Area 2.1

Standard Data Types

Master the four fundamental data types in programming: integer, float, string, and Boolean. Learn when and how to use each type effectively in your programs.

Data Types

Activities

~1.5hrs

Study Time

Learning Objectives

Understand the four standard data types: integer, float, string, and Boolean
Know the appropriate usage contexts for each data type
Apply data type selection based on the nature of data being stored
Recognize how different data types are represented in memory
Identify common errors related to incorrect data type usage

Standard Data Types

Integer

Whole numbers without decimal points, both positive and negative

Examples:

42-170999

Common Uses:

Counting items, array indices, loop counters, age, quantity

Memory Note:

Typically 32 or 64 bits, exact range depends on system

Python Examples:

age = 18
count = 0
temperature = -5
score = 100

Float

Numbers with decimal points, used for precise calculations

Examples:

3.14-2.50.001123.45

Common Uses:

Measurements, calculations, percentages, coordinates, prices

Memory Note:

Usually 64-bit double precision, limited precision for very large/small numbers

Python Examples:

price = 19.99
pi = 3.14159
percentage = 75.5
coordinate = -12.345

String

Sequences of characters including letters, numbers, and symbols

Examples:

"Hello""123 Main St""user@email.com"""

Common Uses:

Names, addresses, messages, file paths, user input, display text

Memory Note:

Variable length, each character typically 1-4 bytes depending on encoding

Python Examples:

name = "Alice Smith"
email = "alice@example.com"
message = "Welcome to the system!"
empty_string = ""

Boolean

Logical values representing true or false states

Examples:

TrueFalse

Common Uses:

Flags, conditions, status indicators, yes/no questions, switches

Memory Note:

Usually 1 byte, though may be optimized to 1 bit in arrays

Python Examples:

is_student = True
game_over = False
logged_in = True
has_permission = False

Common Mistakes & Solutions

Using strings for numbers

Example:

age = "25" instead of age = 25

Problem:

Cannot perform mathematical operations

Solution:

Use integer or float for numeric data

Mixing data types in calculations

Example:

"Age: " + 25 without conversion

Problem:

Type error - cannot add string and integer

Solution:

Convert types: "Age: " + str(25)

Using float for counting

Example:

Using 5.0 instead of 5 for loop counter

Problem:

Unnecessary precision and potential precision errors

Solution:

Use integers for discrete counting

Incorrect Boolean comparisons

Example:

if is_ready == True instead of if is_ready

Problem:

Redundant comparison, less readable

Solution:

Boolean variables can be used directly in conditions

Learning Activities

Data Type Selection Challenge

Decision

25 minutes

Given various real-world data scenarios, choose the most appropriate data type

Type Error Debugging

Debug

35 minutes

Identify and fix common data type errors in Python code samples

Data Type Conversion Practice

Practice

40 minutes

Convert between different data types and understand when conversion is needed

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