Compsci 1 & Computational Thinking Study Guide PDF

please create a practice test for compsci 1 and computational thinking. I also need a study guide, here is a prac test my professor gave me. This is probably not all of the material he will cover in the test but please help , i have some additional notes and such with concepts and topics from Python...

please create a practice test for compsci 1 and computational thinking. I also need a study guide, here is a prac test my professor gave me. This is probably not all of the material he will cover in the test but please help , i have some additional notes and such with concepts and topics from Python for everyone. thank you! Expanded Study Guide for Computational Thinking with Python Here’s an expanded version of the guide with more in-depth explanations and additional complex scenarios that could come up in your exam. I'll also explain some advanced concepts to help you tackle potential trickier questions or code examples. Key Concepts Computational Thinking: Computational thinking is the process of thinking in a way that can be represented through computation. It's essential for problem-solving and is the foundation for programming. Here’s a breakdown of its components: Decomposition: Breaking a large problem into smaller, manageable tasks. For example: Problem: "Calculate the average score of students." Decompose: Collect all students' scores. Sum the scores. Count the number of students. Divide the sum by the number of students to get the average. Pattern Recognition: Identifying patterns that make a problem easier to solve. For example, when solving a series of mathematical operations, we can recognize that addition and multiplication follow predictable rules. Abstraction: Focusing on essential details and ignoring irrelevant information. For example: You can abstract the process of sorting a list without worrying about how the algorithm is implemented in detail. Just know that sorting organizes data in a certain order. Algorithm Design: The step-by-step process to solve a problem. Algorithms are written as code and are at the core of computational thinking. Python Programming Basics 1. Instructions for solving problems must be: Unambiguous: Make sure the instructions leave no room for confusion. For example: Ambiguous: "Find the result." Clear: "Multiply 5 and 10, then subtract 3." Verifiable: A solution should be testable. For example, if you say the result is 50, you should be able to run the program and confirm that the result is indeed 50. Python Syntax & Common Constructs 2. Expression: An expression is any combination of values, variables, operators, and functions that can be evaluated. Example: x + 5 * 2 is an expression where x is a variable, and +, * are operators. 3. Logical Operators: not: Used to reverse the truth value. python Copy code not True # Returns False and: Returns True only if both sides are True. python Copy code True and False # Returns False or: Returns True if at least one side is True. python Copy code True or False # Returns True Control Flow 4. Conditional Execution (If-Else): Conditional execution allows you to execute blocks of code based on whether a condition is True or False. If-Else Example: python Copy code age = 20 if age >= 18: print("Adult") else: print("Child") Elif: For multiple conditions: python Copy code age = 20 if age < 13: print("Child") elif age < 20: print("Teenager") else: print("Adult") 5. Exceptions: Python has built-in exceptions to handle errors without crashing the program. Use try and except blocks. Example: Handling a division by zero: python Copy code try: result = 10 / 0 except ZeroDivisionError: print("Cannot divide by zero") 6. Loops While Loop: Repeats a block of code as long as the condition is True. python Copy code i = 0 while i < 5: print(i) i += 1 For Loop: Iterates over a sequence (like a list or range). python Copy code for num in range(5): print(num)... (151 lines left) Collapse message.txt 8 KB Lectures Day Slides Reading 1 Intro PY4E: Intro 2 Why Computation 3 Computational Thinking 4 Expressions PY4E: Variables 5 Variables 6 Conditional Execution PY4E: Conditionals 7 Alternative Execution 8 Nested Conditionals 9 Exceptions 10 Functions PY4E: Functions 11 Custom Functions 12 Testing 13 Managing State 14 Modular Design 15 Exam Review 16 While PY4E: Iteration 17 For 18 Loop Patterns 19 Strings PY4E: Strings 20 String Methods 21 Files PY4E: Files 22 File Exceptions 23 File Writing 24 Lists PY4E: Lists 25 List Operations 26 Lists Continued 27 Dictionaries PY4E: Dictionaries 28 Dictionaries 29 Tuples PY4E: Tuples 30 Tuples 31 List Comprehensions 32 Generators 33 Web Flask 34 Git 35 Regex PY4E: Regex 36 Regex 37 Cli 38 Objects PY4E: Objects 39 Group Exercises Make sure to use the Python for everyone vocab too and stuff like that.