Comprehensive Data Analysis & E-commerce Management

Python, Data Entry, SQL, Big Data Sales, Tableau, NumPy, Data Visualization, Data Analysis, Power BI, Data Collection · 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. Prod