Data Science & Machine Learning Master Roadmap

Core Python programming, data structures, control flow, functions, OOP, and memory management.

Vectorized numerical computing with NumPy and high-performance data manipulation with Pandas.

Exploratory data visualization, Matplotlib, Seaborn, plotting techniques, and graphical insights.

Descriptive/inferential statistics, probability distributions, hypothesis testing, Bayes theorem, and A/B testing.

Relational data querying, SELECT operations, JOINs, aggregations, GROUP BY, HAVING, and subqueries.

Window functions, CTEs, indexing strategies, query execution plans, transactions, and performance tuning.

Data cleaning, missing value imputation, outlier detection, scaling, encoding, and feature creation.

Linear Regression, Ridge, Lasso, ElasticNet, polynomial regression, assumptions, and loss optimization.

Logistic Regression, Decision Trees, SVM, Naive Bayes, KNN, classification metrics, and ROC-AUC curves.

K-Means, Hierarchical Clustering, DBSCAN, PCA, t-SNE, anomaly detection, and dimensionality reduction.

Cross-validation techniques, GridSearch, RandomSearch, Optuna, bias-variance tradeoff, and metric selection.

Bagging, Random Forests, Boosting (AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost), and Stacking.

Stationarity, trend/seasonality decomposition, ARIMA, SARIMA, Prophet, lag features, and backtesting.

End-to-end ML project pipeline from raw data ingest, preprocessing, training, validation to inference.

Perceptrons, Feedforward Neural Networks, Backpropagation, activation functions, optimizers, and loss functions.

Convolutions, pooling, stride, padding, modern CNN architectures (ResNet, VGG), object detection, and vision tasks.

Sequential models, vanishing gradients, Long Short-Term Memory, Gated Recurrent Units, and seq2seq models.

Tokenization, TF-IDF, Word2Vec, GloVe, FastText, text preprocessing, and classical NLP pipelines.

Self-attention mechanism, multi-head attention, Positional Encoding, BERT, GPT architecture, and Encoder-Decoder.

Prompt engineering, RAG (Retrieval-Augmented Generation), vector databases, LangChain, LlamaIndex, and fine-tuning.

FastAPI, Docker, model registry, MLflow, CI/CD pipelines, monitoring, data drift, and cloud deployment.

Scalable ML architectures, recommendation systems, search ranking, real-time vs batch inference, and SLA metrics.

Curated interview coding problems, real-world data questions, and solutions for Pandas and SQL mastery.