Freelance Data Analysts in MaharashtraFreelance Data Analysts in Maharashtra
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Abu Aasif Ansari's avatar
Abu Aasif Ansari
Bhiwandi, India
I build AI agents & internal tools that act on data
14
Followers
I build AI agents & internal tools that act on data
Cover image for Anomaly Review & Action Console (Retool + AI)
Anomaly Review & Action Console (Retool + AI)
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Cover image for Superstore Sales Dashboard — 3-Page
Superstore Sales Dashboard — 3-Page Power BI Report Built an interactive 3-page Power BI dashboard using the Superstore Sales dataset (9,994 orders across USA). Page 1 — Sales Overview: KPI cards, monthly trend, regional breakdown, category performance. Page 2 — Product Performance: Top products, category donut, profit analysis, sales vs profit scatter. Page 3 — Customer & Shipping: Segment breakdown, top 10 customers, monthly growth, ship mode distribution. Tools: Power BI, DAX, Superstore Dataset Theme: Dark professional with interactive year filter.
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Cover image for AI-Powered Data Cleaning Tool Development
AI-Powered Data Cleaning Tool Development
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Cover image for AI-Powered Data Cleaning Tool
AI-Powered Data Cleaning Tool
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Data Analyst(12)
Sunny Mehta's avatar
Sunny Mehta
Pune, India
Data Analyst & BI Dev | Dashboards, SQL, Python & ETL
New to Contra
Data Analyst & BI Dev | Dashboards, SQL, Python & ETL
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Marketplace Profitability & P&L Engine Overview This project is an end-to-end E-commerce Marketplace Analytics solution built using MySQL, SQL, Power BI, and Excel. The objective of this project is to analyze marketplace profitability across multiple sales channels by calculating revenue, product costs, marketplace commissions, shipping expenses, and net profit. The project simulates a real-world marketplace operations environment where business teams need visibility into product performance, marketplace profitability, and operational KPIs. Business Problem E-commerce businesses often track revenue but fail to understand true profitability. This project helps answer key business questions: Which marketplace generates the highest profit? Which products are most profitable? How do commissions impact margins? What is the overall profit margin? How does shipping cost affect profitability? Tech Stack MySQL SQL Power BI Microsoft Excel Data Sources Products Contains SKU-level product information. Fields: SKU Product Name Category Cost Price Selling Price Orders Contains marketplace order transactions. Fields: Order ID Order Date Marketplace SKU Quantity Marketplace Charges Marketplace commission structure. Fields: Marketplace Commission Percentage Shipping Cost Shipping cost by marketplace. Fields: Marketplace Shipping Cost Returns Order return information. Fields: Order ID Return Status SQL Implementation The project uses multiple SQL tables and joins to create a unified profitability dataset. Key SQL Concepts Used: CREATE TABLE INNER JOIN SQL Views Aggregations Calculated Metrics A consolidated analytical view was created: vw_marketplace_pnl This view combines all business logic and serves as the primary source for Power BI reporting. Profitability Metrics Revenue Revenue = Quantity × Selling Price Product Cost Product Cost = Quantity × Cost Price Commission Cost Commission Cost = Revenue × Marketplace Commission % Shipping Cost Shipping Cost = Quantity × Shipping Cost Profit Profit = Revenue − Product Cost − Commission Cost − Shipping Cost Profit Margin % Profit Margin % = Profit / Revenue Power BI Dashboard Executive Summary KPIs: Total Revenue Total Profit Total Orders Profit Margin % Marketplace Analysis Revenue by Marketplace Profit by Marketplace Marketplace Performance Comparison Product Analysis Top Profitable Products Product Revenue Analysis Product Profitability Ranking Trend Analysis Revenue Trend Profit Trend Order Trend Project Architecture Excel → MySQL → SQL View → Power BI Dashboard Key Outcomes Built a scalable marketplace profitability model. Centralized business logic using SQL Views. Automated profitability calculations. Delivered interactive executive dashboards for decision-making. Simulated a real-world marketplace operations analytics workflow. Skills Demonstrated SQL Data Modeling Business Intelligence Power BI E-commerce Analytics Marketplace Operations Profitability Analysis Data Visualization Author Sunny Mehta Open to opportunities in: E-commerce Operations Marketplace Management Business Analytics Operations Analytics Data Analytics
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Netflix Movies Analysis Dashboard using Power BI Project Overview Interactive Power BI dashboard analyzing Netflix movie trends, popularity, ratings, genres, and language distribution. KPIs Total Movies Total Votes Total Popularity Average Rating Dashboard Features Top 10 Movies by Popularity Release Year Trend Movies Distribution by Language Genre Analysis Popularity vs Vote Average Tools Used Power BI Power Query DAX Data Modeling
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Cover image for Banking Data Analysis Dashboard Develop a
Banking Data Analysis Dashboard Develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers Solution – With our dashboards which are created using Power BI latest tools helps the company to make a decision based on the applicant’s profile like if the applicant is likely to repay the loan then approving the loan otherwise not. About Dataset – This dataset basically contains information about bank details ,various client details which consists of multiple tables which are interlinked with each other through keys like primary key and foreign key. The various tables are Banking Relationship, Client-Banking, Gender, Investment Advisor and Period. KPI’S: In which followings KPIS are present : Total Clients : Total Clients KPI represents total number of clients in banking. Total Clients = DISTINCTCOUNT('Clients - Banking'[Client ID] ) Total Loan : Total Loan gives you information about the bank loan + Business lending + credit cards balance of particular investor , gender. Total Loan = [Bank Loan] + [Business Lending] + [Credit Cards Balance] Bank Loan : Bank Loan gives you information what is the loan amount of loan to be repaid by the client to bank. Bank Loan = SUM('Clients - Banking'[Bank Loans] ) Business Lending : Business lending gives you information about the loan amount given to small business. Business Lending = SUM('Clients - Banking'[Business Lending] ) Total Deposit Total Deposit gives you information about the amount deposited by particular investors in bank Total Deposit = [Bank Deposit] + [Savings Account] + [Foreign Currency Account] + [Checking Accounts] Total Fees : Total Fees is nothing but the amount charged by the bank for account set-up , maintenance charges etc. Total Fees = SUMX('Clients - Banking' , [Total Loan] * 'Clients - Banking'[Processing Fees] ) Bank Deposit : Bank deposit is the money put in the bank. Bank Deposit = SUM('Clients - Banking'[Bank Deposits] ) Checking Account Amount : Checking account amount is nothing but which offers easy access to your money for daily transactional needs. Checking Accounts = SUM('Clients - Banking'[Checking Accounts] ) Total CC Amount : Total CC Amount is a short-term source of financing for a company by a bank. Total CC Amount = SUM('Clients - Banking'[Amount of Credit Cards] ) Saving Account Amount : A savings account is an interest-bearing deposit account held at a bank. Savings Account = SUM('Clients - Banking'[Saving Accounts] ) Foreign Currency Amount : Foreign Currency Account means an account held in a currency that is not the currency of India or Bhutan or Nepal. Foreign Currency Account = SUM('Clients - Banking'[Foreign Currency Account] ) Engagement Account : Engagement Banking is nothing but puts the customer at the center and aims to deliver the digital experiences they expect. Engagment Length = SUM('Clients - Banking'[Engagment Days]) Credit Cards Balance : It is the total amount of money currently owned by a cardholder to their credit card bank. Credit Cards Balance = SUM('Clients - Banking'[Credit Card Balance] ) 📊 Tools Used Excel (Data Cleaning) SQL (Data Processing) Python (EDA) Power BI (Dashboard) 📈 Key Insights Private banks dominate loan distribution European segment shows highest deposits Low-income customers have high loan dependency
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Cover image for Blinkit-Sales-Dashboard This project is an interactive
Blinkit-Sales-Dashboard This project is an interactive Power BI dashboard developed to analyse Blinkit's sales performance, outlet operations, product distribution, and customer-related metrics. The dashboard converts raw retail sales data into meaningful business insights through visualisation and KPI tracking. The objective of this project is to monitor business performance, identify sales trends, evaluate outlet efficiency, and support data-driven decision-making. Key KPIs Total Sales: $1.20M Average Sales: $140.99 Number of Items: 8,523 Average Rating: 3.9 Dashboard Features Sales Analysis Tracked total revenue and average sales performance to understand overall business growth. Outlet Performance Analysed outlet sales based on the following: Outlet Size Outlet Location Type Outlet Establishment Year Outlet Type Product Category Analysis Identified top-performing product categories contributing the highest revenue. Customer & Product Insights Compared low-fat and regular products to understand purchasing trends and monitored customer ratings for performance evaluation. Interactive Filters Added slicers for: Outlet Location Type Outlet Size Item Type Outlet Type Outlet Identifier These filters allow users to dynamically explore data and generate business insights. Project Outcome This project demonstrates practical skills in business intelligence, KPI reporting, analytical thinking, and dashboard development using Power BI. It highlights the ability to transform raw business data into business insights for decision-making. Developed By Sunny Mehta
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Data Analyst(4)
Smita S's avatar
Smita S
Pune, India
Senior Data Analyst | Business Intelligence Consultant
10
Followers
Senior Data Analyst | Business Intelligence Consultant
Cover image for Power BI Templates for Self-Service Reporting
Power BI Templates for Self-Service Reporting
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Cover image for Inventory Optimization with ABC-XYZ Segmentation
Inventory Optimization with ABC-XYZ Segmentation
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Cover image for MRR & Churn Analysis Dashboard for SaaS Retention and Growth
MRR & Churn Analysis Dashboard for SaaS Retention and Growth
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Data Analyst(3)
Umar Shaikh's avatar
Umar Shaikh
Pune, India
Data Scientist, Backend Engineer, Data Analyst
10
Followers
Data Scientist, Backend Engineer, Data Analyst
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main_palmer_penguin_EDA_2024-project
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Cover image for main_palmer_penguin_EDA_2024-project
main_palmer_penguin_EDA_2024-project
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Cover image for Fraud_Transaction_Detection-Fraud_Finder_ID12254…
Fraud_Transaction_Detection-Fraud_Finder_ID12254…
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Cover image for International Debt Statistics - PPG Bilateral Debt
International Debt Statistics - PPG Bilateral Debt
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Data Analyst(4)
Sarbjot Singh's avatar
Sarbjot Singh
Pimpri-Chinchwad, India
Power BI Expert | Turning Raw Data into Actionable Insights
New to Contra
Power BI Expert | Turning Raw Data into Actionable Insights
Cover image for Data Analytics Project | BlinkIT
Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz )
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I’m excited to share my latest Data Analytics project where I analyzed Customer Shopping Behavior to uncover trends in sales, demographics, and purchasing habits.This project was a great exercise in building a complete data pipeline. The Tech Stack: Python (Pandas & NumPy): Used in Jupyter Notebooks for initial data exploration and statistical analysis. PostgreSQL: Utilized for rigorous data cleaning, querying, and structuring the dataset for analysis. Power BI: Built an interactive dashboard to visualize key metrics like seasonal trends, subscription impacts, and category performance. Project Highlights: ✅ Data Cleaning: Leveraged PostgreSQL and Pandas to handle missing values and standardize categorical data.
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Cover image for Venezuela’s Oil Reserves Analysis Dashboard
Venezuela’s Oil Reserves Analysis Dashboard 📊 I am excited to share my latest data analytics project focusing on the energy sector. Using a dataset covering 23 reservoirs, I designed a comprehensive dashboard to track production capacity and resource distribution in Venezuela. Key Features: ✅ Real-time Metrics: Tracking the 390.50 Billion total barrels in reserve. ✅ Granular Analysis: Production capacity breakdown by reservoir name and oil grade (Extra Heavy to Light). ✅ Stakeholder Mapping: Visualizing the production sum by major operators. Tools Used: [pandas(jupyter notebook)/mysql(for analysis and cleaning of data), Power BI / Excel(for building dashboard)] I’d love to hear your thoughts or feedback on the dashboard design! must visit my github for full project -( https://lnkd.in/dYKdZ8J2 )
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Cover image for Data Analytics Project | BlinkIT
Data Analytics Project | BlinkIT Grocery Sales Analysis Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈 📊 What I worked on: Analyzed sales performance across product categories, outlet types, sizes, and locations Identified top-performing item categories and customer preferences Studied the impact of outlet size, establishment year, and visibility on sales Converted raw data into actionable insights and business recommendations 💡 Key Insights: Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales Medium-sized outlets outperform others in overall revenue FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz ) Feedback and suggestions are always welcome!
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Data Analyst(4)
Mahin Sherashia's avatar
Mahin Sherashia
Mumbai, India
Your CRO and Paid Ads Growth Partner | Startups & Enterprise
Your CRO and Paid Ads Growth Partner | Startups & Enterprise
Cover image for Conversion Optimization - 28% improvement in premium Signups
Conversion Optimization - 28% improvement in premium Signups
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Certified Google Analytics Expert
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Cover image for 0 to 1,000 signups per week for SaaS client
0 to 1,000 signups per week for SaaS client
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Cover image for 0 to 200 orders per week for an eCommerce brand
0 to 200 orders per week for an eCommerce brand
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Data Analyst(2)
VARSHA ZOKE's avatar
VARSHA ZOKE
Pune, India
MS PowerBI certified DataAnalyst | Get results, improvements
MS PowerBI certified DataAnalyst | Get results, improvements
Cover image for Cricket WorldCup Dashboard
Cricket WorldCup Dashboard
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Cover image for HR Analysis For Attrition
HR Analysis For Attrition
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Cover image for Swiggy Instamart Sales Dashboard
Swiggy Instamart Sales Dashboard
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Data Analyst(3)