Welcome to my portfolio of data analytics capstone projects! This repository contains hands-on projects that demonstrate my ability to collect, process, store, analyze, and visualize data using modern tools such as Python, SQL, NoSQL, Google Cloud Platform, Tableau, and Looker.
These projects are part of my journey as an aspiring Data Analyst, and they highlight how I apply technical skills to solve real-world business problems.
Focus: Data collection, processing, storage, and reporting.
Tools: Google BigQuery, SQL.
Key Skills: Data cleaning, deduplication, joins, aggregations, financial performance reporting.
Focus: Data analysis and activation through business intelligence dashboards.
Tools: Google Looker Studio (Enterprise), SQL, LookML.
Key Skills: Data storytelling, interactive dashboards, cross-filtering, automated reporting.
Focus: Visualization and dashboard publishing.
Tools: Tableau Online, CSV datasets.
Key Skills: Data import, worksheet creation, dashboard design, KPI tracking.
Programming & Databases: Python, SQL (joins, subqueries, aggregations), NoSQL (MongoDB, Firestore).
Cloud Tools: BigQuery (Data Warehouse), Dataproc (ETL/batch processing), Cloud Storage (Data Lake), Data Lakehouse concepts.
Visualization Tools: Tableau, Looker Studio, Google Sheets Dashboards, Matplotlib, Seaborn.
Analytics: Data cleaning, preprocessing, KPI development, exploratory data analysis (EDA).
Collaboration Tools: GitHub, Jupyter/Colab, Google Workspace.
Scenario: TheLook Fintech, a loan provider for online businesses, wanted to monitor financial performance and manage risk. As a Cloud Data Analyst, I was tasked with addressing Treasury’s business questions:
How to monitor cash flow effectively?
What are the top reasons for loan applications?
How to track loan distribution by region?
Set up the BigQuery environment and imported loan data.
Explored datasets and applied SQL queries to filter, deduplicate, and aggregate records.
Joined loan data with a state classification file to enrich geographic insights.
Built reports to:
Compare loan disbursements vs. repayments (cash flow monitoring).
Identify top loan purposes (deduplicated).
Track borrower distribution by state.
Aggregate daily and yearly loan totals.
Proficiency in BigQuery SQL (joins, deduplication, aggregations).
Understanding of data warehouse workflows (collect → process → store).
Translating raw data into actionable financial insights.
Preview:
After analyzing the data with BigQuery, the Treasury team wanted a dashboard to quickly assess loan health.
Steps Taken:
Connected data to Looker (Google Cloud BI tool).
Created visualizations to answer key questions:
Total outstanding loan balances.
Loan status distribution (current, late, default, paid-off).
Top 10 states with the highest outstanding loans.
Customers who own homes outright with active loans.
Applied LookML and SQL queries to shape datasets.
Designed an interactive dashboard with:
Cross-filtering for dynamic insights.
Automated refresh scheduling for near real-time updates.
Experience with enterprise BI dashboards in Looker.
Hands-on practice with SQL + LookML integration.
Data storytelling and stakeholder-focused design.
Enabling compliance tracking and risk analysis via dashboards.
Preview:
Created a simple sales analysis dashboard to visualize customer loyalty program data.
Uploaded CustomerLoyaltyProgram.csv to Tableau Online.
Built worksheets for:
Product Line Performance by Year (line plot).
Quantity Sold (aggregated, formatted as currency in thousands).
Revenue Trends (total revenue, custom formatting).
Combined sheets into a single Product Sales Dashboard.
Published and exported the dashboard to be shared in multiple formats (PDF, PowerPoint, image).
Proficiency in Tableau Online workflows (upload, worksheets, dashboards).
Designing clean, readable KPIs for business reporting.
Formatting and publishing dashboards for stakeholder use.
Preview:
Through these projects, I strengthened my ability to:
Work across the full data journey (collect → process → store → analyze → activate).
Use Google Cloud tools (BigQuery, Looker, Dataproc, Storage) to solve real business problems.
Design dashboards in Tableau, Looker, and Google Sheets to communicate insights.
Apply SQL, Python, and data visualization to create business value.
Develop a portfolio of projects showcasing end-to-end data analytics skills.