Multi-Track Syllabus (4 Tracks)

▶ Week 0 — Orientation & Computational Thinking

• Why CS matters in research and industry.
• Algorithms in daily life; decomposition and abstraction.
• Environment setup: Python, VS Code, Git, GitHub.
• First program & CLI basics.

Mini-lab: Hello World pipeline (edit → run → commit → push).

▶ Week 1 — Python Basics

• Variables, types, expressions, input/output.
• Conditions, loops; debugging mindset.

Problem set: CLI calculator, FizzBuzz variations.

▶ Week 2 — Core Data Structures

• Strings and slicing; lists & list comprehensions.
• Tuples, sets, dictionaries; when to use which.

Problem set: Word frequency counter & top-k terms.

▶ Week 3 — Functions & Modules

• Defining functions; parameters, return values.
• Recursion basics; standard library tour.

Problem set: Recursive problems + small utilities package.

▶ Week 4 — Algorithms & Big-O

• Searching (linear vs binary) & sorting (bubble, insertion, merge).
• Asymptotic analysis; time/space trade-offs.

Problem set: Leaderboard sorter with benchmarks.

▶ Week 5 — Memory Model & Abstraction

• Names vs objects; mutability & copying; slicing pitfalls.
• Data modeling with nested lists/dicts.

Problem set: 2D image filter (grayscale/blur/edges) in Python.

▶ Week 6 — Object-Oriented Programming

• Classes, methods, dunder methods.
• Encapsulation, inheritance, composition.

Problem set: Bank account / wallet system with unit tests.

▶ Week 7 — Databases & SQL

• Relational modeling; primary/foreign keys.
• SELECT, WHERE, GROUP BY, JOIN; SQLite + Python (sqlite3).

Problem set: Student/course enrollment DB + reports.

▶ Week 8 — Web Basics

• HTML5 semantics; CSS layout; accessibility basics.
• Intro to Flask; templates & routing.

Problem set: Personal site with a Flask contact form.

▶ Week 9 — APIs & JSON

• HTTP methods; requests; consuming public APIs.
• JSON parsing & validation.

Problem set: Weather dashboard (+ caching) via Open API.

▶ Week 10 — Security & Ethics

• Hashing & salting passwords; secure storage.
• Responsible computing, privacy & licenses.

Problem set: Minimal password manager (local, hashed).

▶ Week 11 — Capstone Prep

• Scoping, user stories, milestones, repo setup.

Deliverable: Proposal + wireframes + DB sketch.

▶ Week 12 — Final Project

• Implementation & presentation day.

Deliverables: Code repo, README, demo video, brief report.

▶ Week 0 — What is ML?

• AI vs ML vs DL; use-cases & limitations.
• Environment: Jupyter, data libs, plotting.

Mini-lab: Explore a small dataset end-to-end (load → describe → visualize).

▶ Week 1 — Data & Preprocessing

• Cleaning, imputing, scaling, encoding; train/test split.
• Feature engineering basics.

Problem set: Titanic or housing dataset cleanup & EDA.

▶ Week 2 — Regression

• Linear/regularized regression; bias-variance tradeoff.
• Metrics: MSE, MAE, R²; residual analysis.

Problem set: Predict house prices.

▶ Week 3 — Classification

• Logistic regression, k-NN; decision boundaries.
• Metrics: accuracy, precision/recall, F1, ROC-AUC.

Problem set: Spam or sentiment classifier.

▶ Week 4 — Model Evaluation

• Cross-validation, stratification; data leakage.
• Overfitting/underfitting; learning curves.

Problem set: Hyperparameter tuning with GridSearchCV.

▶ Week 5 — Trees & Ensembles

• Decision trees; feature importance & limits.
• Random forests, gradient boosting (concepts + practice).

Problem set: Tabular classification challenge.

▶ Week 6 — Neural Networks (Intro)

• Perceptron; forward pass; activation functions.
• Keras API; building your first dense network.

Problem set: MNIST digits (fully-connected baseline).

▶ Week 7 — Unsupervised Learning

• k-means & hierarchical clustering.
• PCA & dimensionality reduction.

Problem set: Customer segmentation.

▶ Week 8 — NLP Essentials

• Text preprocessing; bag-of-words; TF-IDF.
• Intro to embeddings; simple sentiment model.

Problem set: Movie review classifier.

▶ Week 9 — Vision Essentials

• CNN intuition; convolutions & pooling.
• Transfer learning with a small dataset.

Problem set: Mini image classifier (cats/dogs subset).

▶ Week 10 — Ethics, Fairness & Deployment

• Bias & fairness; model cards; interpretability basics.
• Packaging, versioning, and simple API deployment.

Problem set: Audit a model for bias + ship a simple API.

▶ Week 11 — Capstone Prep

• Choose domain dataset; define success metrics.

Deliverable: Proposal + baseline notebook.

▶ Week 12 — Final Project

• Implementation & presentation day.

Deliverables: Notebook, repo, model card, demo video.

▶ Basics

• Variables, I/O, control flow (if/for/while)
• Debugging mindset, error messages
• Mini-lab: CLI calculator + arguments

▶ Data Structures

• Strings, lists, tuples, sets, dicts
• List/dict comprehensions, slicing
• Exercise: word frequency & top-k

▶ Functions & Modules

• Functions, parameters, return values
• Imports, packages, virtual envs
• Exercise: utilities package + tests

▶ Files & JSON

• Reading/writing files safely (with)
• JSON encode/decode & validation
• Exercise: merge CSV/JSON datasets

▶ OOP in Python

• Classes, dunder methods, properties
• Inheritance vs composition
• Exercise: wallet/bank accounts

▶ Algorithms & Big-O

• Search (linear/binary), common sorts
• Asymptotics & profiling in Python
• Exercise: leaderboard benchmarks

▶ Testing & Packaging

• pytest basics, fixtures, parametrization
• Type hints & linters (mypy, ruff)
• Package a small library (pip install -e .)

▶ Week 0 — Data Mindset & Stack

• What is Data Science? Roles, workflow, reproducibility.
• Project structure, notebooks vs scripts, environments.

Mini-lab: Create a cookiecutter-style DS project skeleton.

▶ Week 1 — Python for Data

• NumPy arrays & vectorization; broadcasting.
• pandas Series/DataFrame: indexing, filtering, apply.

Problem set: Retail transactions cleanup + KPIs.

▶ Week 2 — Data Wrangling

• Merge/join, reshape (melt/pivot), groupby/agg.
• Handling missing/outliers; datetime ops; text columns.

Problem set: Multi-table join & feature creation.

▶ Week 3 — Visualization

• Matplotlib basics; seaborn* grammar; choosing the right chart.
• Storytelling, annotations, dashboards (intro).

Problem set: Exploratory data story with 4 charts.

▶ Week 4 — SQL for Analytics

• SELECT, JOINs, CTEs, window functions for metrics.
• SQLite + pandas; query optimization basics.

Problem set: Cohort & retention analysis with SQL.

▶ Week 5 — Probability Refresher

• Random variables, distributions, sampling.
• Law of large numbers, CLT; simulation in Python.

Problem set: Monte Carlo estimation mini-lab.

▶ Week 6 — Statistics & Inference

• Confidence intervals, hypothesis testing, p-values.
• AB testing design; power, effect size.

Problem set: AB test analysis & report.

▶ Week 7 — Linear Algebra for ML (Lite)

• Vectors, matrices, norms; projections; eigens (intuition).
• Practical: PCA from scratch vs scikit-learn.

Problem set: PCA on a high-dimensional dataset.

▶ Week 8 — ML Foundations

• Train/validation/test; metrics for regression & classification.
• Baselines, feature scaling, leakage, pipelines.

Problem set: Build a baseline model with a robust pipeline.

▶ Week 9 — Classical Models

• Logistic/linear regression, naive Bayes, SVM (intuition).
• Tree-based models overview; calibration & probability outputs.

Problem set: Compare 3 models on tabular data.

▶ Week 10 — Responsible AI

• Bias/fairness, data documentation, model cards.
• Privacy, PII handling, basic differential privacy ideas.

Problem set: Fairness audit + mitigations write‑up.

▶ Week 11 — MLOps Lite

• Experiment tracking, versioning, environments.
• Exporting models; simple FastAPI/Flask serving.

Deliverable: Reproducible training script + API stub.

▶ Week 12 — Capstone

• End‑to‑end project: data → model → report/dashboard.

Deliverables: Notebook/report, repo, short presentation.