✅ Core Data Analytics Concepts You Should Know:

1. Excel & Spreadsheets (Basics)
- Data entry, sorting, filtering
- Basic formulas: SUM, AVERAGE, IF, VLOOKUP, COUNTIF
- Pivot tables & charts

2. Statistics & Math Basics
- Mean, Median, Mode
- Standard Deviation, Variance
- Correlation & Regression
- Probability basics

3. SQL (Data Extraction)
- SELECT, WHERE, GROUP BY, HAVING
- JOINs (INNER, LEFT, RIGHT)
- Subqueries & CTEs
- Window functions (ROW_NUMBER, RANK, etc.)

4. Data Cleaning & Wrangling
- Handling missing values
- Removing duplicates
- Formatting and standardization

5. Data Visualization
- Tools: Excel, Power BI, Tableau
- Charts: Bar, Line, Pie, Histogram
- Dashboards & storytelling with data

6. Programming with Python (Optional but recommended)
- Pandas, NumPy for data manipulation
- Matplotlib, Seaborn for visualization
- Jupyter Notebooks for analysis

7. Business Understanding
- Asking the right questions
- KPI understanding
- Domain knowledge

8. Projects & Case Studies
- Sales analysis, Customer retention, Market trends
- Use real-world datasets (Kaggle, Google Data Studio)

9. Reporting & Communication
- Presenting insights clearly.
- Visual storytelling
- Report automation basics (Excel, PowerPoint)

10. Tools Knowledge
- Power BI / Tableau
- SQL Workbench / BigQuery
- Excel / Google Sheets

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