Data Analytics - SQL, Python, Excel, AI & ChatGPT

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SQL Cheatsheet 📝 This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics. 1. Database Basics - CREATE DATABASE db_name; - USE db_name; 2. Tables - Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype); - Drop Table: DROP TABLE table_name; - Alter Table: ALTER TABLE table_name ADD column_name datatype; 3. Insert Data - INSERT INTO table_name (col1, col2) VALUES (val1, val2); 4. Select Queries - Basic Select: SELECT * FROM table_name; - Select Specific Columns: SELECT col1, col2 FROM table_name; - Select with Condition: SELECT * FROM table_name WHERE condition; 5. Update Data - UPDATE table_name SET col1 = value1 WHERE condition; 6. Delete Data - DELETE FROM table_name WHERE condition; 7. Joins - Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col; - Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col; - Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col; 8. Aggregations - Count: SELECT COUNT(*) FROM table_name; - Sum: SELECT SUM(col) FROM table_name; - Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col; 9. Sorting & Limiting - Order By: SELECT * FROM table_name ORDER BY col ASC|DESC; - Limit Results: SELECT * FROM table_name LIMIT n; 10. Indexes - Create Index: CREATE INDEX idx_name ON table_name (col); - Drop Index: DROP INDEX idx_name; 11. Subqueries - SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table); 12. Views - Create View: CREATE VIEW view_name AS SELECT * FROM table_name; - Drop View: DROP VIEW view_name;

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Sure! Here’s your content with the asterisks replaced by double asterisks: Real‑world Data Analytics Questions with Answers 🔥 Let’s practice end‑to‑end data thinking using this small dataset. 📊 Dataset: customer_orders | order_id | customer_id | product | category | quantity | unit_price | order_date | |----------|-------------|---------------|------------|----------|------------|-------------| | 1 | 1001 | Laptop | Electronics| 2 | 75000 | 2025‑01‑10 | | 2 | 1002 | Mouse | Electronics| 10 | 1500 | 2025‑01‑12 | | 3 | 1003 | Chair | Furniture | 5 | 8000 | 2025‑01‑15 | | 4 | 1001 | Keyboard | Electronics| 8 | 2500 | 2025‑01‑11 | | 5 | 1004 | Desk | Furniture | 3 | 15000 | 2025‑01‑18 | | 6 | 1002 | Monitor | Electronics| 4 | 25000 | 2025‑01‑20 | | 7 | 1005 | Table | Furniture | 6 | 5000 | 2025‑01‑22 | | 8 | 1003 | Webcam | Electronics| 12 | 3000 | 2025‑01‑14 | 1. Define what “data analysis” means in this context • Data analysis means transforming raw orders into insights: what sells most, who the best customers are, and how revenue changes over time. • You’d use SQL to query, Excel/Python to clean, and Power BI to visualize. 2. What are the key metrics you’d track for this business? • Revenue = quantity × unit_price • Order count and average order value (AOV) • Top‑selling categories and best customers by revenue 3. Write a SQL query for total revenue by category

    
        SELECT 
    category,
    SUM(quantity * unit_price) AS total_revenue
FROM customer_orders
GROUP BY category;
{}
    

4. How would you find repeat customers?

    
        SELECT 
    customer_id,
    COUNT(order_id) AS order_count,
    SUM(quantity * unit_price) AS total_spent
FROM customer_orders
GROUP BY customer_id
HAVING COUNT(order_id) > 1;
{}
    

• Customers with order_count > 1 are repeat buyers. 5. How would you detect “top customers”? • Define “top” by total_spent or average order value: – SUM(revenue) / COUNT(orders) • Use Power BI/Excel to sort descending and highlight top 10%. 6. What would an outlier analysis look like? • Compute min, max, average, standard deviation of revenue per order. • Flag orders where: – revenue > average + 2 * standard_deviation • Check if such orders are errors or real big deals (e.g., enterprise purchase). 7. How would you report month‑on‑month growth? • In SQL/Power BI: – Group by YEAR(order_date) and MONTH(order_date) – Compute revenue per month – Then calculate: ▪ MoM % = (CurrentMonthRevenue − PreviousMonthRevenue) / PreviousMonthRevenue 8. How would you turn this into a dashboard? • Page 1 – Overview: Cards for total revenue, total orders, AOV. • Page 2 – Trends: Line chart for MoM revenue, bar chart for category split. • Page 3 – Customers: Table for top 10 customers and repeat customers. 9. How would you handle dirty data (nulls, duplicates)? • Pre‑check: – COUNT(*) vs COUNT(customer_id) to spot missing customers. • Clean: – Drop or impute missing critical fields. – Remove duplicate orders using DISTINCT or ROW_NUMBER(). 10. How would you explain your findings to a non‑tech manager? • Use simple language + visuals: – “Our top product category is Electronics, contributing X% of revenue.” – “N top customers account for M% of total sales.” • Avoid formulas; focus on business impact: retention, profitability, growth. Double Tap ❤️ For More!

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Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use. 1. Python Basics - Variables: x = 10 y = "Hello" - Data Types:   - Integers: x = 10   - Floats: y = 3.14   - Strings: name = "Alice"   - Lists: my_list = [1, 2, 3]   - Dictionaries: my_dict = {"key": "value"}   - Tuples: my_tuple = (1, 2, 3) - Control Structures:   - if, elif, else statements   - Loops:    

    
            for i in range(5):
        print(i)
    {}
    

  - While loop:   

    
            while x < 5:
        print(x)
        x += 1
    {}
    

2. Importing Libraries - NumPy:

    
          import numpy as np
  {}
    

- Pandas:

    
          import pandas as pd
  {}
    

- Matplotlib:

    
          import matplotlib.pyplot as plt
  {}
    

- Seaborn:

    
          import seaborn as sns
  {}
    

3. NumPy for Numerical Data - Creating Arrays:

    
          arr = np.array([1, 2, 3, 4])
  {}
    

- Array Operations:

    
          arr.sum()
  arr.mean()
  {}
    

- Reshaping Arrays:

    
          arr.reshape((2, 2))
  {}
    

- Indexing and Slicing:

    
          arr[0:2]  # First two elements
  {}
    

4. Pandas for Data Manipulation - Creating DataFrames:

    
          df = pd.DataFrame({
      'col1': [1, 2, 3],
      'col2': ['A', 'B', 'C']
  })
  {}
    

- Reading Data:

    
          df = pd.read_csv('file.csv')
  {}
    

- Basic Operations:

    
          df.head()          # First 5 rows
  df.describe()      # Summary statistics
  df.info()          # DataFrame info
  {}
    

- Selecting Columns:

    
          df['col1']
  df[['col1', 'col2']]
  {}
    

- Filtering Data:

    
          df[df['col1'] > 2]
  {}
    

- Handling Missing Data:

    
          df.dropna()        # Drop missing values
  df.fillna(0)       # Replace missing values
  {}
    

- GroupBy:

    
          df.groupby('col2').mean()
  {}
    

5. Data Visualization - Matplotlib:

    
          plt.plot(df['col1'], df['col2'])
  plt.xlabel('X-axis')
  plt.ylabel('Y-axis')
  plt.title('Title')
  plt.show()
  {}
    

- Seaborn:

    
          sns.histplot(df['col1'])
  sns.boxplot(x='col1', y='col2', data=df)
  {}
    

6. Common Data Operations - Merging DataFrames:

    
          pd.merge(df1, df2, on='key')
  {}
    

- Pivot Table:

    
          df.pivot_table(index='col1', columns='col2', values='col3')
  {}
    

- Applying Functions:

    
          df['col1'].apply(lambda x: x*2)
  {}
    

7. Basic Statistics - Descriptive Stats:

    
          df['col1'].mean()
  df['col1'].median()
  df['col1'].std()
  {}
    

- Correlation:

    
          df.corr()
  {}
    

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Real-world SQL Questions with Answers 🔥 Let's dive into some real-world SQL questions with a mini dataset. 📊 Dataset: employees

    
        id  name    department  salary  manager_id
1   Aditi   HR          30000   5
2   Rahul   IT          50000   6
3   Neha    IT          60000   6
4   Aman    Sales       40000   7
5   Kiran   HR          70000   NULL
6   Mohit   IT          80000   NULL
7   Suresh  Sales       65000   NULL
8   Pooja   HR          30000   5
{}
    

1. Find average salary per department

    
        SELECT department, AVG(salary) AS avg_salary 
FROM employees 
GROUP BY department;
{}
    

2. Find employees earning above department average

    
        SELECT name, department, salary 
FROM employees e 
WHERE salary > ( 
    SELECT AVG(salary) 
    FROM employees 
    WHERE department = e.department 
);
{}
    

3. Find highest salary in each department

    
        SELECT department, MAX(salary) AS max_salary 
FROM employees 
GROUP BY department;
{}
    

4. Find employees who earn more than their manager

    
        SELECT e.name 
FROM employees e 
JOIN employees m ON e.manager_id = m.id 
WHERE e.salary > m.salary;
{}
    

5. Count employees in each department

    
        SELECT department, COUNT(*) AS total_employees 
FROM employees 
GROUP BY department;
{}
    

6. Find departments with more than 2 employees

    
        SELECT department, COUNT(*) AS total 
FROM employees 
GROUP BY department 
HAVING COUNT(*) > 2;
{}
    

7. Find second highest salary

    
        SELECT MAX(salary) 
FROM employees 
WHERE salary < (SELECT MAX(salary) FROM employees);
{}
    

8. Find employees without managers

    
        SELECT name 
FROM employees 
WHERE manager_id IS NULL;
{}
    

9. Rank employees by salary

    
        SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rank 
FROM employees;
{}
    

10. Find duplicate salaries

    
        SELECT salary, COUNT(*) 
FROM employees 
GROUP BY salary 
HAVING COUNT(*) > 1;
{}
    

11. Top 2 highest salaries

    
        SELECT DISTINCT salary 
FROM employees 
ORDER BY salary DESC 
LIMIT 2;
{}
    

Double Tap ❤️ For More

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