Welcome, future business leader! You’re about to take your first step into a larger world of data-driven decision-making. In today’s business landscape, the ability to understand and leverage data is not just a technical skill—it’s a core competency for effective management. This course is designed specifically for MBA students like you. We’ll skip the abstract computer science jargon and focus on one thing: using Python as a powerful tool to solve real-world business problems.
You don’t need any prior coding experience. We’ll start from zero and build your skills step-by-step. By the end of this 50-day journey, you’ll be able to manipulate data, generate insights, and even build predictive models.
Before you begin, please follow the setup instructions in the main README.md at the root of this repository. This will guide you through:
requirements.txt.Once you have completed those steps and activated your virtual environment, you are ready to start this lesson.
Let’s explore the code for today’s lesson. The script for this lesson is helloworld.py.
Day_01_Introduction/helloworld.py in your code editor. You will see that the code is now organized into functions, which is a best practice for writing clean and reusable code.Coding-For-MBA folder) and your virtual environment is active. Then, execute the script by running:
python Day_01_Introduction/helloworld.py
You will see the output of the business calculations printed to your console.
The exercises are designed to help you practice the fundamental concepts introduced in the script.
Company Introduction:
my_solutions.py in the Day_01_Introduction folder.print() function to introduce a fictional company.print("Welcome to InnovateCorp Analytics")Quarterly Sales Calculation:
print() function to calculate and display the total annual sales. (Hint: you can do math right inside the print statement: print(110000 + 120000 + ...)).Checking Data Types in Business:
type() function to check the data types of the following business-related data points.
1500 (e.g., number of units sold)1500.75 (e.g., a price or a financial metric)'InnovateCorp' (e.g., a company name)True (e.g., is the product in stock?)🎉 Congratulations! You’ve just run your first refactored Python script and are on your way to becoming a data-savvy leader.
Run this lesson’s code interactively in your browser:
!!! tip “About JupyterLite” JupyterLite runs entirely in your browser using WebAssembly. No installation or server required! Note: First launch may take a moment to load.
???+ example “helloworld.py” View on GitHub
```python title="helloworld.py"
"""
Day 1: Python for Business Analytics - First Steps (Refactored)
This script demonstrates basic Python concepts using business-relevant examples.
We will perform a simple profit calculation and check the types of various
business-related data points. This version is refactored to use functions.
"""
def calculate_gross_profit(revenue, cogs):
"""Calculates the gross profit from revenue and COGS."""
return revenue - cogs
def calculate_gross_profit_margin(gross_profit, revenue):
"""Calculates the gross profit margin."""
if revenue == 0:
return 0
return (gross_profit / revenue) * 100
def display_business_analytics(revenue, cogs):
"""Calculates and displays key business metrics."""
print("Welcome to the Quarterly Business Review Dashboard")
print()
gross_profit = calculate_gross_profit(revenue, cogs)
gross_profit_margin = calculate_gross_profit_margin(gross_profit, revenue)
print(f"Total Revenue: ${revenue}")
print(f"Cost of Goods Sold: ${cogs}")
print(f"Gross Profit: ${gross_profit}")
print()
print(f"Gross Profit Margin: {gross_profit_margin:.2f}%")
print("-" * 20)
def display_data_types():
"""Displays the types of various business-related data points."""
print("Checking the types of some common business data points:")
units_sold = 1500
product_price = 49.99
company_name = "InnovateCorp"
is_in_stock = True
quarterly_sales = [110000, 120000, 135000, 140000]
print(f"Data: {units_sold}, Type: {type(units_sold)}")
print(f"Data: {product_price}, Type: {type(product_price)}")
print(f"Data: '{company_name}', Type: {type(company_name)}")
print(f"Data: {is_in_stock}, Type: {type(is_in_stock)}")
print(f"Data: {quarterly_sales}, Type: {type(quarterly_sales)}")
if __name__ == "__main__":
# --- Basic Business Calculations ---
revenue_main = 500000
cogs_main = 350000
display_business_analytics(revenue_main, cogs_main)
# --- Understanding Data Types in a Business Context ---
display_data_types()
```
???+ example “solutions.py” View on GitHub
```python title="solutions.py"
"""
Day 1: Solutions to Exercises
"""
# --- Exercise 1: Company Introduction ---
print("--- Solution to Exercise 1 ---")
print("Welcome to InnovateCorp Analytics")
print("-" * 20)
# --- Exercise 2: Quarterly Sales Calculation ---
print("--- Solution to Exercise 2 ---")
# The calculation is done directly inside the print function.
print("Total Annual Sales:")
print(110000 + 120000 + 135000 + 140000)
print("-" * 20)
# --- Exercise 3: Checking Data Types in Business ---
print("--- Solution to Exercise 3 ---")
# Using the type() function to inspect each data point.
print("Data point: 1500, Type:", type(1500))
print("Data point: 1500.75, Type:", type(1500.75))
print("Data point: 'InnovateCorp', Type:", type("InnovateCorp"))
print("Data point: True, Type:", type(True))
print("-" * 20)
```