DeepLearning.AI & Amazon Web Services Data Engineering Capstone Project (June 2025)

• Technical Skills: Requirements Analysis, Data Pipelines, Real Time Data, Data Modeling, Star Schema, DBT, Amazon Web Services, Amazon S3, Terraform, Amazon CloudWatch, CI/CD, Infrastructure as Code (IaC), AWS Glue, AWS Kinesis, Apache Iceberg, Amazon Redshift, Performance Tuning, Apache Superset, Apache Airflow

• Description: The project implements an end-to-end data engineering pipeline on AWS Cloud, orchestrated using Apache Airflow for a company that offers subscription-based music streaming services. The company added a new feature allowing its clients to purchase and download music. The task is to build the data pipeline for the data analysts to analyse the purchase-data. Data is extracted from an API endpoint and an RDS database, then stored in S3 in the Landing Zone. The raw data is transformed using Apache Iceberg within the Transformation Zone and catalogued with AWS Glue. Redshift Spectrum is used for querying data, and quality checks are applied to ensure accuracy. The transformed data is loaded into Amazon Redshift for analytics. DBT is used for modeling and Apache Superset provides interactive dashboards for visualization.

Google Advanced Data Analytics Capstone Project (November 2023)

• Technical Skills: Python (numpy, Pandas, Scipy, seaborn, Matplotlib, statsmodels, scikit-learn), Machine Learning Models: regression (linear, logistic), Naive Bayes, decision trees, random forest, AdaBoost, XGBoost

• Description: Used multiple regression to predict taxi fares, data that would be used as part of a suite of models to optimize revenue for the New York Taxi and Limousine Commission and its drivers

Google Data Analytics Capstone Project (October 2023)

• Technical Skills: Data analysis, data cleaning, data visualization, R programming, data-driven decision making, calculation and analysis in SQL and spreadsheets, effective presentations

• Description: The project was for a bike-share company in Chicago that intended to maximize the number of annual memberships. From the data, it was figured out how casual riders and annual members use bikes differently. From these insights, a marketing strategy was designed to convert casual riders into annual members.