IBM Data Science Professional Certificate: Python for Data Science and Programming Fundamentals

Python for Data Science and Programming Fundamentals

This domain tests practical Python: data types, containers, control flow, functions, classes, exceptions, file handling, and the idioms used in notebooks. Questions often ask you to predict output or choose the safer construct.

Core rules

• Lists, dictionaries, and sets are mutable; strings and tuples are immutable.
• Indexing is zero-based; slicing excludes the stop index.
• == compares values; is compares object identity.
• Default arguments are evaluated once, so avoid mutable defaults such as def f(items=[]).
• Use try/except/else/finally and raise specific exceptions.
• Open files with with open(...) as f so they close reliably.
• Follow PEP 8: snake_case for functions and variables, CapWords for classes.

Worked example

Count the most common words in a file:

from collections import Counter

def top_words(path, n=3):
    with open(path, encoding='utf-8') as f:
        words = f.read().lower().split()
    return Counter(words).most_common(n)

Reasoning: with guarantees closure; lower() normalizes case; split() tokenizes on whitespace; Counter counts without a manual dictionary loop; most_common returns tuples sorted by descending frequency. If punctuation must be removed, preprocess before counting rather than changing the return type.

Traps

• Mutating a list while iterating over it.
• Assigning the result of list.append, which is None.
• Forgetting self in instance methods.
• Catching broad Exception and hiding real bugs.
• Assuming range(1, 5) includes 5; it yields 1 through 4.
• Mixing class attributes and instance attributes.