Data Scientist

Program

Data Scientist

Go from data to predictive, defensible models

Learn Python, statistics, and the machine learning workflow from data to model.

8

Weeks

42

Chapters

4

Practice formats

1

Portfolio artifact

8 weeks — one module per week (8 modules, 42 chapters), each week ending with a live class every Saturday, plus a module exam before the next module opens.

Curriculum

42 chapters across 8 modules — each with guided notes, a live example, a quiz, an exercise, a challenge, and class practice.

Module 2 · DATA PREPARATION & EXPLORATION

  1. 6Data CleaningFinding and handling missing values, duplicates, wrong types, inconsistent text, and outliers.Learn →
  2. 7Exploratory Data AnalysisSummary statistics, distributions and skew, relationships and correlation, segment comparisons, and forming hypotheses.Learn →
  3. 8Data VisualizationChoosing the right chart, Matplotlib and seaborn basics, and designing honest, readable visuals.Learn →
  4. 9Statistics for Data ScienceProbability distributions, sampling and the central limit theorem, confidence intervals, hypothesis tests, and A/B testing.Learn →
  5. 10SQL for Data Scientists: Building Feature TablesPulling modelling data with SQL, defining the unit of analysis, aggregating behaviour into features, avoiding leakage with time windows, and joining labels.Learn →

Module 3 · MACHINE LEARNING

  1. 11Machine Learning FundamentalsSupervised and unsupervised learning, features and targets, train/test splits, baselines, and the scikit-learn API.Learn →
  2. 12Linear RegressionFitting lines, interpreting coefficients, regression metrics (MAE, RMSE, R²), polynomial features, and regularization.Learn →
  3. 13ClassificationLogistic regression and naive Bayes, probabilities and thresholds, the confusion matrix, precision, recall, F1, and ROC AUC.Learn →
  4. 14Decision Trees & Random ForestsHow decision trees split data, depth and overfitting, random forests and boosting, and feature importance.Learn →
  5. 15Model Validation & TuningCross-validation, the bias-variance trade-off, hyperparameter search, pipelines, and data leakage.Learn →
  6. 16Feature EngineeringEncoding categories, scaling, dates and derived features, text features, and ColumnTransformer pipelines.Learn →
  7. 17Unsupervised LearningClustering with k-means, choosing k, principal component analysis, and using unsupervised methods in practice.Learn →

Module 4 · DELIVERING DATA SCIENCE

  1. 18Responsible Evaluation & EthicsEvaluating models honestly, documenting limitations, fairness and bias, privacy, explainability, and monitoring after deployment.Learn →
  2. 19Experiment Design & A/B TestingRandomised experiments, choosing metrics, minimum detectable effect and sample size, two-proportion tests, and pitfalls like peeking and novelty effects.Learn →
  3. 20Decision Thresholds & Business ValueTurning probabilities into actions, cost-benefit matrices, choosing thresholds that maximise value, capacity constraints, and explaining the trade-off.Learn →
  4. 21Communicating Data Science ResultsTranslating model results for non-technical audiences, structuring a findings deck, visualising uncertainty, and writing recommendations people act on.Learn →
  5. 22Documentation, Model Cards & ReproducibilityWhat to document about data and models, model cards and datasheets, reproducible notebooks and environments, and handing projects over.Learn →

Module 5 · ON THE JOB: CHURN, FORECASTING & CAUSAL QUESTIONS

  1. 23On the Job: Predicting Customer Churn"Which customers will cancel next month?" Frame the problem, define the label without leakage, build features, handle class imbalance, choose a threshold by business cost, and hand over a target list.Learn →
  2. 24On the Job: Demand Forecasting"How many units will each store sell next month?" Decompose trend and seasonality, build baselines, fit models, backtest properly, and communicate forecast uncertainty.Learn →
  3. 25On the Job: Did the Discount Work? Causal QuestionsAnswer "what was the effect of X?" when you can't always run an A/B test — correlation vs causation, confounding, difference-in-differences, matching, and uplift modelling.Learn →
  4. 26On the Job: Customer Lifetime ValueHistorical vs. predictive CLV, order frequency, AOV and margin, retention-based lifetime estimates, segmenting by value, and using CLV for acquisition budgets.Learn →
  5. 27On the Job: Anomaly DetectionSpotting unusual values in metrics and transactions, z-scores, IQR rules, rolling baselines, model-based methods, and building alerts people trust.Learn →

Module 6 · ON THE JOB: TEXT, RECOMMENDERS & ADVANCED MODELS

  1. 28On the Job: Classifying Customer Feedback with NLPRoute 10,000 support tickets a week automatically — text cleaning, TF-IDF features, linear classifiers, evaluation per class, and when to use embeddings or LLMs instead.Learn →
  2. 29On the Job: Building a Recommender"Customers who bought this also bought…" — popularity baselines, item-to-item collaborative filtering, content-based and hybrid approaches, cold start, and offline/online evaluation.Learn →
  3. 30On the Job: Gradient Boosting & Explaining ModelsWin on tabular data with gradient boosting — how it works, key hyperparameters, early stopping — then explain predictions with permutation importance, partial dependence and per-prediction reasons.Learn →
  4. 31On the Job: Embeddings & Semantic SearchWhat embeddings are, cosine similarity, semantic search and clustering of text, retrieval for LLM applications, and evaluating search quality.Learn →
  5. 32On the Job: Imbalanced ClassificationWhy accuracy misleads on rare events, precision/recall and PR-AUC, class weights, resampling, threshold tuning, and evaluating rare-event models.Learn →

Module 7 · ON THE JOB: DEPLOYING & MAINTAINING MODELS

  1. 33On the Job: Putting a Model into ProductionYour notebook model needs to run every day for real users — batch vs real-time serving, packaging pipelines, APIs, latency and reliability, and a safe launch.Learn →
  2. 34On the Job: MLOps — Reproducible Training Pipelines"Which data and settings produced the model in production?" Track experiments, version data and models, build automated training pipelines, and use a model registry.Learn →
  3. 35On the Job: Monitoring Models & Handling DriftSix months later the model quietly got worse. Monitor inputs, predictions and outcomes, detect data and concept drift, set alerts, and decide when to retrain.Learn →
  4. 36On the Job: Batch Scoring vs. Real-Time APIsChoosing batch or online inference, scoring pipelines, feature consistency between training and serving, latency and scaling, and rollout strategies.Learn →
  5. 37On the Job: Auditing Performance & Fairness by SegmentSlicing model performance by segment, group fairness metrics (selection rate, recall gaps), causes of disparities, and mitigation options.Learn →

Module 8 · CAPSTONE & CAREER LAUNCH

  1. 38Capstone: End-to-End ProjectFraming a problem, building a reproducible end-to-end pipeline, evaluating honestly, and communicating results in a portfolio-ready report.Learn →
  2. 39Portfolio & Interview Preparation for Data ScientistsStand out in a competitive field — business-focused portfolio projects, the DS CV, statistics and ML interview questions, take-home cases, and product sense.Learn →
  3. 40Job Search Strategy for Data ScientistsTypes of data science roles, reading ads for the real job, building a focused portfolio, tailoring applications, and standing out in a crowded market.Learn →
  4. 41ML & Product Case InterviewsStructuring open-ended ML design and product-metric cases, clarifying goals, choosing data, models and metrics, discussing trade-offs, and common prompts.Learn →
  5. 42Your First 90 Days as a Data ScientistStart with impact — learn the business and data, find a well-scoped first project, build stakeholder trust, avoid common traps, and plan your growth.Learn →
Start learning →

Delivery

Blended — self-paced lessons plus live office hours

Portfolio outcome

A trained and evaluated ML model with a written explanation of its limitations, for your portfolio.

Certificate

Certificate of completion, verifiable online with its credential code.

By the end of this program, you'll be job-ready to…

  • Frame a business problem as a supervised learning problem
  • Clean, explore, and engineer features from raw data
  • Evaluate a model honestly, including where it fails

Salary expectations

Estimated U.S. national average annual salary by experience level.

$85,000

Entry-level

$120,000

Mid-level

$165,000

Senior-level

Estimated figures for planning purposes — actual pay varies by location, employer, and experience.

Tools you'll master

Pythonpandasscikit-learnStatisticsJupyter

Practical training, every chapter

  • A live "try it yourself" example in every chapter you can run immediately
  • A hands-on exercise per chapter, with hints and a reveal-able answer
  • An open-ended challenge per chapter, graded against real success criteria
  • Class practice prompts for pairs or cohorts, not just solo homework

Why take this program

Interview- and job-ready

Built to the same bar Aicloud actually hires against — not a generic syllabus.

Tier 1 company partners

Program partnerships with leading employers are in progress — see Affiliation for status.

Job connection access

Graduates get access to Aicloud's own hiring pipeline and employer referral network.

Professional networking

Cohort discussion boards and live sessions connect you with peers and mentors, not just content.

A real portfolio

You leave with a concrete, explainable artifact — proof of knowledge, not just a certificate.

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