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In-depth tutorials written by Ameer Abdullah, Data Science & AI/ML graduate. Each article explains not just what the code does, but why, building the mental model you need for technical interviews.
Written by Ameer Abdullah
Data Science Graduate · AI/ML & Data Science Domain · Creator of PyCodeIt
A step-by-step method for predicting Python output under pressure - without running code.
Negative indices, step sizes, and reversals - master the patterns that show up in trace questions.
Pattern decks fail when interviewers change types, side effects, or control flow.
Desugar comprehensions into nested loops before you predict output.
Why def f(lst=[]) causes shared state - and how to spot it in a trace.
Structure your narration so interviewers can follow your trace table aloud.
Stack frames make recursion traceable once you tabulate calls and returns.
Insertion order, views, and runtime mutations - common dry-run themes.
Inner, left, right, and full outer joins - understand what rows survive and why before your next data round.
Aggregate without collapsing rows - the core skill data engineers and analysts get tested on.
Common Table Expressions improve readability but the performance story is more nuanced than most articles admit.
Understanding what each clause filters and when to use WHERE versus HAVING prevents the most common SQL mistakes.
B-tree indexes, composite index column order, and when a full table scan is actually faster.
Apply what you've learned in PyCodeIt's interactive Python and SQL sandboxes.