Data Analyst Checklist

📚 1. Learn the Fundamentals

• [ ] Understand what data analysts do
• [ ] Learn types of data: structured vs unstructured
• [ ] Basic business and data-driven decision-making skills

💻 2. Master Excel

• [ ] Formulas (VLOOKUP, INDEX-MATCH, IF, etc.)
• [ ] Pivot Tables
• [ ] Charts & Conditional Formatting
• [ ] Data Cleaning & Analysis

💡 3. Learn SQL (Must-Have Skill)

• [ ] SELECT, WHERE, GROUP BY, HAVING, ORDER BY
• [ ] JOINs (INNER, LEFT, RIGHT, FULL)
• [ ] Subqueries, CTEs, Window Functions
• [ ] Practice on platforms like LeetCode, StrataScratch, Hackerrank

📈 4. Learn Data Visualization

• [ ] Tools: Power BI / Tableau / Excel
• [ ] Create dashboards
• [ ] Understand charts: bar, line, pie, scatter, heatmaps
• [ ] Use filters, slicers, and interactivity

🐍 5. Learn Python for Data Analysis

• [ ] NumPy and Pandas for data manipulation
• [ ] Matplotlib & Seaborn for visualization
• [ ] Data cleaning & EDA
• [ ] Jupyter Notebook for presenting analysis

🧪 6. Statistics & Analytics Concepts

• [ ] Descriptive Statistics (mean, median, mode, std dev)
• [ ] Probability & distributions
• [ ] Hypothesis testing
• [ ] Correlation & regression analysis
• [ ] A/B testing

📊 7. Practice Real Projects

• [ ] Sales data analysis
• [ ] Customer churn analysis
• [ ] Marketing campaign effectiveness
• [ ] Build a portfolio and publish on GitHub / Kaggle

🔧 8. Tools You Should Know

• [ ] Excel, SQL, Power BI/Tableau
• [ ] Python
• [ ] Git & GitHub
• [ ] Google Sheets, Google Data Studio (optional)

📁 9. Build Your Portfolio

• [ ] Upload 3–5 data projects
• [ ] Explain your process, insights, and visualizations
• [ ] Make it recruiter-friendly

🚀 10. Prepare for Interviews

• [ ] Practice case studies
• [ ] Revise SQL queries
• [ ] Explain insights clearly
• [ ] Practice Excel and visualization tasks

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