Data Science Quizzes (MCQs)

Phase 1 Foundations The Prerequisites
Stats & Probability Distributions, Hypothesis Testing, Bayes
01_STATS
Math for DS Linear Algebra, Matrices, Calculus
02_MATH
Python for DS NumPy, Pandas, Vectorization
03_PYDS
Data Cleaning Imputation, Encoding, Scaling
04_CLEAN
Phase 2 Analysis & Viz Understanding Data
EDA Correlation, Skewness, Multivariate Analysis
05_EDA
Visualization Chart Selection, Interpretation, Heatmaps
06_VIZ
Feature Engineering PCA, LASSO, Interaction Features
07_FEAT
Phase 3 Core Machine Learning Modeling Fundamentals
Regression Linear, Polynomial, Cost Functions
08_REGR
Classification Trees, SVM, KNN, Random Forest
09_CLASS
Unsupervised K-Means, Clustering, Anomaly Detection
10_UNSUP
Phase 4 Advanced Modeling Professional Performance
Evaluation Metrics ROC/AUC, Bias-Variance, F1-Score
11_METRIC
Ensemble Methods XGBoost, Bagging, Boosting, Stacking
12_ENSEM
Deep Learning ANN, Activation, Backprop, Dropout
13_DL
Phase 5 Applied & Production Real-world Deployment
NLP TF-IDF, Embeddings, Tokenization
14_NLP
Time Series ARIMA, Seasonality, Stationarity
15_TIME
MLOps & Deployment FastAPI, Model Drift, A/B Testing
16_OPS