Algorithms and Programming

Algorithms and Programming

1. Code Analysis

   • Predict Code Functionality
     • Look at the inputs and figure out the outputs. If the input is 5, what will be printed on the screen? Walk through the code line by line. Ask yourself, "What happens here? What changes?"
   • Trace Code Execution
     • Imagine being the computer. Walk step-by-step through the code, keeping track of variable values as they change. For example, follow the value of x through a loop. Write down the value of x after each iteration.
   • Common Errors
     • Logic Errors: Logic Errors: The program runs but gives the wrong answer.
     • Syntax Errors: "These are 'grammar' mistakes in the code, like missing commas or parentheses."
     • Runtime Errors: "These happen while the program is running. Divide-by-zero errors are common here."

2. Algorithm Design

   • Problem solving process
     • Start with a big problem and break it into smaller steps. Tip: "Use pseudocode first. Write the logic before worrying about the syntax."
   • Control Structures
     • Run instructions in order. Use if/else to make decisions in your program. Use loops to repeat tasks. These can be FOR or WHILE loops.
   • Procedures (Functions)
     • A procedure is reusable code that does a specific task. Example: A calculate_total(price, tax) procedure can be reused whenever you want to calculate the total price.
       • Exam Tip: Procedures will be in the multiple-choice and Create Task. Always ask yourself: does it have inputs (parameters) and does it give back a result (return)?

3. Abstract Data Structures

   • Lists(Arrays)
     • A list is like a shopping list where each item has a position (index). Example: fruits = ["apple", "banana", "cherry"] — To get "banana", use fruits[2].
       • AP CSP counts positions starting from 1, not 0 like some other languages.
   • Dictionaries (key-value pairs)
     • Think of a dictionary as a real dictionary — you look up a word (the key) to get the definition (the value). Example: student = {"name": "Alice", "age": 16} — To get the student’s age, use student["age"].
   • Sets
     • A set is like a bag of unique items. No duplicates allowed. If you want to find unique student names from a list, a set will remove duplicates for you."

4. Searching and Sorting Algorithms

   • You’ll be asked about how data is searched and sorted. If you know the logic behind linear search, binary search, and sorting, you’ll ace these questions
   • Searching
   • Sorting
     • Linear Search: "Go item-by-item until you find what you’re looking for. It’s like looking for a sock in a messy drawer."
     • Binary Search: "If the list is sorted, divide it in half and search in one half at a time."
     • Exam Tip: "Binary search is faster than linear search but only works on sorted lists."