowadays, chatbots are being deployed in fields where their use clearly seemed to be obvious. Firms engaging in Financial Planning, Investment Management and Expense Tracking could reap a wide array of benefits when they make use of this technology. They can have a lasting impact on human-driven sec...
3 comments
Read more

Data Visualisation with Python : My course on Skillshare

Recently, I launched a class on Skillshare called " Intro to Data Visualisation with Python : Create Comprehensive Plots with Matplotlib and Numpy ". It's a class I sought to create as my flagship project for the summer of 2019 This class is all you need to visualise data with Python. It aims to teach the essentials required to visualise data with Matplotlib, a library for plotting in python. The class also covers NumPy in a brief manner, a scientific computing library that goes hand-in-hand with the visualisation capabilities provided by Matplotlib. We'll also work with CSV files with pandas. You'll also be getting the essential coding practices required to create these types of programs. To make the most out of this class, it is beneficial to have some prior programming experience ( preferably python ) but however, it's not necessary. An up-to-date system running Windows, MacOs or Linux with an installed version of python is recommended ( I  will provi...
Post a Comment
Read more

Recommender System using Python

Recommender systems are everywhere. Amazon recommends buys we like, google recommends searches, Youtube recommends systems and Facebook recommends people. All of these implementations describe Recommender Systems. There are 2 types of Recommender Systems. Collaborative Systems - Systems that recommend information based on what you like and what the others like. Content Based Systems- Based on what you have viewed This is a simple ML based recommender System in python. It uses the LightFM library as the dataset import numpy as np from lightfm import LightFM from fetch_lastfm import fetch_lastfm data = fetch_lastfm() model = LightFM( loss = ' warp ' ) model.fit(data[ ' matrix ' ], epochs = 30 , num_threads = 2 ) # Get recommendationns function def get_recommendations ( model , coo_mtrx , users_ids ): n_items = coo_mtrx.shape[ 1 ] for user in users_ids: # TODO create known positives # Artists the model pred...
4 comments
Read more