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1. Data Analysis Fundamentals: Understand the role of a data analyst, the data lifecycle, and key tools and workflows.
2. Excel for Analysis: Work with spreadsheets, formulas, pivot tables, charts, and data cleaning in Excel or Google Sheets.
3. Python for Data Analysis: Use Python with pandas, NumPy, and matplotlib for cleaning, processing, and visualizing data.
4. SQL for Data Querying: Write SQL queries to extract, filter, sort, and aggregate data from databases.
5. Data Visualization: Create visual reports using tools like Excel charts, matplotlib, seaborn, or Tableau.
6. Real Projects: Analyze real business data for projects like customer trends, sales insights, and survey analytics.
7. Decision-Making with Data: Generate insights, present findings, and support business decisions with data.
8. Career Guidance: Build a professional portfolio, improve your CV, and get ready for analyst job interviews or freelance gigs.