Comprehensive Data Analysis & E-commerce Management

Data Collection & Extraction – Collect, extract, and organize data from multiple sources for analysis.
Data Cleaning – Identify and remove duplicates, missing values, incorrect entries, and inconsistencies.
Microsoft Excel – Handle large datasets using formulas, PivotTables, charts, sorting, filtering, conditional formatting, and data validation.
Power BI – Create interactive dashboards and reports to visualize KPIs, trends, sales, inventory, and business performance.
SQL – Query, filter, join, aggregate, and analyze data stored in relational databases.
Python – Use Python for data analysis, automation, data manipulation, and reporting.
NumPy & Pandas – Clean, transform, analyze, and manipulate structured datasets efficiently.
Data Visualization – Convert complex data into easy-to-understand charts, graphs, dashboards, and reports.
Statistical Analysis – Analyze patterns, trends, averages, distributions, and relationships within datasets.
E-commerce Analytics – Analyze product, sales, customer, inventory, and marketplace data to identify business insights.
SEO Data Analysis – Analyze keywords, product titles, descriptions, and search-related data to improve product visibility.
Product Management – Organize and validate product information, SKUs, categories, prices, attributes, and product details.
Inventory Management – Track stock levels, identify low-stock/out-of-stock products, and analyze inventory performance.
Web Research – Collect and verify relevant product, market, competitor, and business information through web research.
eBay/Marketplace Data Management – Manage product listings, pricing, inventory, categories, descriptions, and listing quality.
Data Validation & Quality Checking – Check datasets for errors, duplicates, missing information, incorrect formats, and inconsistencies.
Large Dataset Handling – Efficiently manage and analyze datasets containing thousands of rows and multiple columns.
Reporting & Insights – Transform raw data into meaningful reports and actionable business insights.
Generative AI for Analytics – Use AI tools to support data exploration, reporting, automation, and productivity.
Business Decision Support – Use data-driven insights to help businesses understand performance and make better decisions.