Baibhav Ojha
Data Analyst | Python · SQL · Excel · Power BI | Clean data, clear insights
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🛡️ Verifiziert seit October 2026
Über
Hi, I'm Baibhav — a Data Analyst from Kathmandu, Nepal, specializing in Python, SQL, Excel, and Power BI.
I help businesses and individuals turn messy, chaotic data into clean datasets, useful queries, and clear visualizations that actually support decisions.
My approach is simple: understand the problem first, clean the data, analyze it, and present findings in a way non-technical people can understand. I don't just run numbers — I try to answer the "so what?" question behind every dataset.
Wobei ich Ihnen helfen kann:
• Data Cleaning & Preparation — Fix messy Excel and Google Sheets files: duplicates, broken formats, missing values, and inconsistent entries. I make your data usable before analysis even begins.
• SQL Query Writing — Write custom queries to extract, filter, join, and aggregate data from your database. Whether it's SQLite, MySQL, or PostgreSQL, I can get the data you need in the format you need it.
• Power BI Dashboards — Turn raw numbers into clean, interactive dashboards with KPIs, charts, and slicers. Designed so non-technical clients can explore findings without any specialist knowledge.
• Python Data Analysis — Use pandas, NumPy, matplotlib, and seaborn to explore datasets, find patterns, and produce custom visualizations. Great for one-off analyses or automated reports.
Recent work:
I analyzed global healthcare data spanning 200+ countries — designing a relational SQLite database, writing 50+ SQL queries, and producing 7 visualizations to surface key patterns. Key finding: Nepal has roughly 5.7 times fewer hospital beds per person than the world average.
I also combined CO₂ emissions data with disaster records to tell a climate story about Nepal — showing how Nepal emits far less than high-emitting nations but suffers disproportionately.
Before focusing on data analysis, I managed ticketing operations for a 10,000+ attendee event in Kathmandu, achieving full reconciliation — practical experience handling large, time-sensitive datasets under pressure.
Typical turnaround times:
• Data cleaning (up to ~5,000 rows) — 1–2 days
• SQL queries (up to 10 queries) — 1 day
• Power BI dashboard (1–2 pages) — 3–5 days
• Python analysis (EDA, cleaning, or custom charts) — 2–4 days
• Data entry — volume-based; discussed before starting
Revisions: Two free revisions are included on every project, covering adjustments to the agreed deliverable scope. New requirements or expanded scope are treated as separate work and quoted accordingly.
Accepted data sources: CSV, Excel (.xlsx/.xls), Google Sheets exports, SQLite and MySQL database files, and JSON.
Experience: I have been building my Python and SQL skills since early 2026 through self-study, portfolio projects, and hands-on event data management. I'm early in my freelance career, which is reflected in my pricing.
I communicate clearly, ask questions early when a brief is unclear, and aim to respond promptly. Small tasks are welcome — I'd rather earn trust through a well-executed small job than take on large contracts before I've demonstrated what I can do.
If you have a data problem — messy spreadsheets, a report that hasn't been built yet, or data sitting in a database nobody's querying — send me a message. Let's talk about what you need and whether I can help.
Fähigkeiten
Technologien und Fachkenntnisse, auf die sich dieser Freelancer spezialisiert hat.
Kategorien
Servicebereiche, für die dieser Freiberufler verfügbar ist.
Portfolio
Nepal Health Analysis — Global Dataset (200+ Countries), 50+ SQL Queries
End-to-end data project analyzing healthcare access across 200+ countries. Built a 3-table SQLite database with ~25,000 rows from World Bank data, wrote 50+ analytical SQL queries (joins, aggregations, subqueries), and produced 7 visualizations. Key finding: Nepal has 5.7x fewer hospital beds than the world average.
Climate Justice Analysis — Python
Combined CO₂ emissions data with Nepal disaster records to show how Nepal emits 8x less than high-emitting nations but suffers disproportionately from climate disasters. Performed multi-dataset joins, trend analysis, and data storytelling in Python.
Global CO₂ Analysis — Exploratory Data Analysis
Exploratory analysis of 50,000+ rows of global CO₂ emissions data from Our World in Data. Analyzed top emitters, Nepal's trend, peak year, and CO₂ vs GDP correlation using 10+ chart types in Python (pandas, matplotlib, seaborn).
Runway Career Connect 2026 — Event Data Management (10,000+ Attendees)
Managed ticketing operations and participant data for a 10,000+ attendee education and concert event in Kathmandu. Responsibilities: • Led a team across multiple ticket booths and partner colleges • Built and maintained participant databases with full reconciliation • Handled real-time issues during peak entry hours • Coordinated with registration and organizing committees This was my first hands-on experience managing large, time-sensitive datasets — and it directly led me toward data analytics as a career.
