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사용자 아바타

Aditya D.

@adityadarji1828

monthly-level-three.svgverified-v2.svgpreferred-freelancer-v3.svg
5.0
54
6.1
6.1
98%

Data Analyst | Dashboard | Python | Statistics

$50 USD / 시간
・
국기 ()
India (9:54 AM)
・
3월 14, 2021에 가입
$50 USD / 시간
・
I'm Aditya, with over 4 years of experience in data analytics and Power BI. I've helped more than 30 businesses leverage data to make better decisions and enhance their operations. My versatile skill set includes: - Advanced Excel: Crafting advanced formulas, automating tasks, and optimizing data workflows. - Python and SQL: Proficient in data manipulation, analysis, and database management. - Power BI/Tableau/Looker: Developing user-friendly, data-rich dashboards for visualizing complex information. - Data Visualization: Presenting data in a clear and visually appealing manner to facilitate decision-making. - AI and Machine Learning: Leveraging artificial intelligence and machine learning techniques to drive predictive analytics and enhance decision-making. - Natural Language Processing (NLP): Analyzing and processing human language data for sentiment analysis, text classification, and more. - Big Data Analysis: Handling and analyzing large datasets to uncover valuable insights and trends. Get in touch with me today to discuss your project needs.
인증
제시간에
100%
예산 금액 내에
100%
금액 수락
100%
재채용률
22%
Freelancer Statisticians India
Statisticians 분야의 전체 프리랜서 확인 (대상 국가: India)

포트폴리오

포트폴리오

12386421
This dashboard tracks the onboarding of farmers, their landholdings, and crop data across states and districts. It provides insights for strategic planning and agricultural management. Steps to Build the Dashboard: Data Collection: Compiled data on farmers, landholdings, crop types, and geographic locations. Data Cleaning: Standardized the dataset, removed duplicates, and resolved inconsistencies. Data Modeling: Established relationships between state, district, and crop data to ensure seamless filtering. Visualization Design: Key metrics like Total Farmers Onboarded, Landholding in Acres, and Average Landholding displayed as KPIs. Bar charts for district-wise and crop-wise farmer statistics. Map visualization for onboarding distribution by location. Filters: Added filters for state, district, and crop type to enhance interactivity. Testing and Validation: Ensured data accuracy and visual clarity. This dashboard helps stakeholders analyze farmer onboarding trends and allocate resourc
Customer Onboarding Dashboard
12386399
Purpose: Provides an overview of sales, inventory, and vendor performance for decision-makers. Sales Dashboard Description Purpose: Provides an overview of sales, inventory, and vendor performance for decision-makers. KPIs: Displays key metrics such as total sales, quantity sold, excise tax, inventory, vendors, and profit after tax (PAT). Filters: Dynamic filtering by vendor, inventory category, and date range. Visuals: Top Vendors: Bar chart showing the top 5 vendors by sales. Top Stores: Bar chart of the top 20 stores by sales. Top Brands: Bar chart ranking brands by total sales. Sales Trends: Line chart showing monthly sales with a forecast. Steps to Build Import Data: Load sales, inventory, and vendor datasets into Power BI. Set Up Metrics: Create measures for KPIs like total sales, PAT, and excise tax using DAX. Enable Filters: Add slicers for vendor, inventory, and date range. Design Visuals: Bar charts for vendors, stores, and brands. Line chart for sales trends with forecast f
Sales Dashboard
12386435
This dashboard provides a clear snapshot of marketing performance, including costs, sales, and profitability. Key Features: KPIs: Shows Newspaper, Radio, TV Costs, Sales Volume, Total Sales, and Net Profit. Sales Distribution: Highlights sales spread across campaigns. Spending by Channel: Visualizes spending on TV, Newspaper, and Radio. Key Influencers: Identifies factors like TV and Radio spend driving sales. Scatter Plot: Displays the relationship between TV spending and sales. How It Was Built: Data Import: Loaded data from source files. Data Cleaning: Prepared and formatted the data. Metrics: Calculated costs, sales, and profit using simple formulas. Visuals: Created clear charts and KPIs for easy insights. Design: Arranged visuals for quick understanding. Perfect for monitoring campaign performance at a glance!
Marketing Dashboard
12386414
This dashboard provides insights into key operational metrics, including riders, services, payments, and parts usage. It is designed for easy tracking and decision-making. Steps to Build the Dashboard: Data Collection: Gathered data on riders, services, payments, parts changed, and financials from operational systems. Data Cleaning: Ensured the dataset was consistent and accurate, resolving missing or duplicate entries. Data Modeling: Created relationships between datasets in Power BI for seamless interaction. Visualization Design: Added KPIs (e.g., Total Riders, Avg KMs Driven, Amount Collected). Designed bar charts for aggregators and service types. Incorporated a donut chart for payment modes. Created a line graph to track daily collection trends. Interactivity: Added filters for service types, technicians, chassis numbers, and date range for user-driven insights. Testing: Validated calculations and ensured all visuals reflected accurate data. This dashboard helps businesses moni
Operations Dashboard
12386403
Purpose: Tracks project package progress, testing status, and completion rates for better project management. Steps to Build Import Data: Load project data, including package details, testing status, and client information. Calculate KPIs: Use DAX for metrics like packages completed, remaining, and readiness percentages. Add Filters: Enable filtering by client, priority, work type, and date range. Design Visuals: Gauge chart for goal tracking. Pie chart for completion by test type. Bar charts for client-wise and work-type breakdowns. Test & Publish: Ensure all filters and visuals interact seamlessly and are easy to interpret for stakeholders.
Project Progress Dashboard
12386426
This dashboard monitors onboarding and deboarding trends, stock distribution across cities, and payment collections by type, enabling operational efficiency and strategic planning. Steps to Build the Dashboard: Data Gathering: Collected data on onboarding, deboarding, city-wise stock, and payment collection. Data Cleaning: Ensured consistent formats, handled missing values, and standardized columns. Data Modeling: Established relationships between aggregators, cities, and timestamps for dynamic filtering. Visualization Design: KPIs (e.g., Total Onboarded, Total Deboarded) for overall and aggregator-specific metrics. Line chart visualizing monthly onboarding vs. deboarding trends. Bar charts showing total stock vs. hub stock by city and payment collections by type. Interactive Filters: Added slicers for aggregator, city, and timestamp to tailor the analysis. Testing and Validation: Verified accuracy of metrics and functionality of filters. This dashboard offers a comprehensive view of
Onboard/Deboard Dashboard
12386421
This dashboard tracks the onboarding of farmers, their landholdings, and crop data across states and districts. It provides insights for strategic planning and agricultural management. Steps to Build the Dashboard: Data Collection: Compiled data on farmers, landholdings, crop types, and geographic locations. Data Cleaning: Standardized the dataset, removed duplicates, and resolved inconsistencies. Data Modeling: Established relationships between state, district, and crop data to ensure seamless filtering. Visualization Design: Key metrics like Total Farmers Onboarded, Landholding in Acres, and Average Landholding displayed as KPIs. Bar charts for district-wise and crop-wise farmer statistics. Map visualization for onboarding distribution by location. Filters: Added filters for state, district, and crop type to enhance interactivity. Testing and Validation: Ensured data accuracy and visual clarity. This dashboard helps stakeholders analyze farmer onboarding trends and allocate resourc
Customer Onboarding Dashboard
12386399
Purpose: Provides an overview of sales, inventory, and vendor performance for decision-makers. Sales Dashboard Description Purpose: Provides an overview of sales, inventory, and vendor performance for decision-makers. KPIs: Displays key metrics such as total sales, quantity sold, excise tax, inventory, vendors, and profit after tax (PAT). Filters: Dynamic filtering by vendor, inventory category, and date range. Visuals: Top Vendors: Bar chart showing the top 5 vendors by sales. Top Stores: Bar chart of the top 20 stores by sales. Top Brands: Bar chart ranking brands by total sales. Sales Trends: Line chart showing monthly sales with a forecast. Steps to Build Import Data: Load sales, inventory, and vendor datasets into Power BI. Set Up Metrics: Create measures for KPIs like total sales, PAT, and excise tax using DAX. Enable Filters: Add slicers for vendor, inventory, and date range. Design Visuals: Bar charts for vendors, stores, and brands. Line chart for sales trends with forecast f
Sales Dashboard
12386435
This dashboard provides a clear snapshot of marketing performance, including costs, sales, and profitability. Key Features: KPIs: Shows Newspaper, Radio, TV Costs, Sales Volume, Total Sales, and Net Profit. Sales Distribution: Highlights sales spread across campaigns. Spending by Channel: Visualizes spending on TV, Newspaper, and Radio. Key Influencers: Identifies factors like TV and Radio spend driving sales. Scatter Plot: Displays the relationship between TV spending and sales. How It Was Built: Data Import: Loaded data from source files. Data Cleaning: Prepared and formatted the data. Metrics: Calculated costs, sales, and profit using simple formulas. Visuals: Created clear charts and KPIs for easy insights. Design: Arranged visuals for quick understanding. Perfect for monitoring campaign performance at a glance!
Marketing Dashboard
12386414
This dashboard provides insights into key operational metrics, including riders, services, payments, and parts usage. It is designed for easy tracking and decision-making. Steps to Build the Dashboard: Data Collection: Gathered data on riders, services, payments, parts changed, and financials from operational systems. Data Cleaning: Ensured the dataset was consistent and accurate, resolving missing or duplicate entries. Data Modeling: Created relationships between datasets in Power BI for seamless interaction. Visualization Design: Added KPIs (e.g., Total Riders, Avg KMs Driven, Amount Collected). Designed bar charts for aggregators and service types. Incorporated a donut chart for payment modes. Created a line graph to track daily collection trends. Interactivity: Added filters for service types, technicians, chassis numbers, and date range for user-driven insights. Testing: Validated calculations and ensured all visuals reflected accurate data. This dashboard helps businesses moni
Operations Dashboard
12386403
Purpose: Tracks project package progress, testing status, and completion rates for better project management. Steps to Build Import Data: Load project data, including package details, testing status, and client information. Calculate KPIs: Use DAX for metrics like packages completed, remaining, and readiness percentages. Add Filters: Enable filtering by client, priority, work type, and date range. Design Visuals: Gauge chart for goal tracking. Pie chart for completion by test type. Bar charts for client-wise and work-type breakdowns. Test & Publish: Ensure all filters and visuals interact seamlessly and are easy to interpret for stakeholders.
Project Progress Dashboard
12386426
This dashboard monitors onboarding and deboarding trends, stock distribution across cities, and payment collections by type, enabling operational efficiency and strategic planning. Steps to Build the Dashboard: Data Gathering: Collected data on onboarding, deboarding, city-wise stock, and payment collection. Data Cleaning: Ensured consistent formats, handled missing values, and standardized columns. Data Modeling: Established relationships between aggregators, cities, and timestamps for dynamic filtering. Visualization Design: KPIs (e.g., Total Onboarded, Total Deboarded) for overall and aggregator-specific metrics. Line chart visualizing monthly onboarding vs. deboarding trends. Bar charts showing total stock vs. hub stock by city and payment collections by type. Interactive Filters: Added slicers for aggregator, city, and timestamp to tailor the analysis. Testing and Validation: Verified accuracy of metrics and functionality of filters. This dashboard offers a comprehensive view of
Onboard/Deboard Dashboard
변경 내역 저장 완료
5.0 · 54 Reviews
리뷰
사용자 아바타
Power BI Dashboard for Stock Analysis • 가격 미공개
Aditya did an exceptional job building a Power BI dashboard for our company. I would highly recommend Aditya for any Power BI projects – he is truly a skilled and reliable professional!
Saad B.
@saadbenyattou
•
국기 ()
khobar, Saudi Arabia
•
16일 전
공유
L
Power BI Developer to Create Interactive Report • 가격 미공개
Perfect communication and timely quality work. Thank you Aditya!
Laurent G.
@Laugro1968
•
국기 ()
Wilrijk, Belgium
•
18일 전
공유
T
Interactive Power BI Report for Website • 가격 미공개
Very happy with the dashboard Aditya created. He was very professional throughout.
Tony S.
@TourTips
•
국기 ()
Wilpshire, United Kingdom
•
26일 전
공유
사용자 아바타
Power BI Developer for Sales Insights • 가격 미공개
I would rate Aditya very highly on both technical and interpersonal skills. He understand your logic, clearly communicates and gets on calls with you to discuss updates and receives feedback time to time to ensure the build is top notch.
Bharath S.
@IndianCPGGuys
•
국기 ()
Hyderabad, India
•
2개월 전
공유
D
Advanced Excel Data Compilation & Analysis • 가격 미공개
Thank you for all the great work!
Dev K.
@Dev1303
•
국기 ()
Ahmedabad, India
•
3개월 전
공유
경험
Reporting Analyst
11월, 2022 - 선물
•
2 , 1
TATA AIG
11월, 2022 - 선물
•
2 , 1
Tools used - SQL, Python, Excel Working on ETL Process. Automating reconciliation process. Creating Data Quality checks using SQL queries.
11월, 2022 - 선물
•
2 , 1
Web Scraping and Database Management
3월, 2022 - 6월, 2022
•
2 , 30
Freelance
3월, 2022 - 6월, 2022
•
2 , 30
The goal was to create a database that had the following data country name - state/province - region - city - Pincode Using an online web scraper I scraped data from websites. The final part of the project was to club all these data into a single database and used Microsoft Access to dump all the country data into the database.
3월, 2022 - 6월, 2022
•
2 , 30
교육
Mumbai University
2020 - 2023
•
3
Bsc. Data Science & Business Analytics
국기 ()
India
2020 - 2023
•
3
자격증
Data Analytics and Visualisation Virtual Experience Program
2022
Forage
Did Exploratory Data Analysis using Python (Matplotib, Seaborn, etc) and created a predictive model using machine learning techniques.
2022
자격증
P
Preferred Freelancer Program SLA
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