Priya Dey — Data Analyst Portfolio

01About

I'm an aspiring Data Analyst with a background in Geography (B.Sc.), graduating in 2024. After graduation, I spent a year preparing for competitive government exams (SSC CGL, SSC MTS) — a phase that built the discipline and structured problem-solving I now bring to data work. In July 2025 I made a deliberate pivot into data analytics, completing a Data Analyst Bootcamp and diving into SQL, Python, and Power BI.

Since then I've completed two remote internships and seven independent projects spanning SQL analysis, Power BI dashboarding, and Python-based machine learning — across healthcare, retail, e-commerce, and beauty datasets. I care about accuracy over impressive-looking charts — if a number can't survive a stakeholder asking "how did you get this," it doesn't go on the dashboard.

03Projects

CASE_01

Colorectal Cancer Analysis Dashboard

Tools: Python, EDA, Power BI, DAX, Power Query

Problem: Raw healthcare data needed to be turned into a usable view of risk factors and survival trends for non-technical stakeholders.

Approach: Cleaned and preprocessed 90,000+ patient records with Pandas — handling missing values, duplicates, and inconsistencies — then ran EDA in Python to surface patterns before building the dashboard.

Impact: Delivered a 3-page interactive Power BI dashboard uncovering risk factors and survival trends across age, gender, and region.

GitHub repo →

CASE_02

E-Commerce Sales Analysis Dashboard

Tools: SQL, Excel, Power BI, DAX, Power Query

Problem: Sales data needed to be validated and structured before it could be trusted for reporting on trends and revenue drivers.

Approach: Cleaned, validated, and prepared 10,000+ sales records using Excel and SQL, then analyzed the data to identify patterns before visualizing it.

Impact: Built interactive Power BI dashboards that surfaced sales trends, customer behavior, and key revenue drivers.

GitHub repo →

CASE_03

Amazon Sales Dashboard

Tools: Power BI, DAX

Problem: Needed a clear view of how Amazon product categories perform on sales, pricing, discounts, and customer ratings.

Approach: Built an interactive Power BI dashboard with bar, column, and scatter visuals plus slicers, comparing discounted vs. actual price, discount percentage, and average ratings across categories.

Impact: Surfaced top revenue-generating categories, highest-discount categories, and pricing gaps — insights useful for catalog and pricing strategy.

GitHub repo →

CASE_04

Beauty Products Sales Dashboard

Tools: Power BI, DAX, Power Query

Problem: A beauty brand needed visibility into how sales, profit, and stock varied by season, brand, and product.

Approach: Designed a 3-page Power BI dashboard — Season-wise, Brand Performance, and Product Performance — tracking total sales, profit rank, quantity, and stock levels.

Impact: Made it easy to spot top-performing brands and products by profitability and season, supporting smarter inventory and stocking decisions.

GitHub repo →

CASE_05

Retail Sales Analysis using SQL

Tools: SQL Server, SSMS

Problem: Retail transaction data needed to be broken down by product category and customer demographics to guide business and marketing decisions.

Approach: Wrote SQL queries using GROUP BY, aggregate functions, and CASE WHEN age-bucketing to analyze revenue by category, gender, age group, and month.

Impact: Found Electronics led category revenue (₹156,905), female customers slightly outspent male customers, and the 34–55 age group was the highest-spending segment — insights turned into concrete marketing recommendations.

GitHub repo →

CASE_06

Chocolate Sales Analysis using SQL

Tools: SQL Server (SSMS)

Problem: Needed to turn raw chocolate sales transactions into a clear picture of sales performance across countries, products, and time.

Approach: Used SQL aggregate functions, GROUP BY, and date functions to calculate total revenue and break it down by country, product, and month.

Impact: Identified Australia as the top revenue-generating country and "50% Dark Bites" as the best-selling product out of ₹6.18M in total revenue, and pinpointed January and June as peak sales months.

GitHub repo →

CASE_07

Diabetes Risk Prediction

Tools: Python, Pandas, Scikit-learn, Logistic Regression

Problem: Needed to predict diabetes risk from patient demographic, medical, and lifestyle data.

Approach: Ran EDA and data cleaning on the dataset, encoded categorical variables, and trained a Logistic Regression classification model to predict diabetes risk.

Impact: Delivered an end-to-end ML workflow — from raw data to a trained, evaluated model — extending my analytics skill set beyond dashboards into predictive modeling.

GitHub repo →

04Experience

Data Analyst Intern

Zaalima Development Pvt. Ltd — Remote

May 2026 — Present

Performing data cleaning and preprocessing using SQL and Python (missing values, duplicates, inconsistencies), supporting data validation and quality checks, and developing interactive Power BI dashboards for business reporting.

Data Analyst Intern

SkillFied Mentor — Remote

Feb 2026 — May 2026

Performed EDA on 5 real-world datasets across Banking, Healthcare, and Automotive domains using Python; cleaned data with Pandas and NumPy; conducted statistical analysis to generate actionable business insights.

Data Analyst Bootcamp: Basics to Advanced

Udemy — Career transition

Jul 2025 — Jan 2026

Pivoted into data analytics after a year preparing for competitive government exams, and built foundational skills in SQL, Python, Power BI, and EDA from the ground up.

Competitive Exam Preparation

SSC CGL & SSC MTS

Aug 2024 — Jun 2025

Prepared for and appeared in national-level competitive government exams, sharpening quantitative aptitude, analytical reasoning, and disciplined, structured problem-solving.

B.Sc. in Geography

West Bengal State University

Nov 2021 — Aug 2024 · CGPA 8.23

Graduated with a strong analytical foundation, later applied to data-driven problem solving.

Turning raw data into
decisions people act on.

Let's work together