Learning Path

• Foundation — Core Python
• System & Tools
• Concurrent & Async Programming
• Network Programming
• Data & Web
• Quality & Security
• Advanced Topics
• Quick Paths by Goal
  • Interview Preparation
  • Data Science / ML
  • Systems / Infrastructure
• See Also

A recommended progression through these cheat sheets, organized by skill level and topic area. Whether you're new to Python or brushing up for an interview, follow the path that fits your goals.

Foundation — Core Python

Start here if you're new to Python or need a refresher on fundamentals.

Step	Topic	What You'll Learn
1	Python Basics	Syntax, operators, control flow, comprehensions
2	Functions	Arguments, decorators, closures, annotations
3	Data Structures	dict, list, set, heap with practical patterns
4	Classes & OOP	Inheritance, magic methods, properties, MRO
5	Generators	Generator functions,yield from, async generators
6	Type Hints	Static typing, Protocols, TypedDict, Generics
7	Regular Expressions	Pattern matching, groups, lookahead
8	Unicode	Encoding, normalization, bytes vs str

Deepen your foundation:

Step	Topic	What You'll Learn
9	Itertools	Combinatorics, chaining, grouping, infinite iterators
10	Collections	deque, Counter, defaultdict, namedtuple, ChainMap
11	Functools	partial, cache, singledispatch, wraps, reduce
12	Dataclasses	@dataclass, field, frozen, InitVar, inheritance

System & Tools

Apply Python to interact with the operating system and build command-line tools.

Step	Topic	What You'll Learn
13	OS Interfaces	Process management, environment, platform info
14	Pathlib	Modern path manipulation, globbing, file operations
15	File I/O	Streams, binary data, text encoding
16	Date/Time	datetime, timezone, formatting, timedelta
17	Logging	Log levels, handlers, structured logging
18	Argparse	CLI argument parsing, subcommands
19	Click	Modern CLI framework, commands, groups

Concurrent & Async Programming

Master Python's concurrency models for I/O-bound and CPU-bound work.

Step	Topic	What You'll Learn
20	Threading	Threads, locks, queues, GIL implications
21	Multiprocessing	Processes, pools, shared memory
22	Futures	ThreadPoolExecutor, ProcessPoolExecutor
23	Async Guide	Coroutines, event loop, async/await mental model
24	Async Basics	asyncio.run, tasks, gather, timeouts
25	Async Servers	TCP/UDP servers, streams
26	Async Advanced	Semaphores, queues, graceful shutdown

Network Programming

Build networked applications from raw sockets to secure communications.

Step	Topic	What You'll Learn
27	Socket Basics	Address resolution, TCP/UDP, byte order
28	Socket Servers	select, poll, epoll, ThreadingTCPServer
29	Async Sockets	Non-blocking I/O, async operations
30	SSL/TLS	Secure connections, context configuration
31	SSH	paramiko, key management, tunneling
32	Socket Sniffer	Raw sockets, packet capture

Data & Web

Work with databases, build web APIs, and manipulate data.

Step	Topic	What You'll Learn
33	SQLAlchemy	Engine, connections, transactions, raw SQL
34	SQLAlchemy ORM	Models, relationships, sessions
35	SQLAlchemy Query	Joins, subqueries, aggregation patterns
36	FastAPI	Routes, Pydantic, DI, async handlers
37	NumPy	Arrays, broadcasting, linear algebra, ufuncs
38	Pandas	DataFrames, groupby, merge, pivot, datetime

Quality & Security

Test your code and keep it secure.

Step	Topic	What You'll Learn
39	Pytest	Fixtures, parametrize, tmp_path, monkeypatch
40	Unittest Mock	patch, Mock, AsyncMock, autospec
41	Profiling	cProfile, timeit, memory_profiler, optimization
42	Cryptography	AES-GCM, Argon2, key derivation
43	TLS/SSL	Certificate verification, secure defaults
44	Vulnerabilities	Injection, timing attacks, secure coding

Advanced Topics

Specialized areas for ML engineering, high-performance computing, and language extensions.

Step	Topic	What You'll Learn
45	C Extensions	pybind11, Cython, ctypes comparisons
46	PyTorch	Tensors, autograd, DataLoader, distributed
47	Megatron	Model/pipeline parallelism, distributed training
48	LLM Serving	vLLM, SGLang, continuous batching
49	LLM Benchmarks	Throughput, latency, benchmarking methodology
50	Slurm	Job submission, GPU scheduling, array jobs
51	Ray	Distributed tasks, actors, cluster management

Quick Paths by Goal

Interview Preparation

1. Interview Cheatsheet — question-indexed map
2. Follow links into each topic area for detailed answers
3. Focus on: OOP (classes), concurrency (threading, GIL), asyncio, decorators, generators, SQLAlchemy

Data Science / ML

1. NumPy → Pandas
2. PyTorch → Megatron → LLM Serving
3. Slurm for cluster scheduling

Systems / Infrastructure

1. OS → Pathlib → Logging
2. CLI tools → Click
3. Network → SSL/TLS → SSH
4. Security → Vulnerabilities

See Also

• Python 3 new features — what changed between versions
• What's New in Python 3 — version-by-version highlights