Devashish Sharma

Motivated Computer Science graduate with a Master's in Advanced Computer Science from Oxford Brookes University and hands-on experience in cloud computing, programming, and data engineering. Skilled in AWS, GCP, Azure, Python, Django, HTML, CSS, PHP, SQL and various cloud and web technologies, with a good foundation in data engineering and machine learning. Seeking an entry-level position in cloud computing or software development to leverage my technical skills and contribute to innovative projects.

• Verizon - Cloud Platform Job Simulation, 04/2024, 05/2024
• Cybersecurity Virtual Experience Program, 03/2022, 03/2022
• Solutions Architect Virtual Experience Program, 03/2022, 03/2022
• Virtual training Program, 12/2021, 12/2021
• Virtual Experience Program Participant, 07/2021, 08/2021
• Develop a Company Website with Wix, 07/2021, 08/2021

SCALABILITY AND PERFORMANCE OPTIMIZATION IN CLOUD-BASED E-COMMERCE AND M-COMMERCE APPLICATIONS

06/2023 - 10/2023

Oxford Brookes University (Dissertation)

• Investigated strategies for enhancing scalability and performance in cloud-based e-commerce and m-commerce applications through a combination of literature review and prototype development.
• Developed an e-commerce application using Python and Django framework, deployed as a microservices architecture on AWS Elastic Beanstalk.
• Integrated a rule-based Dynamic Resource Allocation (DRA) logic into the application backend for real-time resource allocation decisions.
• Addressed professional and ethical considerations related to cloud computing and e-commerce practices.
• Presented findings and recommendations for future work in the field of cloud-based application optimization.

Predicting UEFA Champions League 2023 Outcomes: A Data Science and Machine Learning Approach

02/2023 - 05/2023

Oxford Brookes University (Academic)

• Conducted analysis on UEFA Champions League match outcome prediction from 2016-2023
• Utilized regression and classification algorithms for forecasting
• Three tests conducted with varying dataset sizes and features
• Achieved high accuracy, precision, and F1 scores in the second test:
• Random Forest Classifier: Accuracy 76.47%, Precision 77%, F1-score 75%
• Support Vector Mechanism: Accuracy 76.20%, Precision 77%, F1-score 75%
• Challenges identified include overfitting and inadequate feature selection
• Addressed legal, ethical, and security implications of predictive models.

Sentiment Analyzer

12/2021 - 01/2022

SmartKnower

Utilized natural language processing (NLP) and text analytics to analyze sentiment in texts, demonstrating proficiency in emerging technologies like machine learning.

Facial Recognition for Mood Detection

12/2021 - 01/2022

SmartKnower

Leveraged Microsoft Face API to detect mood using facial recognition techniques, showcasing the practical application of cloud computing in innovative projects.

Website Solution for a Sweet/Snack Shop Called “Dada Halwai”

04/2021 - 06/2021

MES Garware College of Commerce (Academic project)

• Front End: HTML, CSS, BOOTSTRAP
• Back End: PHP
• Database: MySQL
• Created an e-commerce website to enhance the shop's online presence and sales.

Inventory Management System for Bags Manufacturing Factory

02/2020 - 04/2020

MES Garware College of Commerce (Academic project)

• Front End: VB6
• Back End: MS ACCESS 2003 onwards
• Developed a comprehensive inventory management system to streamline the factory's operations.

• Cooking
• Cricket
• Fitness
• Football
• Gaming
• Technology Enthusiast
• Travel