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.