Data Science Exam Study Guide
Seven topic guides covering everything tested on data science interviews and exams. Code examples, exam traps, and tips throughout.
Test your Data Science knowledge →Free · No sign-upNumPy Explained — Arrays, Broadcasting, Indexing & Common Operations
NumPy is the foundation of scientific Python. Here's what data science exams test — arrays, broadcasting, and vectorised operations.
Pandas Explained — DataFrames, Filtering, GroupBy & Merge
Pandas is the data manipulation library in Python. Here's what data science exams test — DataFrames, filtering, and aggregation.
Statistics for Data Science — Mean, Distributions, Hypothesis Testing
Statistics is the language of data science. Here's what exams test — from descriptive stats to hypothesis testing.
Machine Learning Overview — Supervised, Unsupervised & Key Concepts
Machine learning fundamentals are tested on every data science exam. Here's the core concepts and where beginners get confused.
ML Model Evaluation — Accuracy, Precision, Recall, F1 & ROC-AUC
Choosing the right evaluation metric is critical. Here's what exams test — precision vs recall tradeoff and when accuracy fails.
Feature Engineering — Encoding, Scaling, Missing Values & Selection
Feature engineering often matters more than model choice. Here's what data science exams test.
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