Learning Targets:

1. Declare and assign variables with meaningful identifiers; inspect and convert types using type(), int(), float(), and str().

2. Perform arithmetic, comparison, and string operations; format output with f-strings and control precision with round().

3. Manipulate lists, dictionaries, sets, and tuples (index, slice, add/remove/update elements, membership tests) to model simple problems.

4. Acquire input using input(); validate and convert values safely with guard code to prevent crashes from bad input.

5. Import and apply standard modules (e.g., math, random, statistics) to solve computational tasks.

Modules

Learning Targets:

1. Implement branching with if/elif/else and boolean logic (and, or, not) to control program behavior.

2. Employ for and while loops to iterate over ranges, strings, and collections; use break and continue appropriately.

3. Apply counting, accumulation, and searching patterns to process data sets correctly and efficiently for the problem scale.

4. Trace program state step-by-step (manually or with a debugger) to predict outputs and detect off-by-one and boundary errors.

5. Refactor repetitive code into clear loop structures and helper logic to reduce duplication.

Modules

Learning Targets:

1. Define and call functions with positional, keyword, and default parameters; return values that satisfy the stated contract.

2. Apply local and global scope rules to prevent unintended side effects and name collisions.

3. Document functions and modules using docstrings (PEP 257) and optional type hints to improve readability and tooling support.

4. Organize code into modules and packages; import with aliases and use the if __name__ == '__main__' guard for runnable scripts.

Modules

Learning Targets:

1. Read from and write to text files safely using with open(...) and pathlib.Path for cross-platform file paths.

2. Parse and generate CSV files with the csv module, handling headers and delimiters correctly.

3. Load and dump JSON data to and from Python dictionaries/lists using json.load()/json.dump().

4. Transform datasets using loops and list/dictionary methods (e.g., append, extend, update, get) to filter, aggregate, or reshape records.

5. Handle file-related exceptions (e.g., FileNotFoundError, PermissionError) with try/except to produce informative, user-friendly messages.

Modules

Learning Targets:

1. Load CSV data into a pandas DataFrame and inspect structure (shape, columns, dtypes) and basic summary info.

2. Filter rows, select columns, sort values, and compute simple aggregates (e.g., mean, count, min/max); create derived columns with vectorized expressions.

3. Generate basic plots (line, bar, histogram) using matplotlib or pandas .plot(); include titles, axis labels, and legends.

4. Export cleaned data and charts to files (e.g., CSV, PNG) with meaningful filenames and organized folders.

5. Justify choices that avoid misleading visuals and respect privacy when using real or shared datasets.

Modules

Learning Targets:

1. Diagnose and fix syntax, runtime, and logic errors using stack traces, strategic print/log statements, and VS Code breakpoints/watch.

2. Write and run basic unit tests (unittest or pytest) that cover typical and edge cases; interpret failures to guide fixes.

3. Enforce code style and readability by applying PEP 8; format with black and identify issues with flake8.

4. Version code with Git: initialize a repository, create branches, make atomic commits with clear messages, and push to GitHub.

5. Maintain a .gitignore and a README that document installation, usage, and testing instructions for the project.

Modules