Data Scientist
For aspiring data scientists who want to turn raw data into decisions. You will be able to analyze data, build and evaluate ML models, communicate insights, and ship models to production.
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01 Mathematics & Statistics Build the math and statistical intuition that every model, metric, and experiment relies on. 02 Programming & Data Tools Write clean Python and SQL to load, transform, and query data reliably. 03 Data Wrangling & Exploration Collect, clean, and explore data to uncover patterns and craft useful features. 04 Data Visualization Turn analysis into clear static charts, interactive visuals, and dashboards. 05 Core Machine Learning Train, evaluate, tune, and explain classical ML models with scikit-learn. 06 Applied Machine Learning Apply ML to forecasting, recommendations, text, and sequential decision problems. 07 Deep Learning & Generative AI Build neural networks with PyTorch and use modern LLMs, RAG, and agents in data work. 08 MLOps & Deployment Package, serve, monitor, and automate models so they deliver value in production. 09 Business, Communication & Ethics Frame the right problems, communicate results clearly, and apply data responsibly. 10 Capstone & Career Prove your skills with portfolio projects and prepare for data science interviews. Data Scientist For aspiring data scientists who want to turn raw data into decisions. You will be able to analyze data, build and evaluate ML models, communicate insights, and ship models to production. 10 STAGES · 45 TOPICS · 176 SUBTOPICS 1.1 Linear Algebra Vectors, Matrices & Tensors Matrix Operations Multiplication · Transformations · Determinants · Inverses Eigenvalues & Eigenvectors Matrix Decompositions SVD · PCA 1.2 Calculus & Optimization Limits & Continuity Derivatives & Gradients Partial Derivatives · Jacobian · Chain Rule Integrals Optimization Maxima & Minima · Gradient Descent · Hessian 1.3 Probability Probability Fundamentals Random Variables · Conditional Probability · Independence Probability Distributions Normal · Binomial · Poisson · Exponential Bayes' Theorem & Bayesian Inference Parameter Estimation MLE · MAP Markov Chains & Monte Carlo Simulation 1.4 Statistics & Experimentation Descriptive Statistics Mean · Median · Variance · Percentiles Hypothesis Testing p-values · t-tests · Chi-square · ANOVA Confidence Intervals A/B Testing Sample Size · Statistical Power · Multiple Testing Causal Inference Propensity Score Matching · Difference-in-Differences 2.1 Python Fundamentals Core Syntax Variables · Data Types · Control Flow Data Structures Lists · Dictionaries · Sets · Tuples Functions, Iterators & Generators Object-Oriented Programming (OOP) Error Handling & Exceptions Type Hints & Validation (Pydantic) 2.2 Numerical & Tabular Data Jupyter Notebooks NumPy Arrays · Broadcasting · Vectorization Pandas DataFrames · Series · Merging · Grouping Polars Expressions · Lazy Queries 2.3 SQL & Databases SQL Fundamentals Filtering · Joins · Aggregations Advanced SQL Window Functions · CTEs · Subqueries Cloud Data Warehouses Snowflake · BigQuery · Redshift · Databricks NoSQL Databases MongoDB · Cassandra Data Transformation (dbt) 2.4 Software Engineering Practices Command Line Basics Version Control Git · GitHub Environments & Packaging venv · conda · uv · Poetry Code Quality PEP 8 · Ruff · pre-commit Unit Testing (pytest) 3.1 Data Collection File Formats CSV · JSON · Parquet APIs REST · GraphQL · JSON Web Scraping BeautifulSoup · Scrapy · Playwright · Selenium Database Connectivity SQLAlchemy · psycopg 3.2 Data Cleaning Missing Values Dropping · Imputation Outliers Z-score · IQR · Isolation Forest Type Conversions & Date Parsing Text Cleaning Regex · String Methods Data Validation Pandera · Great Expectations 3.3 Exploratory Data Analysis (EDA) Univariate Analysis Bivariate & Multivariate Analysis Correlation & Covariance 3.4 Feature Engineering Encoding Categoricals One-Hot · Ordinal · Target Encoding Scaling & Transformations Standardization · Normalization · Log Transform Feature Extraction Dates · Text · Aggregates Feature Selection Filter · Wrapper · Embedded Methods 3.5 Big Data Processing Apache Spark & PySpark DataFrames · Spark SQL · MLlib Distributed Python Dask · Ray Local Analytics Engines (DuckDB) 4.1 Visualization Principles Choosing the Right Chart Visual Encoding & Color Avoiding Misleading Charts 4.2 Static Visualization Matplotlib Figures · Axes · Subplots Seaborn Distributions · Relationships · Categorical Plots 4.3 Interactive Visualization Plotly Plotly Express · Graph Objects Bokeh 4.4 Specialized Visualization Geospatial Maps GeoPandas · Folium Network Graphs (NetworkX) 4.5 Dashboards & Reporting Python Dashboards Streamlit · Dash BI Tools Tableau · Power BI · Looker Studio Reproducible Reports Jupyter · Quarto 5.1 ML Foundations Learning Paradigms Supervised · Unsupervised · Reinforcement Train, Validation & Test Splits Bias-Variance Tradeoff Data Leakage scikit-learn Workflow Estimators · Pipelines · ColumnTransformer 5.2 Regression Linear Regression Simple · Multiple Polynomial Regression Regularization Ridge · Lasso · Elastic Net 5.3 Classification Logistic Regression K-Nearest Neighbors (KNN) Support Vector Machines (SVM) Naive Bayes Decision Trees 5.4 Ensemble Methods Bagging & Random Forest Gradient Boosting XGBoost · LightGBM · CatBoost Stacking & Voting 5.5 Unsupervised Learning Clustering K-Means · Hierarchical · DBSCAN · Gaussian Mixtures Dimensionality Reduction PCA · t-SNE · UMAP Association Rules Apriori · FP-Growth Anomaly Detection Isolation Forest · One-Class SVM · LOF 5.6 Model Evaluation & Tuning Cross-Validation K-Fold · Stratified · Time-Series Split Classification Metrics Precision · Recall · F1 · ROC-AUC · PR-AUC Regression Metrics MSE · RMSE · MAE · MAPE · R-squared Hyperparameter Tuning Grid Search · Random Search · Optuna Imbalanced Data SMOTE · Class Weights · Threshold Tuning Explainability SHAP · LIME · Permutation Importance · Partial Dependence 6.1 Time Series Forecasting Stationarity & Autocorrelation ACF · PACF Classical Models ARIMA · SARIMA · Exponential Smoothing Forecasting Libraries Prophet · StatsForecast · sktime · Darts ML Forecasting Lag Features · Gradient Boosting Backtesting & Forecast Evaluation 6.2 Recommendation Systems Content-Based Filtering Collaborative Filtering Matrix Factorization Ranking Metrics Precision@K · NDCG · MAP 6.3 NLP Fundamentals Text Preprocessing Tokenization · Stemming · Lemmatization Bag of Words & TF-IDF Word Embeddings Word2Vec · GloVe Sentiment Analysis Topic Modeling LDA · BERTopic 6.4 Reinforcement Learning Basics Multi-Armed Bandits Epsilon-Greedy · Thompson Sampling Markov Decision Processes (MDP) Q-Learning & Value Iteration Policy Gradients 7.1 Neural Network Foundations Perceptrons & Activation Functions Forward & Backpropagation Loss Functions & Optimizers SGD · Adam · AdamW · RMSprop Regularization Dropout · Batch Normalization · Weight Decay · Early Stopping 7.2 Deep Learning Frameworks PyTorch Fundamentals Tensors · Autograd · nn.Module · DataLoader Keras & TensorFlow JAX (Optional) 7.3 Specialized Architectures Convolutional Neural Networks (CNNs) Transfer Learning ResNet · EfficientNet · ViT Detection & Segmentation YOLO · Faster R-CNN · U-Net Sequence Models RNN · LSTM · GRU Graph Neural Networks (GNNs) Deep Reinforcement Learning DQN · PPO Diffusion Models 7.4 Generative AI & LLMs Transformers Attention · Self-Attention · Multi-Head Attention Hugging Face Ecosystem Transformers · Datasets · Hub Large Language Models GPT · Claude · Gemini · Llama · Mistral Prompt Engineering Few-Shot · Chain-of-Thought · Structured Output Retrieval-Augmented Generation Chunking · Embeddings · Reranking Vector Databases Chroma · Qdrant · Milvus · pgvector · Pinecone Fine-Tuning PEFT · LoRA · QLoRA AI Agents & Tool Use LangGraph · CrewAI · OpenAI Agents SDK · MCP 8.1 Model Packaging Serialization Pickle · Joblib · Safetensors Portable Formats & Runtimes ONNX · ONNX Runtime · TensorRT Model Optimization Quantization · Pruning · Distillation 8.2 Experiment Tracking & Registry Experiment Tracking MLflow · Weights & Biases Data & Model Versioning DVC · lakeFS Model Registry (MLflow Model Registry) 8.3 APIs & Model Serving REST APIs FastAPI · Flask Serving Frameworks BentoML · Ray Serve · Triton Inference Server · vLLM Batch vs Real-Time Inference 8.4 Containers & Cloud Docker Dockerfile · Images · Containers · Compose Kubernetes Basics Cloud ML Platforms SageMaker · Vertex AI · Azure ML Serverless Deployment AWS Lambda · Cloud Run · Azure Functions 8.5 Pipelines & CI/CD CI/CD GitHub Actions · GitLab CI Workflow Orchestration Airflow · Prefect · Dagster · Mage Feature Stores Feast · Hopsworks 8.6 Monitoring & LLMOps Data & Model Drift Evidently · NannyML Model Observability Arize · Prometheus · Grafana LLMOps LangSmith · Langfuse · MLflow Tracing 9.1 Business Acumen Framing Problems as Data Problems Success Metrics KPIs · OKRs · North Star Metrics ROI of ML Projects 9.2 Communication Storytelling with Data Presenting to Non-Technical Audiences Technical Writing & Reports 9.3 Ethics & Responsible AI Data Privacy GDPR · CCPA · HIPAA Algorithmic Bias & Fairness Fairness Metrics · Fairlearn AI Governance EU AI Act · Model Cards · Datasheets 9.4 Project Management CRISP-DM Lifecycle Agile Methods Scrum · Kanban 10.1 Portfolio Projects End-to-End ML Project Data · Model · API · Dashboard Business Case Studies Churn · Fraud · Forecasting · Pricing Kaggle Competitions Publishing Work GitHub · Blog Posts · Hugging Face Spaces 10.2 Job Preparation Resume & Portfolio Review SQL & Python Coding Interviews Statistics & ML Interviews Product Case Interviews Metrics · A/B Tests Take-Home Assignments 10.3 Continuous Learning Reading Research Papers arXiv · Hugging Face Papers Communities Kaggle · Meetups · Conferences