📊 Data Analytics Basics Cheatsheet

1. What is Data Analytics?
Analyzing raw data to find patterns, trends, and insights to support decision-making.

2. Types of Data Analytics:
⦁ Descriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What might happen next?
⦁ Prescriptive: What should be done?

3. Key Tools & Languages:
⦁ Excel – Quick analysis & charts
⦁ SQL – Query and manage databases
⦁ Python (Pandas, NumPy, Matplotlib)
⦁ Power BI / Tableau – Dashboards & visualization

4. Data Cleaning Basics:
⦁ Handle missing values
⦁ Remove duplicates
⦁ Convert data types
⦁ Standardize formats

5. Exploratory Data Analysis (EDA):
⦁ Summary stats (mean, median, mode)
⦁ Data distribution
⦁ Correlation matrix
⦁ Visual tools: bar charts, boxplots, scatter plots

6. Data Visualization:
⦁ Use charts to simplify insights
⦁ Choose chart types based on data (line for trends, bar for comparisons, pie for proportions)

7. SQL Essentials:
⦁ SELECT, WHERE, JOIN, GROUP BY, HAVING, ORDER BY
⦁ Aggregate functions: COUNT, SUM, AVG, MAX, MIN

8. Python for Analysis:
⦁ Pandas for dataframes
⦁ Matplotlib/Seaborn for plotting
⦁ Scikit-learn for basic ML models

9. Metrics to Know:
⦁ Growth %, Conversion rate, Retention rate
⦁ KPIs specific to domain (finance, marketing, etc.)

10. Real-World Use Cases:
⦁ Customer segmentation
⦁ Sales trend analysis
⦁ A/B testing
⦁ Forecasting demand


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