Frame
What problem are we actually solving?
- Business objective
- Target
- Success metric
- Constraints
- Cost of errors
10-Month Live Full Stack Data Science Program
Full Stack Data Science Course: From Raw Data to Production ML
Python, SQL, statistics, machine learning, APIs, Docker, deployment and production ML through live classes and end-to-end projects.
Develop capability across the complete data-science lifecycle—Python, SQL, statistics, analysis, machine learning, model evaluation, API development, Docker and deployment fundamentals. You move beyond notebook-only projects into usable data products with documented evaluation and a prediction interface.
Business ProblemRaw DataAnalysisFeaturesModelEvaluationAPIDeploymentMonitoring
- 10 months
- Duration
- Live online
- Class format
- 5 projects
- Substantial builds
- 1 production capstone
- Capstone
- Placement support
- Career assistance
Includes placement support under written eligibility, scope and exclusion terms; not a placement guarantee.
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Full Stack Data Science Course at a Glance
- Program name
- Full Stack Data Science Course
- Duration
- 10 months
- Delivery format
- Live online classes with an instructor
- Skill level
- Beginner-friendly with consistent practice expected
- Projects
- 5 substantial end-to-end builds
- Capstone
- 1 production-style capstone from raw data to deployed model
- Certificate
- School of Core AI completion certificate under the published completion requirements
- Fee
- Fees vary by batch — request the current written fee sheet before enrolling
- EMI availability
- Ask admissions for the instalment options applicable to the current batch
- Career support
- Placement support under written eligibility, scope and exclusion terms
What Is Full Stack Data Science?
Full Stack Data Science means being capable across the complete lifecycle of a data product — framing a problem, working with raw data, analyzing it, building and evaluating machine-learning models, exposing predictions through an application or API, deploying the solution and understanding its production behavior.
Full Stack does NOT mean becoming an expert in every AI, cloud, frontend and infrastructure technology. It means owning the lifecycle — knowing enough about every stage to be responsible for the outcome, and knowing when a specialist needs to step in.
What Does “Full Stack” Actually Mean for a Data Scientist?
Own
These are your responsibilities as a Full Stack Data Scientist.
- Problem framing
- Data exploration
- Statistics
- Feature engineering
- Modeling
- Evaluation
- Basic serving and deployment
Understand and collaborate on
You should be able to work with specialists and reason about these areas.
- Data engineering
- Cloud infrastructure
- MLOps
- Production monitoring
- CI/CD
Specialist disciplines
These belong to dedicated SCAI pathways, not this course.
- Advanced data engineering
- Kubernetes and platform engineering
- Advanced AI Engineering
- AIOps
- LLMOps
- Frontend engineering
You Trained the Model. What Happens Next?
model.fit(X_train, y_train)
accuracy = 0.91The notebook is complete. The system is not.
- Where does live input come from?
- Who calls the model?
- How is preprocessing reproduced at inference time?
- How is the model packaged?
- How is a model version identified?
- What happens when data distribution changes?
- How do we know if predictions degrade?
Full Stack Data Science exists to close the gap between building a model and delivering a usable data product.
The SCAI Full Stack Data Science Lifecycle
Nine stages connect a business question to a monitored, deployed data product. The course is structured around this lifecycle so learners understand why each stage exists and what it hands to the next.
Acquire
Where does the data come from?
- SQL
- APIs
- Files
- Databases
- External sources
Validate
Can this data be trusted?
- Missing values
- Schema
- Duplicates
- Outliers
- Leakage
- Data quality
Understand
What does the data tell us?
- Statistics
- EDA
- Visualization
- Hypothesis testing
- Relationships
Engineer
What information should the model receive?
- Transformations
- Feature engineering
- Encoding
- Scaling
- Reusable preprocessing
Model
Which approach best fits the problem?
- Baseline
- Regression
- Classification
- Clustering
- Ensembles
- Applied DL where appropriate
Evaluate
Is the model genuinely useful?
- Cross-validation
- Metrics
- Thresholds
- Error analysis
- Explainability
- Business suitability
Deliver
How will another application use the model?
- FastAPI
- REST
- Validation
- Model packaging
- Docker
Observe
What happens after release?
- Logs
- Monitoring fundamentals
- Data drift awareness
- Model degradation
- Retraining concepts
Learners responsible for deeper production operations — reliability engineering, observability, lifecycle automation — can continue into the AIOps pathway. AIOps Course
Kaggle Teaches Modeling. Production Adds the Rest.
Competition notebooks are a genuinely good way to develop modeling intuition. The gap appears when a model must serve real users inside a real system.
| Competition / Notebook Project | Production Data Science |
|---|---|
| Dataset already provided | Data sources must be identified |
| Mostly clean dataset | Data must be validated |
| Fixed target | Business objective must be clarified |
| Leaderboard metric | Technical and business metric |
| Notebook | Maintainable project structure |
| Local prediction | API/service |
| Single environment | Reproducible environment |
| No real consumers | Input/output contract |
| One-off experiment | Model and version tracking |
| No runtime lifecycle | Monitoring and iteration |
Kaggle is excellent for developing modeling intuition. Full Stack Data Science adds the engineering and decision-making layers needed to turn models into usable systems.
The Highest Accuracy Model Is Not Always the Best Model
Model selection is a business decision, not a leaderboard position. Consider a customer-churn system for a subscription business with 100,000 customers, of whom 5,000 (5%) actually churn each quarter.
Model A
- Accuracy
- 96.0%
- Recall on churners
- 38%
- Precision
- 71%
- Inference cost
- High — large ensemble, slower response
- Explainability
- Low — difficult to explain individual predictions
Flags 1,900 of 5,000 churners but also 775 false alarms per quarter. The support team cannot explain why customers were flagged, so outreach scripts stay generic.
Model B
- Accuracy
- 93.4%
- Recall on churners
- 64%
- Precision
- 44%
- Inference cost
- Low — gradient-boosted trees, milliseconds
- Explainability
- High — feature attributions per prediction
Flags 3,200 of 5,000 churners with 4,100 false alarms. Because every flag comes with the drivers (payment failures, support escalations, usage drops), the retention team personalises outreach and saves measurably more customers per campaign.
Model A wins on accuracy because the churn class is small — predicting "no churn" for everyone already scores 95%. Model B trades a little overall accuracy for more than 1,300 additional churners caught per quarter and explanations the team can act on. The right choice depends on the cost of a missed churner versus the cost of an unnecessary outreach — that is threshold selection and business-metric design, and it is taught explicitly in this course.
Business metric selection
Choose the metric that reflects the decision the business will make — recall at an acceptable precision level, not raw accuracy on an imbalanced problem.
Threshold choice
The default 0.5 threshold is rarely right. Moving it changes precision and recall, and the right point depends on campaign capacity and cost per contact.
False positive / false negative trade-offs
A missed churner may cost a subscription; a wasted outreach costs minutes. Quantify both before selecting a model.
Model limitations
Document what the model does not see — unlogged complaints, competitor actions, seasonality — so users know when to distrust it.
Error analysis
Study the records the model gets wrong. Error patterns often reveal missing features or data-quality problems worth fixing before the next training run.
Who Should Learn Full Stack Data Science?
This course fits learners who want one structured technical route from programming and data fundamentals to applied machine-learning systems — including deployment and production awareness. It demands steady practice over ten months.
Graduate / Fresher
Needs a structured technical route from programming and data fundamentals to applied ML systems, with projects to show for it.
Data Analyst
Already works with data and wants to progress into statistics, machine learning and predictive systems beyond dashboard work.
Software Developer
Can code but needs stronger statistics, data analysis and ML capability to build data-driven products.
Working Professional / Career Switcher
Wants an end-to-end technical path rather than isolated tutorials, with a portfolio that demonstrates system thinking.
When Full Stack Data Science May Not Be the Right Path
If your primary goal is one of the following, a different SCAI pathway will serve you better — and we would rather point you there than keep you here.
- If your primary goal is dashboards, bi and reporting
- A Data Analytics pathway may be more appropriate. Data Science Course
- If your primary goal is rag, llms and generative ai applications
- The Generative AI program is the right home. Generative AI Course
- If your primary goal is production ai systems and broader engineering
- The AI Engineering Program fits better. AI Engineering Course
- If your primary goal is monitoring, reliability, automation and ai operations
- The AIOps Course is the specialist path. AIOps Course
- If your primary goal is autonomous agents and multi-agent systems
- The Agentic AI Course covers that specialization. Agentic AI Course
Before you start
Professional coding experience is not required — the course begins with Python and SQL foundations. You need regular practice time and a computer capable of running development tools.
Ask admissions for the expected weekly commitment and system requirements before enrolling.
What You Should Be Able to Do by the End of the Program
Capability statements, not slogans. Each item below is something you should be able to demonstrate through your projects by the end of the ten-month program.
- Query relational datasets with SQL
- Build reusable Python data workflows
- Clean and validate messy datasets
- Perform exploratory analysis
- Apply statistics appropriately
- Detect leakage and common data problems
- Engineer features
- Build baseline models
- Train ML models
- Evaluate models correctly
- Perform error analysis
- Work with applied NLP/CV problems
- Expose models through APIs
- Package applications in Docker
- Understand deployment fundamentals
- Understand the model and version lifecycle
- Recognize monitoring and drift issues
- Communicate assumptions and limitations
- Present technical work as a decision-oriented data product
How Deep Does the Full Stack Data Science Course Go?
A transparent depth map. "Strong" means you can use it independently on real problems; "Applied" means you use it on real problems with guidance; "Fundamentals" means you understand and can reason about it; "Introductory" means awareness level.
- Python
- Strong working proficiency
- SQL
- Strong working proficiency
- Statistics
- Strong applied foundation
- EDA
- Strong
- Machine Learning
- Strong
- Deep Learning
- Applied
- NLP
- Applied
- Computer Vision
- Applied
- FastAPI
- Production foundation
- Docker
- Production foundation
- Databases
- Working proficiency
- Redis
- Fundamentals
- Kafka
- Fundamentals
- MLOps
- Production fundamentals
- Generative AI
- Introductory extension only
Depth claims map to the actual curriculum. Advanced specialisation in any of these belongs to dedicated SCAI pathways, not to inflated claims on this page.
Full Stack Data Science Learning Roadmap
Ten months, eight phases. Each phase produces capability the next phase assumes — the sequencing is the design.
Phase 1 — Programming & Data Foundations
- Python
- OOP
- Git
- NumPy
- Pandas
- SQL
- Data handling
Phase 2 — Statistics & Data Analysis
- Probability
- Statistics
- Hypothesis testing
- EDA
- Visualization
- Data quality
Phase 3 — Machine Learning
- Regression
- Classification
- Clustering
- Ensembles
- Feature engineering
- Cross-validation
- Hyperparameter tuning
- Explainability
Phase 4 — Applied Deep Learning & Unstructured Data
- Neural networks
- NLP
- Computer vision
- Embeddings where relevant to DS
Phase 5 — Building Data Products
- Project structure
- FastAPI
- REST
- Validation
- Testing
- Logging
Phase 6 — Deployment Foundations
- Docker
- Runtime and environment
- Model artifacts
- Deployment fundamentals
Phase 7 — Production ML Awareness
- Tracking
- Versioning
- Monitoring fundamentals
- Drift
- Retraining concepts
Phase 8 — Full Stack Capstone
- Problem → Data → Model → Production
Full Stack Data Science Projects
Each project produces a reviewable artifact with a defined problem, data, evaluation method, and portfolio evidence.
1
Retail data pipeline
- Business problem
- Consolidate scattered retail sales data into a reliable, queryable source.
- Data source
- Raw transactional and catalogue files with realistic quality issues.
- Modeling work
- A versioned ingestion and cleaning pipeline with validation checks and SQL models.
- Evaluation decision
- Reconciliation of totals against source data and documented quality checks.
- Portfolio output
- A repository with pipeline code, validation report and run instructions.
2
Churn prediction service
- Business problem
- Identify customers likely to leave so retention can act early.
- Data source
- Customer account and behaviour dataset with class imbalance.
- Modeling work
- Baseline, feature engineering, model selection and error analysis, served through a FastAPI endpoint.
- Evaluation decision
- Recall and precision trade-offs reviewed against the retention decision, not accuracy alone.
- Portfolio output
- A deployed prediction API with documented evaluation and a model card.
3
Document-routing system
- Business problem
- Route incoming documents to the correct processing queue automatically.
- Data source
- A document corpus with categories and realistic noise.
- Modeling work
- Text preprocessing, a classification model and a routing decision layer with confidence handling.
- Evaluation decision
- Per-category performance, error analysis on misrouted documents and a low-confidence fallback rule.
- Portfolio output
- A working routing service with evaluation report and failure examples.
4
Visual inspection system
- Business problem
- Flag defective items on a production line using images.
- Data source
- An image dataset of acceptable and defective samples.
- Modeling work
- An applied computer-vision classifier with preprocessing and threshold selection.
- Evaluation decision
- Cost-of-error analysis: false rejects versus missed defects, with a chosen operating point.
- Portfolio output
- A demonstration application with documented evaluation and limitations.
5
Production capstone
- Business problem
- Take a business problem from raw data to a monitored, deployed service.
- Data source
- A dataset the learner scopes and validates with instructor review.
- Modeling work
- The complete lifecycle: data preparation, modelling, API, packaging, deployment and monitoring plan.
- Evaluation decision
- A code-reviewed repository, a written release report and a live demonstration before reviewers.
- Portfolio output
- An end-to-end data product presented as portfolio proof of full stack capability.
Fewer Tutorial Projects. Deeper End-to-End Builds.
Portfolio quality does not come from a long list of small exercises. It comes from demonstrating judgement across a full build: framing the problem, handling messy data, establishing baselines, making evaluation decisions, analysing failures, engineering the service, deploying it and documenting the whole thing.
- Problem framing
- Messy data
- Data quality
- Baseline thinking
- Evaluation decisions
- Failure analysis
- Engineering
- Deployment
- Documentation
This course includes 5 substantial builds plus 1 production capstone. The count is modest by design — each project is deep enough to demonstrate every dimension above.
From Business Problem to Production: A Full Data Science Walkthrough
One flagship example, end to end. This is the kind of reasoning the capstone asks you to demonstrate — and the kind of explanation interviews increasingly test.
Business Question
Which customers are likely to churn next quarter? The success metric is not model accuracy — it is whether the retention team can contact the right customers early enough to change the outcome.
Data
CRM records, transaction history and support interactions. The data lives in three systems with different identifiers and update frequencies.
Data Quality
Missing tenure values, duplicate accounts, support tickets linked to the wrong customer, and leakage risk: any field updated after the churn event (e.g. "retention offer sent") must be excluded from training features.
Analysis
Churners differ from retained customers on payment failures, support escalations and usage decline — but the differences vary by customer segment, so a single global story would be misleading.
Features
Tenure, 90-day transaction frequency, support-ticket frequency, payment-failure count, and rolling usage trends. Features are computed only from data available before the prediction date.
Baseline
A simple interpretable baseline: customers with a payment failure in the last 60 days are flagged. It catches fewer churners but establishes the bar any model must beat.
Model
Compare a logistic regression and a gradient-boosted tree under time-ordered cross-validation. The tree wins on PR-AUC; the regression is kept for its coefficient transparency.
Evaluation
Raw accuracy is misleading with a 5% churn rate. Recall at an operationally acceptable precision, threshold analysis against campaign capacity, and error analysis by segment are what actually inform the go/no-go decision.
Serving
Expose a /predict endpoint through FastAPI. The request schema validates customer features; the response returns the probability and the top contributing factors.
Packaging
The service, the model artifact and the preprocessing pipeline are packaged in Docker so the exact runtime can be reproduced anywhere.
Production Questions
Which inputs will change shape over time? What should be logged for every prediction? What monitoring signal would tell us the model is degrading? When is retraining justified?
Business Output
A ranked list of retention candidates with reasons — a deliverable the retention team can act on, not a notebook.
Production-Style Full Stack Data Science Capstone
Build the complete lifecycle of a data product — from raw data and model training to a containerized prediction service and production feedback loop.
Data Layer
Data Source
Ingestion
Validation
Transformation
Turn raw inputs into trustworthy training data.
ML Layer
Feature Pipeline
Training
Evaluation
Model Artifact
Build, evaluate and version the predictive model.
Serving Layer
Prediction API
Docker
Deployment
Package the model into a usable prediction service.
Production Feedback Layer
Logs / Monitoring
Iteration / Retraining Decision
Observe performance and decide when iteration is required.
The capstone focuses on the production foundations a Full Stack Data Scientist should understand; advanced platform automation, Kubernetes and AIOps remain specialist pathways.
Full Stack Data Science Course Curriculum
Nine modules, each taught through four layers: what you understand, what you use, what you practise, and what you build. The build is always a reviewable output.
- What You'll Understand
- Python fundamentals
- SQL queries
- Version control
- Data-quality concepts
- What You'll Use
- Python
- Pandas
- SQL
- Git
- What You'll Practice
- Build a validated data ingestion script; Write reconciled SQL queries; Add quality checks to data pipelines
- What You'll Build
- A versioned data pipeline with quality checks
How You Learn
Structured learning is not about watching videos — it is the progression from concept to reviewed system. Each stage exists because skipping it produces learners who can follow tutorials but cannot build independently.
Concept
Understand the principle before touching code.
Guided Lab
Implement it with step-by-step support.
Independent Exercise
Solve a problem without step-by-step instructions.
System Build
Integrate the capability into a larger project.
Review
Discuss implementation, errors and trade-offs with an instructor.
How Your Progress Is Evaluated
Progress is measured through reviewable work, not attendance. The mechanisms below are how the course checks capability at each stage.
- Python and SQL exercises
- Data-analysis assignments
- ML evaluation exercises
- Module projects
- Code review
- Architecture review
- Project presentations
- The production capstone
The School of Core AI completion certificate is issued after the cohort-specific requirements are met. The requirements are shared in the batch and enrolment documents — ask admissions for the current written terms.
Technology Stack Across the Data Science Lifecycle
Technologies organised by the function they serve in the lifecycle — not a logo wall. Only technologies actually taught in the curriculum are listed.
Data & Storage
- SQL
- APIs
- Files
- Databases
- Kafka fundamentals
Analysis
- Python
- NumPy
- Pandas
- Visualization tools
Modeling
- Scikit-learn
- Deep-learning framework taught in the module
Application
- FastAPI
- Validation tooling
Infrastructure
- Docker
- Redis fundamentals
Production
- Experiment and model tracking
- Versioning
- Monitoring concepts
What We Do Not Teach Just to Make the Syllabus Look Bigger
A transparent syllabus is more useful than an impressive-looking one. These technologies are deliberately left to specialist SCAI pathways — with the reasoning.
- React
- A Full Stack Data Scientist should understand how a model is consumed by an application but does not need to become a frontend specialist.
- Kubernetes
- Containerization and deployment fundamentals matter. Advanced orchestration belongs more naturally in MLOps/AIOps or platform-focused roles.
- Advanced LLM Engineering
- Understanding modern AI is useful, but advanced RAG, fine-tuning and LLM evaluation belong in the Generative AI pathway.
- Advanced Agent Systems
- Multi-agent systems belong in the Agentic AI specialization rather than being forced into a Data Science curriculum.
- Advanced AI Operations
- Monitoring awareness belongs here; deep reliability, observability and lifecycle automation belong in AIOps.
Which Roles Does Full Stack Data Science Prepare You For?
Honest role boundaries. This course prepares you strongly for some roles, conditionally for others, and explicitly not for the rest without further specialisation.
Strong alignment
- Data Scientist
- Associate / Junior Data Scientist
- Applied Data Scientist
- ML/Data Science Analyst
These roles are exactly the lifecycle this course teaches: data work, statistics, modelling, evaluation and communication.
Alignment depends on prior background
- Machine Learning Engineer
- Analytics Engineer
- Production-oriented ML roles
These roles lean on software-engineering depth. Learners entering from a development background align faster; pure freshers need additional engineering practice.
Requires additional specialisation
- Data Engineer
- AI Engineer
- MLOps Engineer
- AIOps Engineer
- LLM Engineer
These are distinct disciplines — data infrastructure, production AI architecture, platform operations, LLM systems. SCAI teaches them in dedicated programs, not as a footnote here.
See What Learners Actually Build
Real proof beats testimonials. This section is reserved for verified learner artifacts — published when consented, never manufactured.
- GitHub repositories
- Project architecture diagrams
- Deployed API screenshots
- Experiment-tracking screenshots
- Model evaluation reports
- Project READMEs
- Verified career outcomes
Artifact gallery is being prepared with learner consent. Nothing is displayed here until it is real and verified.
Career and Placement Support
Placement support is assistance, not a guarantee. The services below are delivered under written eligibility conditions; the current policy defines participation rules, duration and exclusions.
- Resume review
- LinkedIn review
- GitHub portfolio review
- Project presentation coaching
- Python/SQL interview preparation
- ML interview preparation
- Mock interviews
- Job and application support
- Hiring opportunities and industry connections
We distinguish placement assistance from a placement guarantee. Ask admissions for the current written support terms, eligibility rules and exclusions before enrolling.
Full Stack Data Science vs Generative AI vs AI Engineering vs AIOps
Full Stack Data Science is strongest in data, statistics, machine learning and end-to-end predictive systems. Generative AI specialises in LLMs and generative applications. AI Engineering builds production AI systems. AIOps operates them. The four solve different problems — the strongest column below shows where each path is genuinely the best choice.
| Aspect | Full Stack Data Science | Generative AI | AI Engineering | AIOps |
|---|---|---|---|---|
| Primary focus | End-to-end data science lifecycle | LLMs, RAG and generative applications | Production AI applications and services | AI system operations and reliability |
| Starting point | Beginner-friendly with consistent practice | Comfortable with Python and development concepts | Stronger programming foundation | Production or AI engineering experience |
| Statistics depth | Strong applied foundation | Applied where relevant to LLM work | Applied | Awareness |
| Machine learning depth | Strong | Applied | Strong integration focus | Awareness |
| LLM / RAG depth | Introductory extension only | Strong — the specialty | Integration focus | Awareness |
| API / service development | Production foundation (FastAPI) | Applied for LLM apps | Strong | Monitoring of services |
| Deployment | Docker and deployment fundamentals | Applied for LLM apps | Strong production engineering | Operations focus |
| Monitoring | Fundamentals + drift awareness | LLM evaluation focus | System reliability | Strong — the specialty |
| Production architecture | Predictive data products | Generative applications | Complex AI systems | Platform operations |
| Operations / reliability | Awareness | Awareness | Strong | Strong — the specialty |
| Best suited for | Learners who want ownership of the data-science lifecycle | Learners building LLM/RAG applications | Learners engineering larger AI systems | Engineers operating AI in production |
| Expected prior experience | None — foundations taught | Working Python + dev concepts | Strong programming base | Production/AI engineering background |
| Example outcome | Churn model served through an API with monitoring | RAG application with evaluated retrieval | Production AI service with architecture ownership | Reliable AI operations with observability |
Highlighted cells mark where each pathway is genuinely the strongest choice.
Can You Learn Full Stack Data Science for Free?
Yes. High-quality resources for Python, statistics, SQL, machine learning and deployment are widely available for free, and self-disciplined learners can absolutely build these skills without paying.
What structured, paid learning should provide in exchange:
- Sequencing — what to learn, in what order, and why
- Accountability — a cohort schedule that keeps you moving
- Live problem solving — instructors who answer the question in front of you
- Feedback — code review and project review from experienced practitioners
- Project architecture guidance — so your portfolio shows system thinking
- Career preparation — interviews, presentation and positioning
Full Stack Data Science Course Fees
Fees and instalment options can change by batch. Request the current written fee sheet — including the total possible cost across upfront and deferred components, GST treatment, payment dates and cancellation terms — before enrolling.
- Total possible cost across upfront, deferred and EMI components
- Whether GST is included or additional
- Booking or registration amount
- Payment schedule
- Available instalment or EMI terms and any third-party financing conditions
- Whether certificate, assessments and career support are included
Last curriculum review: 2026-09-09
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