2015is084_oshani-oshani-weerakoon

Oshani Weerakoon

Desirous of the business analyst position, with 6 months of training experience as a Business Analyst. Aiming to utilize my strong analytical ability, documentation, communication and leadership skills in the working environment while promoting collaboration within teams in achieving business goals


Email: osw0294@gmail.com  
Phone: 717006784
Course: IS

Interests:Business Analysis, Project Management, Business Intelligence, Human Computer Interaction, Data Science, Data Analysis

Technical Skills: Java SE, Python, R, PHP, Laravel, Nodejs

Project Experience

Chat bot assistant for Shopping App (2018)
Interactive chat bot assistant for online shopping in mobile shopping app
A system for in-store navigation (2018)
Navigating the customer to destination through most feasible route inside the store
MedOnline (2018)
Web based online pharmacy store (Laravel, PHP) where registered patients can upload scanned images of prescriptions from a medical practitioner to the site and get the list of medicines delivered to the door. A mobile application (Ionic) will help track delivery locations for each client
iShop (2017)
A system with a web based platform (CodeIgniter, Bootstrap, PHP) for shop owners and mobile application for customers that function as a Smart Shopping Assistant for finding list of items in a multistoried mall using estimote beacons

Work Experience

Trainee Associate Business Analyst | Zone 24×7 (pvt) Ltd. Nawala, Sri Lanka (August 2018 – February 2019)

Achievements | Awards

• GHC 2019 Scholarship Recipient: Grace Hopper Celebration 2019 for Women in Computing, Orlando, FL
• Facebook Udacity Scholarship Recipient: Secure & Private AI Scholarship Challenge 2019
• Google AdWords Certified 2018: Google Academy of Ads
• Achiever Award: UCSC Appreciation Awards 2017
• 2nd Runner-up: Dialog Gaming Hackathon 2017
• Semi-Finalist: IEEE SS12 Asia 2017 Make-A-Thon, Sri Lanka IEEE Sri Lanka Section

Final Year Project

Towards Prediction of Landslide Susceptibility using Random Forest for Kalutara District, Sri Lanka

• The study is based on predicting landslide susceptibility of 84 landslide occurrence points in Kalutara district, Sri Lanka under 12 landslide conditioning factors. The main objective of this study is to explore the integration of geospatial technologies such as Geographic Information Systems (GIS) with suitable machine learning techniques to get refined and improved models with a better landslide prediction accuracy than statistical analysis.
Related Areas: Data Analytics, Geographic Information Systems (GIS), Machine Learning (Random Forest)
Tools: QGIS, R