Data Analytics: Complete Course Script - From Beginner to Professional

 Comprehensive data analytics course covering fundamentals, tools, techniques, real-world projects, and career pathways. Learn skills to become a professional data analyst in 12 weeks.

Course Overview and Learning Objectives

What is Data Analytics?

Definition: Data Analytics is the practice of examining raw data to discover patterns, draw conclusions, and support decision-making. It combines statistics, programming, and business acumen to transform data into actionable insights.

Course Structure

This comprehensive course is designed to take you from complete beginner to job-ready data analyst in 12 weeks through structured learning, hands-on projects, and real-world applications.

Prerequisite: Basic computer literacy. No prior analytics or programming experience required.

Learning Outcomes

By completing this course, you will be able to:

• Understand data analytics fundamentals and concepts
• Use Excel for data analysis and visualization
• Write SQL queries to extract and transform data
• Use Python for data manipulation and analysis
• Create compelling data visualizations
• Develop analytics dashboards
• Conduct exploratory data analysis
• Build predictive models
• Present insights to stakeholders
• Execute end-to-end analytics projects

---

WEEK 1-2: FOUNDATIONS OF DATA ANALYTICS

Module 1: Data Analytics Fundamentals

Lesson 1.1: Introduction to Data Analytics

Key Concepts:

Data Raw facts and observations. Examples: Sales transactions, customer demographics, website clicks.

Information Data processed into meaningful context. Example: "Sales increased 15% in Q3 among 25-35 age group in North India."

Insights Conclusions drawn from information that drive decisions. Example: "We should target 25-35 age group with Q4 campaign in North India for maximum ROI."

The Analytics Process:

Raw Data → Collection → Cleaning → Analysis → Visualization → 
Insights → Action → Business Impact

Lesson 1.2: Types of Analytics

Descriptive Analytics - "What Happened?" Analyzing historical data to understand patterns.

Example: "Average monthly sales over past 12 months"

Tools: Excel, Power BI, Tableau

Outcome: Reports, dashboards, summaries

Diagnostic Analytics - "Why Did It Happen?" Investigating reasons behind outcomes.

Example: "Why did sales increase 15% in Q3?"

Techniques: Correlation analysis, trend analysis, root cause analysis

Tools: SQL, Python, statistical software

Predictive Analytics - "What Will Happen?" Using historical data to forecast future outcomes.

Example: "Predict next month's sales based on historical trends"

Techniques: Machine learning, regression, time series forecasting

Tools: Python, R, specialized ML platforms

Prescriptive Analytics - "What Should We Do?" Recommending actions to achieve desired outcomes.

Example: "Allocate 40% budget to North India, 35% to South, 25% to East for maximum projected revenue"

Techniques: Optimization, simulation, decision analysis

Module 2: Data Types and Structures

Lesson 2.1: Data Types

Structured Data Organized in tables with rows and columns. Example: Customer database with columns (Name, Age, Email, Purchase Amount).

Unstructured Data No predefined structure. Examples: Text documents, images, videos, audio.

Semi-Structured Data Mix of structured and unstructured. Examples: JSON, XML, log files.

Quantitative (Numerical) Data Measurable quantities. Examples: Age, salary, temperature, revenue.

Categorical (Qualitative) Data Non-numeric categories. Examples: Color, gender, product category, region.

Lesson 2.2: Data Distribution

Normal Distribution Bell-shaped curve; most data points near mean. Many natural phenomena follow normal distribution.

Skewed Distribution Asymmetrical. Positive skew (tail right), negative skew (tail left).

Bimodal Distribution Two peaks. Suggests two distinct groups in data.

Understanding distributions helps identify:

• Outliers and anomalies
• Appropriate statistical tests
• Data transformation needs

Module 3: Key Analytics Concepts

Lesson 3.1: Statistical Foundations

Mean (Average) Sum of values divided by count.

Mean = (Sum of all values) / (Number of values)

Example: (10 + 20 + 30 + 40 + 50) / 5 = 30

Median Middle value when data sorted. Less affected by outliers.

Example: 10, 20, 30, 40, 50
Median = 30 (middle value)

Mode Most frequently occurring value.

Example: 10, 20, 20, 30, 30, 30
Mode = 30 (occurs 3 times)

Standard Deviation Measures spread/variability of data. High SD = data spread out; Low SD = data clustered.

Variance Square of standard deviation. Measures how far data points are from mean.

Correlation Relationship between two variables. Ranges from -1 to +1.

Correlation = 1: Perfect positive relationship
Correlation = 0: No relationship
Correlation = -1: Perfect negative relationship

Lesson 3.2: Sampling and Bias

Sampling Selecting subset of data for analysis. Used when full dataset is too large.

Sample Size Larger samples more accurately represent population. Balance between precision and resources.

Bias Systematic error skewing results. Types: Selection bias, measurement bias, response bias.

Mitigation: Random sampling, stratified sampling, careful data collection.

---

WEEK 3-4: EXCEL FOR DATA ANALYSIS

Module 4: Excel Fundamentals and Data Preparation

Lesson 4.1: Excel Basics

Spreadsheet Structure Rows (horizontal), columns (vertical), cells (intersections).

Data Entry Best Practices:

• One data point per cell
• Consistent formatting
• No merged cells in data ranges
• Column headers in first row
• No blank rows/columns within data

Lesson 4.2: Formulas and Functions

Mathematical Functions

=SUM(A1:A10)          // Add values
=AVERAGE(A1:A10)      // Calculate average
=COUNT(A1:A10)        // Count cells with numbers
=MIN(A1:A10)          // Find minimum
=MAX(A1:A10)          // Find maximum

Logical Functions

=IF(A1>100, "High", "Low")        // Conditional logic
=AND(A1>50, B1<100)               // All conditions true?
=OR(A1=1, A1=2, A1=3)             // Any condition true?

Text Functions

=LEN(A1)              // Length of text
=UPPER(A1)            // Convert to uppercase
=CONCATENATE(A1, " ", B1)  // Combine text
=LEFT(A1, 3)          // First 3 characters

Lesson 4.3: Data Cleaning

Common Data Quality Issues:

Missing Values

• Identify: Look for blank cells
• Handle: Delete, fill with mean/median, leave as is depending on context

Duplicate Records

• Identify: Sort and visually inspect, or Data > Remove Duplicates
• Handle: Remove duplicates or investigate root cause

Inconsistent Formatting

• Standardize text cases (UPPER, LOWER, PROPER functions)
• Consistent date formats
• Consistent number formats

Outliers

• Identify: Values significantly different from others
• Verify: Confirm if genuine or data error
• Handle: Keep if genuine, remove if error

Module 5: Pivot Tables and Summarization

Lesson 5.1: Creating Pivot Tables

What is a Pivot Table? Summarizes large datasets into meaningful insights. Rotates (pivots) data dimensions.

Example Scenario: Raw data: Sales transactions with Date, Region, Product, Amount

Pivot table shows: Revenue by Region and Product

Steps to Create:

1. Select data range
2. Insert > Pivot Table
3. Drag fields to Rows, Columns, Values
4. Customize as needed

Lesson 5.2: Aggregation Functions in Pivot Tables

SUM:        Total of values
AVERAGE:    Mean value
COUNT:      Number of items
MIN/MAX:    Smallest/largest value
STDEV:      Standard deviation

Practical Example:

Data: Sales by Salesperson, Month, Product

Pivot Table Structure:
─────────────────────────────────────────
      Jan      Feb      Mar    Total
─────────────────────────────────────────
John  $5,000   $6,000   $7,000  $18,000
Jane  $4,500   $5,500   $6,200  $16,200
Bob   $3,200   $3,800   $4,100  $11,100
─────────────────────────────────────────
Total $12,700  $15,300  $17,300 $45,300

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WEEK 5-6: SQL FOR DATA ANALYSIS

Module 6: SQL Fundamentals

Lesson 6.1: Introduction to SQL

What is SQL? Structured Query Language. Universal language for interacting with databases.

Why SQL?

• Access large datasets efficiently
• Extract specific data subsets
• Perform complex calculations
• Join data from multiple tables

Lesson 6.2: Basic SELECT Queries

Selecting All Data:

sql

SELECT * FROM customers;

Selecting Specific Columns:

sql

SELECT name, email, age FROM customers;

Filtering with WHERE:

sql

SELECT * FROM customers
WHERE age > 25 AND region = 'North India';

Logical Operators:

AND:  Both conditions true
OR:   At least one condition true
NOT:  Condition not true
IN:   Value in list
BETWEEN: Value in range
LIKE: Pattern matching

Lesson 6.3: Aggregation and Grouping

Aggregate Functions:

sql

SELECT 
  COUNT(*) as total_customers,
  AVG(purchase_amount) as avg_purchase,
  SUM(purchase_amount) as total_revenue,
  MAX(purchase_amount) as max_purchase
FROM orders;

GROUP BY - Aggregate by Category:

sql

SELECT 
  region,
  COUNT(*) as customer_count,
  SUM(purchase_amount) as total_sales,
  AVG(purchase_amount) as avg_sale
FROM orders
GROUP BY region;

HAVING - Filter Groups:

sql

SELECT 
  product_category,
  COUNT(*) as sales_count
FROM orders
GROUP BY product_category
HAVING COUNT(*) > 50;

Module 7: Advanced SQL

Lesson 7.1: Joins

INNER JOIN - Common Records

sql

SELECT 
  o.order_id,
  c.customer_name,
  o.purchase_amount
FROM orders o
INNER JOIN customers c
  ON o.customer_id = c.customer_id;

LEFT JOIN - All from Left Table

sql

SELECT 
  c.customer_name,
  COUNT(o.order_id) as order_count
FROM customers c
LEFT JOIN orders o
  ON c.customer_id = o.customer_id
GROUP BY c.customer_name;

Lesson 7.2: Subqueries

Subquery in WHERE Clause:

sql

SELECT * FROM customers
WHERE customer_id IN (
  SELECT customer_id FROM orders
  WHERE purchase_amount > 10000
);

Subquery in FROM Clause:

sql

SELECT 
  region,
  avg_purchase
FROM (
  SELECT 
    region,
    AVG(purchase_amount) as avg_purchase
  FROM orders
  GROUP BY region
) as regional_avg
WHERE avg_purchase > 5000;

Lesson 7.3: Window Functions

Running Total:

sql

SELECT 
  date,
  amount,
  SUM(amount) OVER (
    ORDER BY date
  ) as running_total
FROM sales
ORDER BY date;

Rank Within Groups:

sql

SELECT 
  salesperson,
  sales,
  RANK() OVER (ORDER BY sales DESC) as rank
FROM sales_performance;

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WEEK 7-8: PYTHON FOR DATA ANALYSIS

Module 8: Python Basics for Analytics

Lesson 8.1: Python Setup and Libraries

Essential Libraries:

python

# Data manipulation
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Statistical analysis
from scipy import stats
import statsmodels.api as sm

# Machine learning (later weeks)
from sklearn import preprocessing, ensemble, metrics

Lesson 8.2: Working with Data in Pandas

Creating DataFrames:

python

# From dictionary
data = {
  'Name': ['Alice', 'Bob', 'Charlie'],
  'Age': [25, 30, 35],
  'Salary': [50000, 60000, 75000]
}
df = pd.DataFrame(data)

# From CSV
df = pd.read_csv('customers.csv')

# From Excel
df = pd.read_excel('sales_data.xlsx')

Exploring Data:

python

# First/last rows
df.head()
df.tail()

# Dataset info
df.info()           # Data types, missing values
df.describe()       # Statistical summary
df.shape            # Rows and columns

# Check for missing values
df.isnull().sum()

Data Cleaning:

python

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.dropna()                    # Remove rows with NaN
df['Age'] = df['Age'].fillna(df['Age'].mean())  # Fill with mean

# Remove outliers (values > 3 std devs)
df = df[np.abs(stats.zscore(df['Salary'])) < 3]

# Data type conversion
df['Date'] = pd.to_datetime(df['Date'])
df['Age'] = df['Age'].astype(int)

Filtering and Selection:

python

# Filter rows
high_earners = df[df['Salary'] > 60000]

# Multiple conditions
result = df[(df['Age'] > 25) & (df['Salary'] < 80000)]

# Select columns
selected = df[['Name', 'Salary']]

# Select by location
df.loc[0]           # First row
df.iloc[0:5]        # First 5 rows
df.loc[df['Age'] > 30, 'Name']  # Names of people over 30

Module 9: Data Analysis with Python

Lesson 9.1: Descriptive Statistics

python

# Mean, median, mode
df['Salary'].mean()
df['Salary'].median()
df['Salary'].mode()

# Spread
df['Salary'].std()      # Standard deviation
df['Salary'].var()      # Variance
df['Salary'].min()
df['Salary'].max()

# Percentiles
df['Salary'].quantile(0.25)  # 25th percentile
df['Salary'].quantile(0.75)  # 75th percentile

Lesson 9.2: Grouping and Aggregation

python

# Group by single column
by_region = df.groupby('Region')['Salary'].mean()

# Multiple aggregations
summary = df.groupby('Department').agg({
    'Salary': ['mean', 'min', 'max'],
    'Age': 'mean',
    'Name': 'count'
})

# Group by multiple columns
by_dept_region = df.groupby(['Department', 'Region'])['Salary'].sum()

Lesson 9.3: Merging and Joining Data

python

# Merge DataFrames
df_merged = pd.merge(df_customers, df_orders, 
                     on='customer_id', 
                     how='inner')

# Concatenate
df_combined = pd.concat([df1, df2], axis=0)

# Join
df_result = df1.join(df2, on='key')

Lesson 9.4: Correlation and Statistical Testing

python

# Correlation matrix
correlation = df.corr()

# Correlation with specific column
df.corr()['Salary'].sort_values(ascending=False)

# Statistical test - t-test (comparing two groups)
from scipy import stats
group1 = df[df['Region'] == 'North']['Salary']
group2 = df[df['Region'] == 'South']['Salary']
t_stat, p_value = stats.ttest_ind(group1, group2)

print(f"T-statistic: {t_stat}, P-value: {p_value}")
if p_value < 0.05:
    print("Significant difference between regions")

---

WEEK 8-9: DATA VISUALIZATION

Module 10: Visualization Fundamentals

Lesson 10.1: Visualization Principles

Choose Right Chart Type:

Line Chart - Show trends over time

Sales Over 12 Months (ascending trend)

Bar Chart - Compare categories

Sales by Region (North > South > East)

Pie Chart - Show parts of whole

Market Share: Company A 40%, B 35%, C 25%

Scatter Plot - Show relationship between two variables

Age vs Salary (positive correlation)

Histogram - Show distribution

Customer age distribution (bell curve)

Lesson 10.2: Visualization with Python

Matplotlib - Basic Plotting:

python

import matplotlib.pyplot as plt

# Line chart
plt.figure(figsize=(10, 6))
plt.plot(df['Month'], df['Sales'], marker='o')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.title('Monthly Sales Trend')
plt.grid(True)
plt.show()

# Bar chart
plt.bar(df['Region'], df['Total_Sales'])
plt.xlabel('Region')
plt.ylabel('Sales ($)')
plt.title('Sales by Region')
plt.show()

# Histogram
plt.hist(df['Age'], bins=20)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Age Distribution')
plt.show()

Seaborn - Advanced Visualization:

python

import seaborn as sns

# Heatmap showing correlations
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

# Box plot by category
sns.boxplot(x='Region', y='Salary', data=df)
plt.title('Salary Distribution by Region')
plt.show()

# Scatter plot with colors
sns.scatterplot(x='Age', y='Salary', hue='Region', data=df)
plt.title('Age vs Salary by Region')
plt.show()

Module 11: Business Intelligence Dashboards

Lesson 11.1: Dashboard Design Principles

KPI (Key Performance Indicator) Selection: Choose 3-5 most important metrics for target audience.

Visual Hierarchy: Most important metrics prominent; supporting details secondary.

Color Usage:

• Green = Good
• Yellow = Warning
• Red = Alert
• Avoid rainbow colors (accessibility)

Interactivity: Filters, drill-downs, date ranges for exploration.

Lesson 11.2: Building Dashboards with Power BI

Dashboard Components:

┌─────────────────────────────────────┐
│    Dashboard: Monthly Performance    │
├─────────────────────────────────────┤
│  Revenue: $2.5M    Growth: +15%     │
│  Customers: 15K    Churn: 2.3%      │
├────────────────────┬────────────────┤
│  Revenue Trend     │ Sales by Region│
│  (Line Chart)      │ (Pie Chart)    │
├────────────────────┼────────────────┤
│  Top Products      │ Forecast       │
│  (Bar Chart)       │ (Area Chart)   │
└────────────────────┴────────────────┘

---

WEEK 10: EXPLORATORY DATA ANALYSIS (EDA)

Module 12: Conducting Comprehensive EDA

Lesson 12.1: EDA Framework

Step 1: Understand Data Context

• What does data represent?
• How was data collected?
• What are obvious limitations?

Step 2: Explore Structure

• Rows and columns count
• Data types
• Missing values percentage

Step 3: Univariate Analysis Analyze individual variables.

python

# Numerical variables
df['Age'].describe()
plt.hist(df['Age'], bins=30)

# Categorical variables
df['Region'].value_counts()
df['Region'].value_counts().plot(kind='bar')

Step 4: Multivariate Analysis Analyze relationships between variables.

python

# Correlation between continuous variables
df[['Age', 'Salary', 'Experience']].corr()

# Relationship between categorical and continuous
df.groupby('Region')['Salary'].mean()
sns.boxplot(x='Region', y='Salary', data=df)

Step 5: Anomalies and Outliers Identify unusual patterns.

python

# Identify outliers
Q1 = df['Salary'].quantile(0.25)
Q3 = df['Salary'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['Salary'] < Q1 - 1.5*IQR) | 
              (df['Salary'] > Q3 + 1.5*IQR)]

# Flag suspicious patterns
df[df['Age'] > 100]  # Unrealistic ages
df[df['Salary'] < 0]  # Negative salaries

---

WEEK 11: PREDICTIVE ANALYTICS

Module 13: Introduction to Predictive Modeling

Lesson 13.1: Regression Analysis

Linear Regression - Predicting Continuous Values

Problem: Predict employee salary based on experience years

python

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import numpy as np

# Prepare data
X = df[['Years_Experience']].values
y = df['Salary'].values

# Split into training (80%) and testing (20%)
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.2, random_state=42
)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate
r2 = r2_score(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))

print(f"R² Score: {r2:.2f}")  # 0 to 1; higher is better
print(f"RMSE: ${rmse:.2f}")

Interpretation:

• R² = 0.85: Model explains 85% of salary variation
• RMSE = $5,000: Average prediction error is $5,000

Lesson 13.2: Classification - Predicting Categories

Problem: Predict if customer will churn (leave)

python

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd

# Prepare features and target
X = df[['Age', 'Tenure_Months', 'Monthly_Spend', 'Support_Tickets']]
y = df['Churned']  # 0 = No, 1 = Yes

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

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

# Predict
y_pred = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2%}")  # Percentage correct

# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)
# [[TN  FP]   True Negatives & False Positives
#  [FN  TP]]  False Negatives & True Positives

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WEEK 12: CAPSTONE PROJECT & CAREER PATHWAYS

Module 14: Capstone Analytics Project

Project: Retail Sales Analysis and Prediction

Scenario: You're hired as data analyst for online retail company. Your task: analyze sales patterns, identify high-value customers, forecast revenue, and recommend strategies.

Dataset Provided:

Columns: Date, Product, Region, Customer_Type, 
         Units_Sold, Unit_Price, Total_Sales, Customer_ID
Rows: 100,000+ transactions over 2 years

Part 1: Exploratory Analysis (Week 12, Days 1-2)

python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_csv('retail_sales.csv')
df['Date'] = pd.to_datetime(df['Date'])

# Basic exploration
print(f"Dataset shape: {df.shape}")
print(f"\nMissing values:\n{df.isnull().sum()}")
print(f"\nData types:\n{df.dtypes}")

# Statistical summary
print(df.describe())

# Analysis
print("\nTop 5 Products by Revenue:")
print(df.groupby('Product')['Total_Sales'].sum().sort_values(ascending=False).head())

print("\nSales by Region:")
print(df.groupby('Region')['Total_Sales'].sum().sort_values(ascending=False))

# Visualizations
fig, axes = plt.subplots(2, 2, figsize=(15, 10))

# Sales trend over time
daily_sales = df.groupby('Date')['Total_Sales'].sum()
axes[0, 0].plot(daily_sales)
axes[0, 0].set_title('Daily Sales Trend')

# Sales by region
df.groupby('Region')['Total_Sales'].sum().plot(kind='bar', ax=axes[0, 1])
axes[0, 1].set_title('Sales by Region')

# Product performance
df.groupby('Product')['Units_Sold'].sum().plot(kind='barh', ax=axes[1, 0])
axes[1, 0].set_title('Units Sold by Product')

# Customer type distribution
df.groupby('Customer_Type')['Total_Sales'].mean().plot(kind='bar', ax=axes[1, 1])
axes[1, 1].set_title('Average Sales by Customer Type')

plt.tight_layout()
plt.show()

Part 2: Customer Segmentation (Days 3-4)

python

# RFM Analysis: Recency, Frequency, Monetary
from datetime import datetime

reference_date = df['Date'].max() + timedelta(days=1)

rfm = df.groupby('Customer_ID').agg({
    'Date': lambda x: (reference_date - x.max()).days,  # Recency
    'Customer_ID': 'count',  # Frequency
    'Total_Sales': 'sum'  # Monetary
}).rename(columns={
    'Date': 'Recency',
    'Customer_ID': 'Frequency',
    'Total_Sales': 'Monetary'
})

# Score customers (1=worst, 5=best)
rfm['R_Score'] = pd.qcut(rfm['Recency'], 5, labels=[5,4,3,2,1])
rfm['F_Score'] = pd.qcut(rfm['Frequency'].rank(method='first'), 5, labels=[1,2,3,4,5])
rfm['M_Score'] = pd.qcut(rfm['Monetary'], 5, labels=[1,2,3,4,5])

rfm['RFM_Score'] = rfm['R_Score'].astype(int) + rfm['F_Score'].astype(int) + rfm['M_Score'].astype(int)

# Segment customers
def segment(score):
    if score >= 13: return 'Champions'
    elif score >= 10: return 'Loyal'
    elif score >= 7: return 'At Risk'
    else: return 'Lost'

rfm['Segment'] = rfm['RFM_Score'].apply(segment)

print("\nCustomer Segments:")
print(rfm['Segment'].value_counts())

Part 3: Revenue Forecasting (Days 5-6)

python

from sklearn.linear_model import LinearRegression
import numpy as np

# Prepare time series data
daily_revenue = df.groupby('Date')['Total_Sales'].sum().reset_index()
daily_revenue['Days_Since_Start'] = (daily_revenue['Date'] - daily_revenue['Date'].min()).dt.days

# Split into train/test
train_size = int(len(daily_revenue) * 0.8)
train = daily_revenue[:train_size]
test = daily_revenue[train_size:]

# Train model
X_train = train['Days_Since_Start'].values.reshape(-1, 1)
y_train = train['Total_Sales'].values

model = LinearRegression()
model.fit(X_train, y_train)

# Forecast next 30 days
future_days = np.arange(daily_revenue['Days_Since_Start'].max() + 1, 
                        daily_revenue['Days_Since_Start'].max() + 31).reshape(-1, 1)
forecast = model.predict(future_days)

print(f"30-Day Revenue Forecast: ${forecast.sum():,.2f}")
print(f"Average Daily Forecast: ${forecast.mean():,.2f}")

# Visualize
plt.figure(figsize=(12, 6))
plt.plot(daily_revenue['Date'], daily_revenue['Total_Sales'], label='Actual')
future_dates = [daily_revenue['Date'].max() + timedelta(days=i) for i in range(1, 31)]
plt.plot(future_dates, forecast, label='Forecast', linestyle='--')
plt.xlabel('Date')
plt.ylabel('Daily Revenue ($)')
plt.title('Revenue Forecast')
plt.legend()
plt.show()

Part 4: Insights and Recommendations (Day 7)

Present findings:

1. Top Performing Products: Product A and C drive 60% of revenue
2. High-Value Customers: Top 20% of customers generate 80% of revenue
3. Growth Opportunity: Region South underperforming; has potential
4. Forecast: Expected 8% revenue growth next month
5. Recommendations:
   • Expand Product A in Region South
   • Launch loyalty program for Champions segment
   • Implement retention campaign for At Risk customers
   • Optimize pricing in high-margin products

---

CAREER PATHWAYS AND ADVANCED TOPICS

Module 15: Career Development

Lesson 15.1: Data Analyst Career Paths

Entry-Level Analyst

• 0-2 years experience
• Focus: Data collection, cleaning, basic reporting
• Salary: ₹3-6 lakhs annually
• Skills: Excel, SQL, basic Python/R

Mid-Level Analyst

• 2-5 years experience
• Focus: Advanced analysis, dashboards, project leadership
• Salary: ₹6-12 lakhs
• Skills: SQL, Python, Power

  Course Overview and Learning Objectives

  What is Data Analytics?

  Definition: Data Analytics is the practice of examining raw data to discover patterns, draw conclusions, and support decision-making. It combines statistics, programming, and business acumen to transform data into actionable insights.

  Course Structure

  This comprehensive course is designed to take you from complete beginner to job-ready data analyst in 12 weeks through structured learning, hands-on projects, and real-world applications.

  Prerequisite: Basic computer literacy. No prior analytics or programming experience required.

  Learning Outcomes

  By completing this course, you will be able to:

  • Understand data analytics fundamentals and concepts
  • Use Excel for data analysis and visualization
  • Write SQL queries to extract and transform data
  • Use Python for data manipulation and analysis
  • Create compelling data visualizations
  • Develop analytics dashboards
  • Conduct exploratory data analysis
  • Build predictive models
  • Present insights to stakeholders
  • Execute end-to-end analytics projects

  ---

  WEEK 1-2: FOUNDATIONS OF DATA ANALYTICS

  Module 1: Data Analytics Fundamentals

  Lesson 1.1: Introduction to Data Analytics

  Key Concepts:

  Data Raw facts and observations. Examples: Sales transactions, customer demographics, website clicks.

  Information Data processed into meaningful context. Example: "Sales increased 15% in Q3 among 25-35 age group in North India."

  Insights Conclusions drawn from information that drive decisions. Example: "We should target 25-35 age group with Q4 campaign in North India for maximum ROI."

  The Analytics Process:

  Raw Data → Collection → Cleaning → Analysis → Visualization → 
  Insights → Action → Business Impact

  Lesson 1.2: Types of Analytics

  Descriptive Analytics - "What Happened?" Analyzing historical data to understand patterns.

  Example: "Average monthly sales over past 12 months"

  Tools: Excel, Power BI, Tableau

  Outcome: Reports, dashboards, summaries

  Diagnostic Analytics - "Why Did It Happen?" Investigating reasons behind outcomes.

  Example: "Why did sales increase 15% in Q3?"

  Techniques: Correlation analysis, trend analysis, root cause analysis

  Tools: SQL, Python, statistical software

  Predictive Analytics - "What Will Happen?" Using historical data to forecast future outcomes.

  Example: "Predict next month's sales based on historical trends"

  Techniques: Machine learning, regression, time series forecasting

  Tools: Python, R, specialized ML platforms

  Prescriptive Analytics - "What Should We Do?" Recommending actions to achieve desired outcomes.

  Example: "Allocate 40% budget to North India, 35% to South, 25% to East for maximum projected revenue"

  Techniques: Optimization, simulation, decision analysis

  Module 2: Data Types and Structures

  Lesson 2.1: Data Types

  Structured Data Organized in tables with rows and columns. Example: Customer database with columns (Name, Age, Email, Purchase Amount).

  Unstructured Data No predefined structure. Examples: Text documents, images, videos, audio.

  Semi-Structured Data Mix of structured and unstructured. Examples: JSON, XML, log files.

  Quantitative (Numerical) Data Measurable quantities. Examples: Age, salary, temperature, revenue.

  Categorical (Qualitative) Data Non-numeric categories. Examples: Color, gender, product category, region.

  Lesson 2.2: Data Distribution

  Normal Distribution Bell-shaped curve; most data points near mean. Many natural phenomena follow normal distribution.

  Skewed Distribution Asymmetrical. Positive skew (tail right), negative skew (tail left).

  Bimodal Distribution Two peaks. Suggests two distinct groups in data.

  Understanding distributions helps identify:

  • Outliers and anomalies
  • Appropriate statistical tests
  • Data transformation needs

  Module 3: Key Analytics Concepts

  Lesson 3.1: Statistical Foundations

  Mean (Average) Sum of values divided by count.

  Mean = (Sum of all values) / (Number of values)
  
  Example: (10 + 20 + 30 + 40 + 50) / 5 = 30

  Median Middle value when data sorted. Less affected by outliers.

  Example: 10, 20, 30, 40, 50
  Median = 30 (middle value)

  Mode Most frequently occurring value.

  Example: 10, 20, 20, 30, 30, 30
  Mode = 30 (occurs 3 times)

  Standard Deviation Measures spread/variability of data. High SD = data spread out; Low SD = data clustered.

  Variance Square of standard deviation. Measures how far data points are from mean.

  Correlation Relationship between two variables. Ranges from -1 to +1.

  Correlation = 1: Perfect positive relationship
  Correlation = 0: No relationship
  Correlation = -1: Perfect negative relationship

  Lesson 3.2: Sampling and Bias

  Sampling Selecting subset of data for analysis. Used when full dataset is too large.

  Sample Size Larger samples more accurately represent population. Balance between precision and resources.

  Bias Systematic error skewing results. Types: Selection bias, measurement bias, response bias.

  Mitigation: Random sampling, stratified sampling, careful data collection.

  ---

  WEEK 3-4: EXCEL FOR DATA ANALYSIS

  Module 4: Excel Fundamentals and Data Preparation

  Lesson 4.1: Excel Basics

  Spreadsheet Structure Rows (horizontal), columns (vertical), cells (intersections).

  Data Entry Best Practices:

  • One data point per cell
  • Consistent formatting
  • No merged cells in data ranges
  • Column headers in first row
  • No blank rows/columns within data

  Lesson 4.2: Formulas and Functions

  Mathematical Functions

  =SUM(A1:A10)          // Add values
  =AVERAGE(A1:A10)      // Calculate average
  =COUNT(A1:A10)        // Count cells with numbers
  =MIN(A1:A10)          // Find minimum
  =MAX(A1:A10)          // Find maximum

  Logical Functions

  =IF(A1>100, "High", "Low")        // Conditional logic
  =AND(A1>50, B1<100)               // All conditions true?
  =OR(A1=1, A1=2, A1=3)             // Any condition true?

  Text Functions

  =LEN(A1)              // Length of text
  =UPPER(A1)            // Convert to uppercase
  =CONCATENATE(A1, " ", B1)  // Combine text
  =LEFT(A1, 3)          // First 3 characters

  Lesson 4.3: Data Cleaning

  Common Data Quality Issues:

  Missing Values

  • Identify: Look for blank cells
  • Handle: Delete, fill with mean/median, leave as is depending on context

  Duplicate Records

  • Identify: Sort and visually inspect, or Data > Remove Duplicates
  • Handle: Remove duplicates or investigate root cause

  Inconsistent Formatting

  • Standardize text cases (UPPER, LOWER, PROPER functions)
  • Consistent date formats
  • Consistent number formats

  Outliers

  • Identify: Values significantly different from others
  • Verify: Confirm if genuine or data error
  • Handle: Keep if genuine, remove if error

  Module 5: Pivot Tables and Summarization

  Lesson 5.1: Creating Pivot Tables

  What is a Pivot Table? Summarizes large datasets into meaningful insights. Rotates (pivots) data dimensions.

  Example Scenario: Raw data: Sales transactions with Date, Region, Product, Amount

  Pivot table shows: Revenue by Region and Product

  Steps to Create:

  1. Select data range
  2. Insert > Pivot Table
  3. Drag fields to Rows, Columns, Values
  4. Customize as needed

  Lesson 5.2: Aggregation Functions in Pivot Tables

  SUM:        Total of values
  AVERAGE:    Mean value
  COUNT:      Number of items
  MIN/MAX:    Smallest/largest value
  STDEV:      Standard deviation

  Practical Example:

  Data: Sales by Salesperson, Month, Product
  
  Pivot Table Structure:
  ─────────────────────────────────────────
        Jan      Feb      Mar    Total
  ─────────────────────────────────────────
  John  $5,000   $6,000   $7,000  $18,000
  Jane  $4,500   $5,500   $6,200  $16,200
  Bob   $3,200   $3,800   $4,100  $11,100
  ─────────────────────────────────────────
  Total $12,700  $15,300  $17,300 $45,300

  ---

  WEEK 5-6: SQL FOR DATA ANALYSIS

  Module 6: SQL Fundamentals

  Lesson 6.1: Introduction to SQL

  What is SQL? Structured Query Language. Universal language for interacting with databases.

  Why SQL?

  • Access large datasets efficiently
  • Extract specific data subsets
  • Perform complex calculations
  • Join data from multiple tables

  Lesson 6.2: Basic SELECT Queries

  Selecting All Data:

  sql

  SELECT * FROM customers;

  Selecting Specific Columns:

  sql

  SELECT name, email, age FROM customers;

  Filtering with WHERE:

  sql

  SELECT * FROM customers
  WHERE age > 25 AND region = 'North India';

  Logical Operators:

  AND:  Both conditions true
  OR:   At least one condition true
  NOT:  Condition not true
  IN:   Value in list
  BETWEEN: Value in range
  LIKE: Pattern matching

  Lesson 6.3: Aggregation and Grouping

  Aggregate Functions:

  sql

  SELECT 
    COUNT(*) as total_customers,
    AVG(purchase_amount) as avg_purchase,
    SUM(purchase_amount) as total_revenue,
    MAX(purchase_amount) as max_purchase
  FROM orders;

  GROUP BY - Aggregate by Category:

  sql

  SELECT 
    region,
    COUNT(*) as customer_count,
    SUM(purchase_amount) as total_sales,
    AVG(purchase_amount) as avg_sale
  FROM orders
  GROUP BY region;

  HAVING - Filter Groups:

  sql

  SELECT 
    product_category,
    COUNT(*) as sales_count
  FROM orders
  GROUP BY product_category
  HAVING COUNT(*) > 50;

  Module 7: Advanced SQL

  Lesson 7.1: Joins

  INNER JOIN - Common Records

  sql

  SELECT 
    o.order_id,
    c.customer_name,
    o.purchase_amount
  FROM orders o
  INNER JOIN customers c
    ON o.customer_id = c.customer_id;

  LEFT JOIN - All from Left Table

  sql

  SELECT 
    c.customer_name,
    COUNT(o.order_id) as order_count
  FROM customers c
  LEFT JOIN orders o
    ON c.customer_id = o.customer_id
  GROUP BY c.customer_name;

  Lesson 7.2: Subqueries

  Subquery in WHERE Clause:

  sql

  SELECT * FROM customers
  WHERE customer_id IN (
    SELECT customer_id FROM orders
    WHERE purchase_amount > 10000
  );

  Subquery in FROM Clause:

  sql

  SELECT 
    region,
    avg_purchase
  FROM (
    SELECT 
      region,
      AVG(purchase_amount) as avg_purchase
    FROM orders
    GROUP BY region
  ) as regional_avg
  WHERE avg_purchase > 5000;

  Lesson 7.3: Window Functions

  Running Total:

  sql

  SELECT 
    date,
    amount,
    SUM(amount) OVER (
      ORDER BY date
    ) as running_total
  FROM sales
  ORDER BY date;

  Rank Within Groups:

  sql

  SELECT 
    salesperson,
    sales,
    RANK() OVER (ORDER BY sales DESC) as rank
  FROM sales_performance;

  ---

  WEEK 7-8: PYTHON FOR DATA ANALYSIS

  Module 8: Python Basics for Analytics

  Lesson 8.1: Python Setup and Libraries

  Essential Libraries:

  python

  # Data manipulation
  import pandas as pd
  import numpy as np
  
  # Visualization
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  # Statistical analysis
  from scipy import stats
  import statsmodels.api as sm
  
  # Machine learning (later weeks)
  from sklearn import preprocessing, ensemble, metrics

  Lesson 8.2: Working with Data in Pandas

  Creating DataFrames:

  python

  # From dictionary
  data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Salary': [50000, 60000, 75000]
  }
  df = pd.DataFrame(data)
  
  # From CSV
  df = pd.read_csv('customers.csv')
  
  # From Excel
  df = pd.read_excel('sales_data.xlsx')

  Exploring Data:

  python

  # First/last rows
  df.head()
  df.tail()
  
  # Dataset info
  df.info()           # Data types, missing values
  df.describe()       # Statistical summary
  df.shape            # Rows and columns
  
  # Check for missing values
  df.isnull().sum()

  Data Cleaning:

  python

  # Remove duplicates
  df = df.drop_duplicates()
  
  # Handle missing values
  df = df.dropna()                    # Remove rows with NaN
  df['Age'] = df['Age'].fillna(df['Age'].mean())  # Fill with mean
  
  # Remove outliers (values > 3 std devs)
  df = df[np.abs(stats.zscore(df['Salary'])) < 3]
  
  # Data type conversion
  df['Date'] = pd.to_datetime(df['Date'])
  df['Age'] = df['Age'].astype(int)

  Filtering and Selection:

  python

  # Filter rows
  high_earners = df[df['Salary'] > 60000]
  
  # Multiple conditions
  result = df[(df['Age'] > 25) & (df['Salary'] < 80000)]
  
  # Select columns
  selected = df[['Name', 'Salary']]
  
  # Select by location
  df.loc[0]           # First row
  df.iloc[0:5]        # First 5 rows
  df.loc[df['Age'] > 30, 'Name']  # Names of people over 30

  Module 9: Data Analysis with Python

  Lesson 9.1: Descriptive Statistics

  python

  # Mean, median, mode
  df['Salary'].mean()
  df['Salary'].median()
  df['Salary'].mode()
  
  # Spread
  df['Salary'].std()      # Standard deviation
  df['Salary'].var()      # Variance
  df['Salary'].min()
  df['Salary'].max()
  
  # Percentiles
  df['Salary'].quantile(0.25)  # 25th percentile
  df['Salary'].quantile(0.75)  # 75th percentile

  Lesson 9.2: Grouping and Aggregation

  python

  # Group by single column
  by_region = df.groupby('Region')['Salary'].mean()
  
  # Multiple aggregations
  summary = df.groupby('Department').agg({
      'Salary': ['mean', 'min', 'max'],
      'Age': 'mean',
      'Name': 'count'
  })
  
  # Group by multiple columns
  by_dept_region = df.groupby(['Department', 'Region'])['Salary'].sum()

  Lesson 9.3: Merging and Joining Data

  python

  # Merge DataFrames
  df_merged = pd.merge(df_customers, df_orders, 
                       on='customer_id', 
                       how='inner')
  
  # Concatenate
  df_combined = pd.concat([df1, df2], axis=0)
  
  # Join
  df_result = df1.join(df2, on='key')

  Lesson 9.4: Correlation and Statistical Testing

  python

  # Correlation matrix
  correlation = df.corr()
  
  # Correlation with specific column
  df.corr()['Salary'].sort_values(ascending=False)
  
  # Statistical test - t-test (comparing two groups)
  from scipy import stats
  group1 = df[df['Region'] == 'North']['Salary']
  group2 = df[df['Region'] == 'South']['Salary']
  t_stat, p_value = stats.ttest_ind(group1, group2)
  
  print(f"T-statistic: {t_stat}, P-value: {p_value}")
  if p_value < 0.05:
      print("Significant difference between regions")

  ---

  WEEK 8-9: DATA VISUALIZATION

  Module 10: Visualization Fundamentals

  Lesson 10.1: Visualization Principles

  Choose Right Chart Type:

  Line Chart - Show trends over time

  Sales Over 12 Months (ascending trend)

  Bar Chart - Compare categories

  Sales by Region (North > South > East)

  Pie Chart - Show parts of whole

  Market Share: Company A 40%, B 35%, C 25%

  Scatter Plot - Show relationship between two variables

  Age vs Salary (positive correlation)

  Histogram - Show distribution

  Customer age distribution (bell curve)

  Lesson 10.2: Visualization with Python

  Matplotlib - Basic Plotting:

  python

  import matplotlib.pyplot as plt
  
  # Line chart
  plt.figure(figsize=(10, 6))
  plt.plot(df['Month'], df['Sales'], marker='o')
  plt.xlabel('Month')
  plt.ylabel('Sales ($)')
  plt.title('Monthly Sales Trend')
  plt.grid(True)
  plt.show()
  
  # Bar chart
  plt.bar(df['Region'], df['Total_Sales'])
  plt.xlabel('Region')
  plt.ylabel('Sales ($)')
  plt.title('Sales by Region')
  plt.show()
  
  # Histogram
  plt.hist(df['Age'], bins=20)
  plt.xlabel('Age')
  plt.ylabel('Frequency')
  plt.title('Age Distribution')
  plt.show()

  Seaborn - Advanced Visualization:

  python

  import seaborn as sns
  
  # Heatmap showing correlations
  sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
  plt.title('Correlation Heatmap')
  plt.show()
  
  # Box plot by category
  sns.boxplot(x='Region', y='Salary', data=df)
  plt.title('Salary Distribution by Region')
  plt.show()
  
  # Scatter plot with colors
  sns.scatterplot(x='Age', y='Salary', hue='Region', data=df)
  plt.title('Age vs Salary by Region')
  plt.show()

  Module 11: Business Intelligence Dashboards

  Lesson 11.1: Dashboard Design Principles

  KPI (Key Performance Indicator) Selection: Choose 3-5 most important metrics for target audience.

  Visual Hierarchy: Most important metrics prominent; supporting details secondary.

  Color Usage:

  • Green = Good
  • Yellow = Warning
  • Red = Alert
  • Avoid rainbow colors (accessibility)

  Interactivity: Filters, drill-downs, date ranges for exploration.

  Lesson 11.2: Building Dashboards with Power BI

  Dashboard Components:

  ┌─────────────────────────────────────┐
  │    Dashboard: Monthly Performance    │
  ├─────────────────────────────────────┤
  │  Revenue: $2.5M    Growth: +15%     │
  │  Customers: 15K    Churn: 2.3%      │
  ├────────────────────┬────────────────┤
  │  Revenue Trend     │ Sales by Region│
  │  (Line Chart)      │ (Pie Chart)    │
  ├────────────────────┼────────────────┤
  │  Top Products      │ Forecast       │
  │  (Bar Chart)       │ (Area Chart)   │
  └────────────────────┴────────────────┘

  ---

  WEEK 10: EXPLORATORY DATA ANALYSIS (EDA)

  Module 12: Conducting Comprehensive EDA

  Lesson 12.1: EDA Framework

  Step 1: Understand Data Context

  • What does data represent?
  • How was data collected?
  • What are obvious limitations?

  Step 2: Explore Structure

  • Rows and columns count
  • Data types
  • Missing values percentage

  Step 3: Univariate Analysis Analyze individual variables.

  python

  # Numerical variables
  df['Age'].describe()
  plt.hist(df['Age'], bins=30)
  
  # Categorical variables
  df['Region'].value_counts()
  df['Region'].value_counts().plot(kind='bar')

  Step 4: Multivariate Analysis Analyze relationships between variables.

  python

  # Correlation between continuous variables
  df[['Age', 'Salary', 'Experience']].corr()
  
  # Relationship between categorical and continuous
  df.groupby('Region')['Salary'].mean()
  sns.boxplot(x='Region', y='Salary', data=df)

  Step 5: Anomalies and Outliers Identify unusual patterns.

  python

  # Identify outliers
  Q1 = df['Salary'].quantile(0.25)
  Q3 = df['Salary'].quantile(0.75)
  IQR = Q3 - Q1
  outliers = df[(df['Salary'] < Q1 - 1.5*IQR) | 
                (df['Salary'] > Q3 + 1.5*IQR)]
  
  # Flag suspicious patterns
  df[df['Age'] > 100]  # Unrealistic ages
  df[df['Salary'] < 0]  # Negative salaries

  ---

  WEEK 11: PREDICTIVE ANALYTICS

  Module 13: Introduction to Predictive Modeling

  Lesson 13.1: Regression Analysis

  Linear Regression - Predicting Continuous Values

  Problem: Predict employee salary based on experience years

  python

  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import r2_score, mean_squared_error
  import numpy as np
  
  # Prepare data
  X = df[['Years_Experience']].values
  y = df['Salary'].values
  
  # Split into training (80%) and testing (20%)
  X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
  )
  
  # Train model
  model = LinearRegression()
  model.fit(X_train, y_train)
  
  # Make predictions
  y_pred = model.predict(X_test)
  
  # Evaluate
  r2 = r2_score(y_test, y_pred)
  rmse = np.sqrt(mean_squared_error(y_test, y_pred))
  
  print(f"R² Score: {r2:.2f}")  # 0 to 1; higher is better
  print(f"RMSE: ${rmse:.2f}")

  Interpretation:

  • R² = 0.85: Model explains 85% of salary variation
  • RMSE = $5,000: Average prediction error is $5,000

  Lesson 13.2: Classification - Predicting Categories

  Problem: Predict if customer will churn (leave)

  python

  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score, confusion_matrix
  import pandas as pd
  
  # Prepare features and target
  X = df[['Age', 'Tenure_Months', 'Monthly_Spend', 'Support_Tickets']]
  y = df['Churned']  # 0 = No, 1 = Yes
  
  # Split data
  X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2
  )
  
  # Train model
  model = RandomForestClassifier(n_estimators=100)
  model.fit(X_train, y_train)
  
  # Predict
  y_pred = model.predict(X_test)
  
  # Evaluate
  accuracy = accuracy_score(y_test, y_pred)
  print(f"Accuracy: {accuracy:.2%}")  # Percentage correct
  
  # Confusion matrix
  cm = confusion_matrix(y_test, y_pred)
  print(cm)
  # [[TN  FP]   True Negatives & False Positives
  #  [FN  TP]]  False Negatives & True Positives

  ---

  WEEK 12: CAPSTONE PROJECT & CAREER PATHWAYS

  Module 14: Capstone Analytics Project

  Project: Retail Sales Analysis and Prediction

  Scenario: You're hired as data analyst for online retail company. Your task: analyze sales patterns, identify high-value customers, forecast revenue, and recommend strategies.

  Dataset Provided:

  Columns: Date, Product, Region, Customer_Type, 
           Units_Sold, Unit_Price, Total_Sales, Customer_ID
  Rows: 100,000+ transactions over 2 years

  Part 1: Exploratory Analysis (Week 12, Days 1-2)

  python

  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  # Load data
  df = pd.read_csv('retail_sales.csv')
  df['Date'] = pd.to_datetime(df['Date'])
  
  # Basic exploration
  print(f"Dataset shape: {df.shape}")
  print(f"\nMissing values:\n{df.isnull().sum()}")
  print(f"\nData types:\n{df.dtypes}")
  
  # Statistical summary
  print(df.describe())
  
  # Analysis
  print("\nTop 5 Products by Revenue:")
  print(df.groupby('Product')['Total_Sales'].sum().sort_values(ascending=False).head())
  
  print("\nSales by Region:")
  print(df.groupby('Region')['Total_Sales'].sum().sort_values(ascending=False))
  
  # Visualizations
  fig, axes = plt.subplots(2, 2, figsize=(15, 10))
  
  # Sales trend over time
  daily_sales = df.groupby('Date')['Total_Sales'].sum()
  axes[0, 0].plot(daily_sales)
  axes[0, 0].set_title('Daily Sales Trend')
  
  # Sales by region
  df.groupby('Region')['Total_Sales'].sum().plot(kind='bar', ax=axes[0, 1])
  axes[0, 1].set_title('Sales by Region')
  
  # Product performance
  df.groupby('Product')['Units_Sold'].sum().plot(kind='barh', ax=axes[1, 0])
  axes[1, 0].set_title('Units Sold by Product')
  
  # Customer type distribution
  df.groupby('Customer_Type')['Total_Sales'].mean().plot(kind='bar', ax=axes[1, 1])
  axes[1, 1].set_title('Average Sales by Customer Type')
  
  plt.tight_layout()
  plt.show()

  Part 2: Customer Segmentation (Days 3-4)

  python

  # RFM Analysis: Recency, Frequency, Monetary
  from datetime import datetime
  
  reference_date = df['Date'].max() + timedelta(days=1)
  
  rfm = df.groupby('Customer_ID').agg({
      'Date': lambda x: (reference_date - x.max()).days,  # Recency
      'Customer_ID': 'count',  # Frequency
      'Total_Sales': 'sum'  # Monetary
  }).rename(columns={
      'Date': 'Recency',
      'Customer_ID': 'Frequency',
      'Total_Sales': 'Monetary'
  })
  
  # Score customers (1=worst, 5=best)
  rfm['R_Score'] = pd.qcut(rfm['Recency'], 5, labels=[5,4,3,2,1])
  rfm['F_Score'] = pd.qcut(rfm['Frequency'].rank(method='first'), 5, labels=[1,2,3,4,5])
  rfm['M_Score'] = pd.qcut(rfm['Monetary'], 5, labels=[1,2,3,4,5])
  
  rfm['RFM_Score'] = rfm['R_Score'].astype(int) + rfm['F_Score'].astype(int) + rfm['M_Score'].astype(int)
  
  # Segment customers
  def segment(score):
      if score >= 13: return 'Champions'
      elif score >= 10: return 'Loyal'
      elif score >= 7: return 'At Risk'
      else: return 'Lost'
  
  rfm['Segment'] = rfm['RFM_Score'].apply(segment)
  
  print("\nCustomer Segments:")
  print(rfm['Segment'].value_counts())

  Part 3: Revenue Forecasting (Days 5-6)

  python

  from sklearn.linear_model import LinearRegression
  import numpy as np
  
  # Prepare time series data
  daily_revenue = df.groupby('Date')['Total_Sales'].sum().reset_index()
  daily_revenue['Days_Since_Start'] = (daily_revenue['Date'] - daily_revenue['Date'].min()).dt.days
  
  # Split into train/test
  train_size = int(len(daily_revenue) * 0.8)
  train = daily_revenue[:train_size]
  test = daily_revenue[train_size:]
  
  # Train model
  X_train = train['Days_Since_Start'].values.reshape(-1, 1)
  y_train = train['Total_Sales'].values
  
  model = LinearRegression()
  model.fit(X_train, y_train)
  
  # Forecast next 30 days
  future_days = np.arange(daily_revenue['Days_Since_Start'].max() + 1, 
                          daily_revenue['Days_Since_Start'].max() + 31).reshape(-1, 1)
  forecast = model.predict(future_days)
  
  print(f"30-Day Revenue Forecast: ${forecast.sum():,.2f}")
  print(f"Average Daily Forecast: ${forecast.mean():,.2f}")
  
  # Visualize
  plt.figure(figsize=(12, 6))
  plt.plot(daily_revenue['Date'], daily_revenue['Total_Sales'], label='Actual')
  future_dates = [daily_revenue['Date'].max() + timedelta(days=i) for i in range(1, 31)]
  plt.plot(future_dates, forecast, label='Forecast', linestyle='--')
  plt.xlabel('Date')
  plt.ylabel('Daily Revenue ($)')
  plt.title('Revenue Forecast')
  plt.legend()
  plt.show()

  Part 4: Insights and Recommendations (Day 7)

  Present findings:

  1. Top Performing Products: Product A and C drive 60% of revenue
  2. High-Value Customers: Top 20% of customers generate 80% of revenue
  3. Growth Opportunity: Region South underperforming; has potential
  4. Forecast: Expected 8% revenue growth next month
  5. Recommendations:
     • Expand Product A in Region South
     • Launch loyalty program for Champions segment
     • Implement retention campaign for At Risk customers
     • Optimize pricing in high-margin products

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  CAREER PATHWAYS AND ADVANCED TOPICS

  Module 15: Career Developmentgf

  Lesson 15.1: Data Analyst Career Paths

  Entry-Level Analyst

  • 0-2 years experience
  • Focus: Data collection, cleaning, basic reporting
  • Salary: ₹3-6 lakhs annually
  • Skills: Excel, SQL, basic Python/R

  Mid-Level Analyst

  • 2-5 years experience
  • Focus: Advanced analysis, dashboards, project leadership
  • Salary: ₹6-12 lakhs
  • Skills: SQL, Python, Power