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B 3.1.3 Worksheet Python Answers
Part 1: Definitions Fill in the blanks to complete the following definitions: Instance attributes in Python are declared inside the __init__ method using self.attribute_name = value. Each object of the...
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B 3.1.4 Worksheet Java
Example 1: Consider a class named Dog. A dog has attributes like name (String), breed (String), and age (int). Task 1: Define the Dog class in Java. Task 2: Instantiate two Dog objects in a main method...
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B 3.1.4 Worksheet Python
Example 1: Consider a class named Dog. A dog has attributes like name (str), breed (str), and age (int). Task 1: Define the Dog class in Python with an __init__ method. Task 2: Instantiate two Dog objects....
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B 3.1.4 Worksheet Java Answers
Explanation: For example let's choose the real-world object: Rectangle 3. Create a constructor for your class that takes initial values for all the attributes: 4. Instantiate at least two objects of your...
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B 3.1.4 Worksheet Python Answers
Explanation: Let's choose the real-world object: Rectangle Instantiate at least two objects of your class:...
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B 3.1.5 Worksheet Java
Worksheet 1: Understanding Access Modifiers Instructions: Consider the following incomplete Java class definition for a BankAccount. Fill in the blanks with the appropriate access modifiers (public or...
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B 3.1.5 Worksheet Python
Worksheet 1: Understanding Visibility Conventions Instructions: Consider the following incomplete Python class definition for a Book. Indicate the intended visibility (public, protected, private-like)...
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B2 Programming
This section focuses on transforming algorithms into code. Key areas include fundamentals (variables, data types, substrings, exceptions), data structures (arrays, lists, stacks, queues), programming...
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A 3.4.4 Distributed databases (HL Only)
Explore distributed databases. Learn key features: concurrency control, consistency, partitioning, security, transparency, fault tolerance, global queries, replication, scalability. Understand ACID for...
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A 3.4.3 OLAP and data mining (HL Only)
Uncover hidden insights in massive datasets. This section explores key data mining techniques: classification, clustering, regression, association rules, sequential patterns, and anomaly detection. Learn...