Python Programming for Professionals

• What is Python? - history, philosophy (The Zen of Python) & real-world applications
• Installing Python, VS Code & configuring virtual environments with venv
• Running Python: interactive REPL, scripts & Jupyter Notebooks
• Understanding the Python interpreter: compilation, bytecode & the CPython runtime
• pip & PyPI: installing and managing third-party packages

• Dynamic typing: integers, floats, booleans, strings & NoneType
• Arithmetic, comparison, logical, bitwise & assignment operators
• String methods: slicing, formatting (f-strings), strip, split & join
• Type conversion: int(), str(), float(), bool() & implicit coercion
• Constants, naming conventions (PEP 8) & code readability best practices

• if / elif / else: writing expressive conditional logic
• while loops: sentinel values, infinite loops & break/continue
• for loops: iterating over sequences, ranges & enumerate()
• Comprehensions: list, set & dictionary comprehensions for concise code

Nested loops, loop-else clauses & the pass statement

• Defining functions: parameters, return values & docstrings
• Positional, keyword, default & variable-length arguments (*args, **kwargs)
• Scope rules: LEGB (Local, Enclosing, Global, Built-in) & the global keyword
• Lambda functions & when to use them
• Recursion: base cases, recursive steps & the call stack

• Lists: indexing, slicing, mutation, sorting & common list methods
• Tuples: immutability, packing/unpacking & namedtuples
• Dictionaries: CRUD operations, dict comprehensions, defaultdict & OrderedDict
• Sets: mathematical operations (union, intersection, difference) & frozensets
• Choosing the right data structure: time & space complexity trade-offs

• Classes & objects - __init__, instance vs class attributes & self
• The four pillars: encapsulation, abstraction, inheritance & polymorphism
• Method types: instance methods, class methods (@classmethod) & static methods
• Magic (dunder) methods — __str__, __repr__, __len__, __eq__ & operator overloading
• Composition vs inheritance: when to use each design approach

• Abstract base classes (ABC) & interfaces with the abc module
• Mixins: adding behaviour to classes without deep inheritance hierarchies
• Properties & descriptors: @property, getters, setters & deleters
• Common design patterns: Singleton, Factory, Observer & Decorator pattern
• SOLID principles applied to Python class design

• Reading & writing text files: open(), context managers (with statement)
• Working with CSV files using csv module & pandas
• JSON serialisation: json.loads(), json.dumps() & handling nested structures
• XML & YAML: parsing strategies and library options
• Binary files: pickle, shelve & when serialisation formats matter

• Iterators & the iteration protocol: __iter__ & __next__
• Generators: yield, generator expressions & lazy evaluation
• itertools module: chain, cycle, groupby, product & more
• functools: reduce(), partial(), lru_cache & total_ordering
• map(), filter() & zip(): functional-style data transformations

• First-class functions: passing functions as arguments & returning them
• Closure mechanics: captured variables & factory functions
• Writing custom decorators: @wraps & preserving metadata
• Stacking decorators & parameterised decorators
• Metaclasses: __new__, __init_subclass__ & class factories

• Exception hierarchy: BaseException, Exception & built-in exception types
• try / except / else / finally: complete exception handling patterns
• Raising & re-raising exceptions: custom exception classes
• Context managers: __enter__ / __exit__ & the contextlib module
• Debugging tools: pdb, breakpoint(), logging module & traceback analysis

• The Global Interpreter Lock (GIL): implications for CPU vs I/O-bound tasks
• threading module: Thread, Lock, Event & thread-safe data structures
• multiprocessing: Process, Pool, Queue & shared memory
• asyncio & async/await: event loops, coroutines & async context managers
• aiohttp & httpx: asynchronous HTTP requests for high-throughput apps

• unittest: TestCase, setUp/tearDown, assertions & test discovery
• pytest: fixtures, parametrize, markers & plugins
• Mocking & patching: unittest.mock, MagicMock & monkeypatching
• Test-driven development (TDD): red-green-refactor cycle
• Code quality tools: flake8, black, isort, mypy (type hints) & pre-commit hooks

• NumPy arrays: ndarray, broadcasting, vectorised operations & universal functions
• Pandas Series & DataFrame: creation, indexing (loc/iloc) & boolean masking
• Data cleaning: handling missing values, duplicates, type casting & outliers
• Aggregation & groupby: split-apply-combine, pivot tables & crosstabs
• Merging, joining & concatenating DataFrames: inner, outer, left & right joins

• Matplotlib: figures, axes, subplots & publication-quality plot styling
• Seaborn: statistical visualisations: heatmaps, violin plots & pair plots
• Plotly: interactive charts, choropleth maps & Dash dashboards
• Choosing the right chart type: bar, line, scatter, histogram & box plots
• Exporting visualisations: PNG, SVG & embedding in reports & notebooks

• Flask fundamentals: routes, templates (Jinja2), request/response cycle
• RESTful API design: endpoints, HTTP methods, status codes & JSON responses
• FastAPI: path parameters, Pydantic models, dependency injection & async routes
• Database integration: SQLAlchemy ORM, migrations with Alembic
• Authentication & security: JWT tokens, OAuth 2.0 & password hashing with bcrypt

• OS & filesystem automation: os, pathlib, shutil & glob for batch file operations
• Scheduling tasks: schedule library, cron jobs & APScheduler
• Web scraping with BeautifulSoup & requests: parsing HTML & extracting data
• Selenium & Playwright: browser automation, form filling & screenshot capture
• Working with APIs: REST, GraphQL & SDK integration (Twilio, Stripe, OpenAI)

• Scikit-learn workflow: data splitting, feature engineering & model evaluation
• Supervised learning: linear regression, decision trees & random forests
• Unsupervised learning: k-means clustering & dimensionality reduction (PCA)
• Model evaluation metrics: accuracy, precision, recall, F1 & ROC-AUC
• Introduction to deep learning: TensorFlow/Keras & PyTorch basics

• Data analytics dashboard: ETL pipeline, pandas analysis & interactive Plotly/Dash visualisations
• REST API backend: FastAPI with PostgreSQL, JWT auth, Dockerised & deployed to AWS/Render
• Web scraping & automation bot: multi-site scraper, data cleaner & scheduled email report
• ML classification app: data preprocessing, model training, evaluation & Streamlit web UI
• CLI productivity tool: task manager / file organiser with rich output, config files & PyPI packaging