This repository contains all the lab sessions completed as part of the Cloud Computing and Big Data Analytics course at FIB (Universitat Politècnica de Catalunya). Each lab session focuses on different aspects of cloud computing, ranging from basic cloud setup to advanced analytics and cloud infrastructure programming.
The assignments for each lab can be found here: https://github.com/CCBDA-UPC/Assignments-2024
This session introduces the basic knowledge required for the subsequent lab sessions. It covers essential tools and concepts necessary for cloud computing tasks.
In this session, we explored the structure of a tweet and the challenges of preprocessing text data, particularly from Twitter. We also set up a Python Development Environment, which will be essential for future lab sessions.
This session involved completing specific labs from the "AWS Academy Cloud Foundations" course. The tasks included taking screenshots of major milestones and reflecting on what was learned or observed.
Continuing from Lab Session #3, we completed additional labs from the "AWS Academy Cloud Foundations" course. Like the previous session, this involved capturing screenshots of key steps and writing reflections on the experiences.
In this session, we followed the AWS Elastic Beanstalk tutorial to deploy a web application using Django. This exercise provided hands-on experience with deploying and managing applications in the cloud.
We used the Scrapy framework to scrape data from a webpage and then analyzed the data using the Elastic Stack (Elasticsearch, Kibana, Beats, and Logstash). This session highlighted the integration of data extraction and analysis tools.
This session focused on Google Cloud's Vision API, exploring its capabilities in image classification and analysis. We used the API to detect text within images and classify them into various categories, showcasing the power of advanced analytics in the cloud.
This session involved programming and automating cloud infrastructure setup, providing practical experience in managing cloud resources programmatically.
The final project involved creating a service for verifying student identities for discounts and other student benefits. This project tied together various skills learned throughout the course, focusing on practical application in a real-world scenario.
This tutorial focuses on federated learning using Flower and Pandas. Federated learning is a machine learning technique that allows models to be trained in a distributed manner across multiple devices or local servers, without gathering or transferring raw data to a central server. Instead, models are sent to local devices where they are trained on local data, and model updates are aggregated to form a global model.
For each lab session, screenshots of key milestones and detailed reflections on what was learned are provided. These can be found in the respective lab session folders.