✅ Step-by-Step Approach to Learn Data Analytics 📈🧠

➊ Excel Fundamentals:
✔ Master formulas, pivot tables, data validation, charts, and graphs.

➋ SQL Basics:
✔ Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions.

➌ Data Visualization:
✔ Get proficient with tools like Tableau or Power BI to create insightful dashboards.

➍ Statistical Concepts:
✔ Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing.

➎ Data Cleaning & Preprocessing:
✔ Learn how to handle missing data, outliers, and data inconsistencies.

➏ Exploratory Data Analysis (EDA):
✔ Explore datasets, identify patterns, and formulate hypotheses.

➐ Python for Data Analysis (Optional but Recommended):
✔ Learn Pandas and NumPy for data manipulation and analysis.

➑ Real-World Projects:
✔ Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection.

➒ Business Acumen:
✔ Understand key business metrics and how data insights impact business decisions.

➓ Build a Portfolio:
✔ Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis.

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