Python for Data Engineering Interviews — The Complete Fundamentals
Master the Python fundamentals that Data Engineering interviewers actually test. 14 chapters, 70+ hands-on coding exercises, and real interview problems — from variables to OOP, regex to JSON parsing.
About This Course
What You'll Learn
Course Curriculum (14 Modules)
Variables, Data Types & Type Casting
Integers, floats, strings, booleans, type casting, and the variable naming conventions DEs follow.
String Manipulation & Formatting
String slicing, formatting, f-strings, split/join, and the text processing pipelines DEs build daily.
Lists, Tuples & Sets — Core Data Structures
Lists, tuples, sets — creation, indexing, slicing, iteration, and the performance tradeoffs interviewers ask about.
Hashable Collections
Unordered collections optimized for fast lookups. Sets enforce uniqueness and support mathematical operations (union, intersection). Dictionaries map keys to values for O(1) access.
Dictionaries & Hash Maps — The DE Power Structure
Dictionary operations, nested lookups, iteration patterns, and the hash map problems that dominate DE interviews.
Conditionals — If/Elif/Else & Boolean Logic
If/elif/else chains, ternary expressions, boolean logic, and the conditional routing patterns in real pipelines.
Loops — For, While, Comprehensions & Iteration
For loops, while loops, enumerate, zip, list comprehensions, and nested iteration — 4 parts covering every pattern.
Functions — Lambda, Map, Filter & Sorting
Defining functions, lambda expressions, map/filter/reduce, sorted() with keys, and functional programming patterns.
Error Handling — Try/Except & Pipeline Safety
Try/except/finally, custom exceptions, and the defensive coding patterns that keep production pipelines alive.
Date & Time — Parsing, Formatting & Arithmetic
datetime, timedelta, strptime/strftime, timezone handling, and the date arithmetic every DE pipeline needs.
Regex — Pattern Matching & Text Extraction
Raw strings, character classes, quantifiers, groups, lookaheads — the regex toolkit for log parsing and data validation.
File I/O — Reading, Writing & Batch Processing
open(), read/write modes, CSV processing, context managers, and the batch file operations pipelines run nightly.
JSON — Parsing, Building & Nested Structures
json.loads/dumps, nested access, schema validation patterns, and the JSON transformations at the heart of every API pipeline.
OOP — Classes, Inheritance & Polymorphism
Classes, __init__, inheritance, polymorphism, dunder methods — the OOP patterns behind Spark, Airflow, and dbt.
Start This Course
Create a free account to enroll, track your progress, complete exercises, and earn a certificate.
Enroll Now →