# Python Data Manipulation with Pandas
import pandas as pd

# Load data
df = pd.read_csv('sales_data.csv')

# Basic statistics
print(df.describe())

# Group by and aggregate
sales_by_region = df.groupby('region')['sales'].sum()
print(sales_by_region)

# Clean data
df = df.dropna()
df['date'] = pd.to_datetime(df['date'])

# Filter and sort
top_sales = df[df['sales'] > 10000].sort_values('sales', ascending=False)
print(top_sales.head())

-- SQL Example: Data Extraction
-- Find top 10 customers by total purchase amount
SELECT 
    customer_id,
    SUM(order_amount) AS total_spent
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY customer_id
ORDER BY total_spent DESC
LIMIT 10;

-- Join tables to get customer details
SELECT 
    c.customer_name,
    c.email,
    SUM(o.order_amount) AS total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id
ORDER BY total_spent DESC;

# Machine Learning with Scikit-learn
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load data
X = df[['feature1', 'feature2', 'feature3']]
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)

# Predict and evaluate
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'MSE: {mse}')

# Data Visualisation with Matplotlib
import matplotlib.pyplot as plt
import seaborn as sns

# Line plot
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['sales'], marker='o')
plt.title('Sales Trend')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.show()

# Correlation heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

# Distribution plot
sns.histplot(df['sales'], bins=30, kde=True)
plt.title('Sales Distribution')
plt.show()

Data Storytelling Checklist:
✅ Know your audience (technical vs non-technical)
✅ Start with the business question, not the data
✅ Use visuals to support your narrative
✅ Keep it simple — avoid jargon
✅ Focus on actionable insights
✅ Practice with real-world datasets

Storytelling Framework:
1. Context — What is the business problem?
2. Analysis — What did you find?
3. Insight — Why does it matter?
4. Action — What should be done?

Communication Examples:
Technical Audience:
"We used a random forest model with 100 trees, achieving an R² of 0.85. The feature importance analysis shows that customer tenure is the strongest predictor of churn."

Non-Technical Audience:
"Our model predicts customer churn with 85% accuracy. The most important factor is how long a customer has been with us. We should focus on retaining long-term customers."

Key Message: Always tailor your communication to your audience.

Data Scientist Roadmap:
Focus: Analysing data and building models.

Skills to Learn:
- Python, SQL, Statistics
- Machine Learning (Scikit-learn, XGBoost)
- Deep Learning (TensorFlow, PyTorch)
- Data Visualisation (Tableau, Matplotlib)
- Business Acumen

Certifications:
- IBM Data Science Professional
- Google Data Analytics Certificate

Projects:
- Predictive modelling (e.g., house prices)
- Customer segmentation
- Time series forecasting

Job Titles: Data Scientist, Data Analyst, AI Researcher

ML Engineer Roadmap:
Focus: Building and deploying ML models in production.

Skills to Learn:
- Python, SQL, Java/Scala
- ML algorithms and deep learning
- Cloud platforms (AWS, Azure, GCP)
- DevOps and MLOps
- Docker and Kubernetes

Certifications:
- AWS Machine Learning Specialty
- Azure AI Engineer

Projects:
- Deploy a model as an API
- Build a CI/CD pipeline for ML
- Monitor model performance

Job Titles: ML Engineer, ML Ops Engineer, AI Engineer

Data Analyst Roadmap:
Focus: Analysing data and creating dashboards.

Skills to Learn:
- Python, SQL
- Data visualisation (Tableau, Power BI)
- Statistics
- Excel
- Communication

Certifications:
- Google Data Analytics Certificate
- Tableau Desktop Specialist

Projects:
- Sales dashboard
- Customer behaviour analysis
- Marketing campaign analysis

Job Titles: Data Analyst, Business Analyst, BI Developer