Introduction to Computing Science

What you'll learn

• Explain what computing science studies, define an algorithm by its definiteness, finiteness and correctness, use abstraction and decomposition to reason about systems, and describe the main fields of computing and their careers.
• Convert between decimal, binary and hexadecimal, represent signed integers in two's complement and recognize overflow, explain floating-point rounding, and describe how Unicode and UTF-8, images and sound are stored as bits.
• Apply a five-step problem-solving process, express algorithms in pseudocode and flowcharts, set up and run Python interactively and as scripts, and distinguish syntax errors, runtime errors and logic errors.
• Use Python's core types, variables as names for objects, arithmetic and comparison operators with correct precedence, Boolean logic with short-circuit evaluation, and type conversion.
• Write if, elif and else statements, match statements and conditional expressions, validate input with guard clauses, and test decisions with boundary values and decision tables.
• Write while and for loops with correct initialization, conditions and updates, apply accumulator, search and nested-loop patterns, trace loops by hand, run seeded simulations and count the steps a loop takes.
• Define functions with parameters, return values, defaults and docstrings, explain local scope, decompose problems top-down into small functions, and write simple recursive functions with base cases.
• Index, slice, search, clean and format strings, explain string immutability, and implement classic text algorithms including word counting, palindrome checks, a Caesar cipher and input validation.
• Choose and use lists, tuples, dictionaries and sets, explain aliasing and copying, write list comprehensions, and compare linear search, binary search and selection sort by counting comparisons.
• Read and write text, CSV and JSON files safely with explicit encodings, handle and raise exceptions appropriately, and build a read, clean, compute and write data pipeline that reports rejected data.
• Test programs with assertions, doctests and unittest using systematically chosen cases, debug methodically with hypotheses and experiments, and develop readable code incrementally following PEP 8.
• Design, implement and test a complete small program, follow academic integrity and AI-assistant policies, apply privacy principles under FERPA, sector laws and state privacy laws, and consider fairness, accessibility and next steps in computing.