Welcome to the MSc Data Analytics GitHub repository, dedicated exclusively to showcasing the projects, code, and materials I have used and developed during my Master of Science in Data Analytics at CCT College Dublin. The program commenced in September 2023 and concluded in October 2024. This repository highlights my academic journey, the technical skills gained, and the applied research undertaken throughout the program.
The MSc in Data Analytics at CCT College Dublin is a structured program designed to equip graduates with advanced tools, techniques, and methodologies to analyze, interpret, and visualize data effectively. The program spans three semesters:
- Semester 1: Focused on foundational subjects to build core competencies in machine learning, data preparation, statistics, and programming.
- Semester 2: Advanced subjects explored big data storage, processing, and data analysis, along with research and professional ethics.
- Semester 3: Dedicated entirely to a capstone project titled "Federated Learning: Evaluating Popular Frameworks and Developing a Cross-Client Horizontal Server." This project focused on bridging the gap between academic Federated Learning (FL) frameworks and real-world FL systems. The research involved building a horizontal FL server for binary classification and image classification tasks, demonstrating improvements in accuracy for non-IID data scenarios while highlighting challenges such as communication issues and infrastructure limitations.
| Module | Credits | Grade (%) | Result |
|---|---|---|---|
| Semester 1 | |||
| Machine Learning for Data Analytics | 10 | 68 | Pass |
| Data Preparation & Visualisation | 10 | 64 | Pass |
| Statistics for Data Analysis | 10 | 67 | Pass |
| Programming for Data Analytics | 5 | 63 | Pass |
| Semester 2 | |||
| Research & Professional Ethics | 5 | 57 | Pass |
| Advanced Data Analysis | 10 | 75 | Pass |
| Big Data Storage & Processing | 10 | 78 | Pass |
| Semester 3 | |||
| Data Analytics Project | 30 | 50 | Pass |
- Cohort: PG24480 - MSc in Data Analytics (L9)
- Completion: October 2024
- Award Classification: Second-class Honours
- Percentage Point Average (PPA): 62.44
This repository includes code and documentation from the following projects:
- Machine Learning for Data Analytics: Implementation of Random Forest, K-Nearest Neighbors, and Neural Networks on various datasets.
- Data Preparation & Visualisation: Data cleaning and exploratory data analysis (EDA) using Python libraries such as
pandasandseaborn. - Big Data Storage & Processing: Projects on distributed data processing using Hadoop, Spark, and MapReduce.
- Capstone Project: A research-driven exploration of Federated Learning that experiments with popular Federated Learning frameworks and introduces a new approach to narrow the gap between these frameworks and real-world Federated Learning scenarios.
- Programming: Python, R, SQL
- Machine Learning Techniques: Decision Trees, Neural Networks, CNNs
- Big Data Ecosystem: Hadoop, Spark, MongoDB, Hive
- Statistics & Analysis: ANOVA, Hypothesis Testing, Chi-Square
- Visualization: matplotlib, seaborn, Tableau
The last day of this beautiful journey was the graduation ceremony held on the 23rd of November 2024 at the Convention Centre Dublin (CCD) in Dublin, Ireland. This event marked the culmination of hard work, dedication, and countless learning experiences throughout my MSc program. I will never forget the Class of 2024!!!
After the graduation, super happy with the parchment in hand 😁!
On the left is my sister Lorena, and on the right is my love Ana. Thank you both for your support during my MSc; this would have been impossible without you ❤️❤️!!
Class of 2024 🎓
Arriving early in the morning for the ceremony!!
This GitHub account serves as an archive for the projects, code, and materials created during my MSc in Data Analytics at CCT College Dublin.
Please note that this account is no longer actively updated.
To explore my ongoing projects and stay up-to-date with my work, visit my personal GitHub account: @josericodata.
Feel free to explore my work and connect:
