ISM4641: Python for Business Analytics

Lecture 1: Introduction to Python for Business

Dr. Smith

2025-08-25

Introduction

Today’s Objectives

• Understand why Python is a crucial tool for modern business analytics.
• Get comfortable with Google Colab, our cloud-based coding environment, including notebook cells and basic Markdown formatting.
• Learn how to make Notebooks looks great using Markdown
• Grasp the fundamental building blocks of Python: variables, data types, operators, and type conversion.
• Write your first lines of Python code and experience the thrill of telling a computer what to do! (well, maybe it’s not a thrill, but useful skill)

The Case for Python

Why Should a Business Student Learn to Code?

The answer is simple: data is everywhere, and the ability to understand and use it is no longer optional, it’s essential.

1. Data is the New Oil, and Python is the Refinery:

2. Competitiveness Demands More Than Just Spreadsheets:

3. Industry Trends Point to One Thing -> Automation and AI:

Imagine the Possibilities

• Marketing: Optimize campaigns, target customer segments, personalize experiences.
• Finance: Forecast sales, assess risks, optimize portfolios.
• Operations: Streamline supply chains, identify bottlenecks, predict maintenance needs.
• Human Resources: Find top talent, improve retention, build diverse teams.

Why Python?

• Versatile: Like a Swiss Army knife, it handles many tasks.
• Easy to Learn: Readable and intuitive syntax.
• Treasure Chest of Libraries: Pre-built modules (Pandas, NumPy) for data analysis.
• Large Community: Tons of resources and support.
• Industry Standard: Employers love it!

Python: The Perfect Tool for Business

• Analyze customer data to identify trends and preferences.
• Build financial models to forecast revenue and manage risk.
• Optimize operations by analyzing supply chain data.
• Automate repetitive tasks to improve efficiency.
• Create compelling data visualizations to communicate insights effectively.

Google Colab

Your Cloud-Based Coding Playground

• Zero Setup: Runs in your browser.
• Free Power: Access to Google’s computing resources (GPUs).
• Teamwork Made Easy: Real-time collaboration.
• Everything in One Place: Jupyter Notebooks combine code, output, and text.

Getting Started with Colab

Following along with the professor...

1. Open Colab: colab.research.google.com
2. Sign in with your Google account.
3. Create a “New notebook”.

Navigating Colab

• Notebook Cells:
  • Code Cells: Write and execute Python code.
  • Text Cells: Add explanations using Markdown.
• Running Code: Click the “Play” button or press Shift + Enter.
• Adding Cells: Use the “+ Code” and “+ Text” buttons.

Common Shortcuts

• Run cell (Ctrl + Enter)
• Run cell and add new cell below (Alt + Enter)
• Run cell and goto the next cell below (Shift + Enter)
• Comment current line (Ctrl + /)

• add a cell above (Ctrl + M + A)
• Add a cell below (Ctrl + M + B)
• Change cell to code (Ctrl + M + Y)
• Change cell to markdown (Ctrl + M + M)

Markdown: Making Your Notebooks Look Good

• Headings: #, ##, ###
• Emphasis:
  • Italic: *text* or _text_
  • Bold: **text** or __text__
• Lists:
  • Unordered: * Item
  • Ordered: 1. Item
• Links: [Text](URL)
• Code:
  • Inline: `code`
  • Block: ```{python} # code here ```

The Building Blocks of Python

What we will cover today:

• Variables: Labeled containers for data.
• Data Types: The kind of information a variable holds (text, numbers, booleans).
• Operators: Symbols to perform operations on data (math, comparison, logic).
• Type Conversion: Changing data types.
• Comments: Explaining your code to humans.

Variables: Giving Names to Your Data

company_name = "TechCorp Inc."  
number_of_employees = 500      
revenue = 25.5                 
is_profitable = True      

• Use the assignment operator (=) to assign values to variables.
• Choose descriptive variable names.

Once a variable is defined, you can use it later in your program. Here we use the print function to display the contents of each variable.

print(company_name, number_of_employees, revenue, is_profitable)

TechCorp Inc. 500 25.5 True

We will see the print function being used more in later classes. For now, note that you can provide not only variables, but text.

print("Company Name =", company_name, ": Number of Empoyees =", number_of_employees, ": Total Revenue =", revenue, ": Is Profitable is", is_profitable)

Company Name = TechCorp Inc. : Number of Empoyees = 500 : Total Revenue = 25.5 : Is Profitable is True

For very long lines of code, we can specify to continue on the next line as folllows:

print("Company Name =", \
    company_name, \
    ": Number of Empoyees =", \
    number_of_employees, \
    ": Total Revenue =", \
    revenue, \
    ": Is Profitable is", \
    is_profitable)

Company Name = TechCorp Inc. : Number of Empoyees = 500 : Total Revenue = 25.5 : Is Profitable is True

Why are Variables Useful?

• Organization: Make your code easier to read.
• Reusability: Refer to values multiple times.
• Flexibility: Easily change values later.

Rules for Naming Variables

• Start with a letter or underscore.
• The rest can be letters, underscores, or numbers.
• Case-sensitive (myVar is different from myvar).
• Cannot use Python keywords (e.g., if, for, while).

Data Types: What are you storing?

• String (str): Text (e.g., "Hello", 'ISM4641').
• Integer (int): Whole numbers (e.g., 10, -5, 0).
• Float (float): Decimal numbers (e.g., 3.14, -2.7).
• Boolean (bool): True or False.

product_name = "Laptop"     # String
quantity_in_stock = 100     # Integer
price_per_unit = 999.99     # Float
is_in_stock = True          # Boolean

Checking Data Types

Python has a built-in function called type that we can use to identify the data type of a variable.

. . .

print(type(product_name))
print(type(quantity_in_stock))
print(type(price_per_unit))
print(type(is_in_stock))

<class 'str'>
<class 'int'>
<class 'float'>
<class 'bool'>

Why are Data Types Important?

• Correct Operations: Python needs to know the type to perform the right operations.
• Memory Management: Different types use different amounts of memory.
• Data Integrity: Ensures your data is stored and processed accurately.

Operators: Doing Things with Data

• Arithmetic Operators: Math (+, -, *, /, //, %, **).
• Comparison Operators: Comparisons (==, !=, >, <, >=, <=).
• Logical Operators: Combine conditions (and, or, not).

Arithmetic Operators

x = 10
y = 3

addition = x + y        # 13
subtraction = x - y     # 7
multiplication = x * y  # 30
division = x / y        # 3.3333...
floor_division = x // y # 3
modulus = x % y        # 1
exponentiation = x ** y # 1000

Comparison Operators

a = 5
b = 2

equal_to = a == b                 # False
not_equal_to = a != b             # True
greater_than = a > b              # True
less_than = a < b                 # False
greater_than_or_equal_to = a >= b # True
less_than_or_equal_to = a <= b    # False

Logical Operators

p = True
q = False

logical_and = p and q    # False
logical_or = p or q     # True
logical_not = not p    # False

Business Scenario: Discount Eligibility

order_total = 120
is_member = True

is_eligible_for_discount = order_total > 100 and is_member

print(is_eligible_for_discount)  # Output: True

True

Type Conversion: Changing Data Types

• int(): Converts to an integer.
• float(): Converts to a float.
• str(): Converts to a string.

quantity_string = "5"
quantity_int = int(quantity_string)

price_int = 10
price_float = float(price_int)

total_cost = 99.99
total_cost_string = str(total_cost)

Why is Type Conversion Important?

• Performing Operations: Can’t add a string and a number directly.
• Data Input: User input often comes as strings.
• Data Compatibility: Ensures different parts of your program work together.

Comments: Explaining your code

• Single-line comments: # This is a comment
• Multi-line comments:

"""
This is a
multi-line
comment
"""

• Clarity: Make your code easier to understand.
• Debugging: Help track down errors.
• Collaboration: Essential for teamwork.

Your Turn!

Practice Exercises!

Open a new notebook in Google Colab and try these exercises.

Exercise 1: Customer Profile

Create variables for as persons name, their age, the city they live in, and if there are a member:

. . .

• Name: “Alice Smith”
• Age: 30
• City: “New York”
• Is a member: True

. . .

Print the values and their data types.

Exercise 2: Order Calculation

Calculate the total cost given price per item, quantity, and discount:

. . .

• Price per item: $25.50
• Quantity: 5
• Discount: 10% (0.10)

. . .

Print the total cost after the discount.

Exercise 3: Shipping Eligibility

Determine if eligible for free shipping given based on the the rule that members with orders over $50 get a discount:

. . .

• Order total > $50
• Customer is a member

. . .

Print True if eligible, False otherwise.

Exercise 4: Markdown Practice

Create a text cell with:

• Main heading: “My Business Analytics Notes”
• Subheading: “Class 1: Introduction to Python”
• Bulleted list: Variables, Data Types, Operators
• Bold: “Python is essential for data analysis.”
• Italic: Data drives business decisions.

Exercise 5: Type Conversion

• purchase_total_str = "125.75"
• Convert to a float: purchase_total_float
• Add shipping cost of $5.99
• Print the final total.

Conclusion

• You’ve taken your first steps into Python for business analytics!
• We covered variables, data types, operators, type conversion and comments in Google Colab.
• Next class: Conditional statements and loops.
• Get ready to unlock the power of data!

Tasks for This Week

• Practice, practice, practice
  • learning to program is like learning a language, or playing a sport
    • you can NOT cram the night before you need the skill!
    • It’s not hard if you do a little everyday - make it a habbit
  • Practice using the problem set for this week.
    • Try to complete each question before looking at the solution
  • Join extra help sessions on Saturday’s (1 to 4pm)
• Complete Assignment01
  • Simple assignment to help you get started
• Prepare for Quiz01 next class
  • In-class quiz covering the content from this week (Week01)

Questions?