Before you start
Python, SQL, Git, and Linux basics.
Fundamentals of algebra, probability, statistics, and optimization.
Time for weekly coding projects and maintaining a GitHub portfolio.
Comfort with notebooks, code files, documentation, and reproducible environments.
Roadmap / 18 weeks
Weeks 1–3 / Python, SQL, Git, and data cleaning
Project: Exploratory analysis repository
Weeks 4–6 / Mathematics, statistics, and optimization
Project: Regression from scratch
Weeks 7–11 / Supervised learning and feature engineering
Project: Business prediction projects
Weeks 12–15 / Unsupervised learning and evaluation
Project: Segmentation or anomaly project
Weeks 16–18 / Documentation and interview practice
Project: Final portfolio review
Syllabus
Python, SQL, and data handling
Python functions, environments, NumPy, pandas, SQL joins and window functions, Git, and testing basics.
Project: Clean and validate a messy dataset, then document its exploratory analysis.
Mathematics and statistics
Vectors, matrices, probability, distributions, sampling, gradients, and loss functions.
Project: Implement linear regression with gradient descent and compare it with a library implementation.
Data preparation and feature engineering
Missing data, encoding, scaling, feature selection, data splits, and leakage prevention.
Project: Create a reusable preprocessing workflow and a feature dictionary.
Regression
Problem framing, regularization, tree and ensemble models, residuals, and metric selection.
Project: Predict a numeric business outcome and compare a baseline with a tuned model.
Classification
Binary and multiclass problems, imbalance, thresholds, precision, recall, F1, and error analysis.
Project: Build a classifier for a problem such as churn, lead scoring, or support tickets.
Unsupervised learning and anomaly detection
Clustering, dimensionality reduction, cluster validation, and outlier detection.
Project: Produce segment profiles and investigate unusual records.
Evaluation, tuning, and interpretation
Cross-validation, hyperparameter search, metric selection, feature importance, and model explanations.
Project: Tune a model and report its results, important drivers, and failure cases.
Portfolio and interview preparation
Repository organization, project summaries, communicating metrics, and discussing trade-offs.
Project: Prepare documented repositories, project summaries, and interview notes.
Schedule and fees
Enquire about the next start date, weekly schedule and total fees before enrolling.