AI & Data · Advanced Career
Data Science
Understand the data. Build the model. Prove the value. Ship responsibly.
From data foundations to explainable models and production-ready AI.
Advanced layer includedIndustry Edge tools and scenario-based labs beyond the recognized core. Curriculum v1.2 • Updated Sep 2026
Recognized curriculum, prerequisites, assessments, and public fees disclosed before counselling.
Industry Edge Lab includedAdvanced tools, emerging patterns, and scenario-based labs beyond the recognized core.Registration and admissions follow-up continue securely in BharatCampus ONE.
Data Science v1.2 · Engineering foundations included
Better data pipelines. Still a Data Science program.
Practice repeatable batch ingestion, SQL join correctness, data contracts, quality checks, dataset provenance and point-in-time features inside the existing 20-week, 150-live-hour syllabus. These exercises reuse the foundations, preparation, feature-engineering, deployment and capstone modules; they do not add extra hours.
Statistics, supervised and unsupervised learning, evaluation, explainability and deep-learning foundations remain core. Full warehouse engineering, Airflow operations, distributed Spark pipelines and Kafka streaming belong in the separate Data Engineering path.
Compare with the planned Data Engineering curriculumIncluded beyond the published curriculum
The recognized core is your foundation—not the finish line.
Every Data Science cohort also enters the CohortAI Industry Edge Lab: an evolving layer of advanced tool categories, emerging architecture and production patterns, and scenario-based labs. A guided counselling preview connects the most relevant capabilities to your target role, followed by a release-governed lab sequence after enrollment.
Release-governed advanced tools Emerging architecture patterns Scenario-based labs
Fit before enrollment
Who should join
Graduates with interest in mathematics, coding and applied AI who want a deep fresher pathway rather than a tool-only course.
Role pathways
Your starting point
Basic algebra and comfort using a computer. Python, SQL and statistics bridges are included.
This is not an exclusion filter. It helps us confirm whether you can start directly or would benefit from a short bridge before the cohort.Recognized core
Skills and technology stack
These employability foundations are part of the public learning promise and remain visible before registration.
Python SQL PostgreSQL foundations Parquet NumPy pandas scikit-learn Jupyter Matplotlib PyTorch/TensorFlow foundations MLflow foundations Docker
Build-first curriculum journey
9 missions. One defensible portfolio.
Mentor-led hybrid learning with 1.5-hour live instruction, all-day supervised practice access, weekly build evidence and a version-locked capstone.
M013 weeks · 22.5 live hours
Python, SQL and data foundations
Data Assembly Line: build one clean dataset from three incompatible sources.
- Python programming for analysis
- NumPy arrays and vectorised thinking
- pandas data manipulation
- Relational modelling, analytical SQL and grain-safe joins
Guided labCombine SQL, CSV and paginated API data into a reproducible analytical dataset; rerun the same batch without duplicate records.
Portfolio evidenceBatch-ingestion script and notebook, SQL script, source manifest, data dictionary and Git repository.
AssessmentFoundation coding and data-wrangling practical with a duplicate-safe rerun and join-grain check.
M023 weeks · 22.5 live hours
Probability and statistics
Uncertainty Clinic: defend a decision when the data is incomplete.
- Probability rules and random variables
- Distributions and expectation
- Sampling and confidence intervals
- Hypothesis testing
Guided labDesign and analyse an experiment with uncertainty, effect size and business interpretation.
Portfolio evidenceStatistical analysis report and experiment-design canvas.
AssessmentApplied statistics case assessment.
M032 weeks · 15 live hours
Data preparation and exploratory analysis
Forensic EDA: find the hidden flaw that would make a model look unrealistically good.
- Missing values, outliers, schema and uniqueness checks
- Categorical and numerical transformation
- Split before fitting transformations; train-only preprocessing and cross-validation pipelines
- Exploratory visualisation
Guided labProfile a noisy dataset, reject broken input contracts and fit preprocessing only on training data; document assumptions and leakage risks.
Portfolio evidenceEDA report, data-quality tests and preprocessing pipeline.
AssessmentLeakage and data-quality review: fail a malformed input and demonstrate train-only fitting.
M043 weeks · 22.5 live hours
Supervised machine learning
Model Tournament: the winner is the best business choice, not merely the highest score.
- Linear and logistic regression
- Decision trees and ensemble intuition
- k-nearest neighbours and support-vector concepts
- Training/validation/test design
Guided labBuild and compare multiple models for a real business prediction problem.
Portfolio evidenceModel comparison notebook, experiment table and recommendation.
AssessmentModel selection viva and reproducibility check.
M052 weeks · 15 live hours
Unsupervised learning and feature engineering
Pattern Discovery Lab: turn mathematical groups into useful business personas.
- Clustering and distance
- Dimensionality reduction
- Association and anomaly concepts
- Feature creation and selection with time-aware joins
Guided labCreate actionable segments and build a timestamped feature example that excludes information unavailable at prediction time.
Portfolio evidenceSegmentation notebook, persona cards, validation report and point-in-time feature check.
AssessmentCluster-quality and actionability review.
M062 weeks · 15 live hours
Model evaluation and explainability
Trust but Verify: explain when the model should not be used.
- Cross-validation and robust comparison
- Precision/recall, ROC and threshold selection
- Calibration and cost-sensitive decisions
- Feature importance and local/global explanation
Guided labAudit a model for error patterns, fairness, calibration and stakeholder explainability.
Portfolio evidenceModel card, threshold policy and explainability dashboard.
AssessmentResponsible-model review board.
M072 weeks · 15 live hours
Deep learning foundations
Learning Curve Lab: diagnose underfitting, overfitting and data problems from evidence.
- Tensors and neural-network intuition
- Forward pass, loss and backpropagation
- Training loops and optimisation
- Dense networks and regularisation
Guided labTrain and evaluate a transfer-learning model on an image or text classification task.
Portfolio evidenceDeep-learning experiment report and reusable training notebook.
AssessmentTraining-loop practical and error-analysis review.
M081 week · 7.5 live hours
Deployment and MLOps foundations
Notebook-to-Service Sprint: prove that another person can run and monitor your model.
- Model packaging and prediction APIs
- Experiment tracking and model registry
- Testing, dataset-version manifests and reproducible batch-to-prediction runs
- Containers and deployment basics
Guided labPackage a model as a tested API, containerise it and document the input dataset version, batch dependencies and monitoring plan.
Portfolio evidenceDeployed model service, model registry record and input-freshness/recovery runbook.
AssessmentDeployment demonstration and production-readiness checklist.
M092 weeks · 15 live hours
End-to-end capstone
Applied AI Review Board: defend value, risk, architecture and evidence.
- Problem and success-metric definition
- Repeatable data acquisition, provenance, quality gates and leakage-safe features
- Experiment design and model development
- Evaluation and responsible-use analysis
Guided labSolve a domain problem from raw data to an explainable prediction service; demonstrate a duplicate-safe data refresh and a failing input-quality check.
Portfolio evidenceCapstone repository, versioned dataset manifest, quality checks, model card, API, monitoring plan and executive recommendation.
AssessmentDemo Day, technical panel and data-science interview simulation.
The week feels different here
A repeatable rhythm from concept to evidence
Weekly rhythm
- 1Concept sprint and visual roadmap
- 2Guided implementation lab
- 3Independent build challenge
- 4Debug, review and production-pattern clinic
- 5Skill check, demo and learning reflection
Learner experience
CohortAI Skill Passport with milestone badges
Build-first weekly checkpoints rather than lecture-only completion
Peer demo and code/design review rituals
Mentor office hours and all-day practice-lab access
Capstone Demo Day with a business narrative, technical walkthrough and interview-style defence
Responsible AI-assisted learning policy: explain, verify and own every submitted artifact
Interview-ready proof
What you will be able to show—not merely claim
These are tangible outputs you can demonstrate, explain, and defend through a technical walkthrough, business narrative, or interview conversation.
Exploratory data and statistical inference case
Supervised machine-learning business solution
Segmentation/recommendation-style unsupervised project
Deep-learning prototype
Deployed end-to-end data-science capstone
Transparent evaluation
How your progress is assessed
Completion standard
Minimum 70% overall, mandatory capstone pass, at least 80% lab submissions and versioned portfolio evidence.
CohortAI Industry Edge Lab
Your published curriculum is the foundation. The Industry Edge is what keeps it moving forward.
The Applied AI Lab extends the program with model evaluation, efficient experimentation, feature management and foundation-model integration patterns.
Your counselling preview connects relevant capability categories to your target role. The complete release-governed lab sequence becomes part of the enrolled learning plan.Version-controlled learning promise
DATA-SCIENCE@1.2.0
Released 9 September 2026 under catalog CAT-2026.09.1. This page presents the approved curriculum, learner outcomes, duration, and public fee for informed comparison.
Straight answers
Frequently asked questions
Who is the Data Science program designed for?
Graduates with interest in mathematics, coding and applied AI who want a deep fresher pathway rather than a tool-only course.
What prerequisites do I need?
Basic algebra and comfort using a computer. Python, SQL and statistics bridges are included.
What is included in the public curriculum?
The public curriculum includes the recognized stack, all 9 module themes, guided labs, portfolio artifacts, assessment model, duration, and public fees. The Industry Edge Preview explains how advanced capability categories extend this foundation for your target role.
Where do counselling and enrollment happen?
CohortAI supports public discovery and program comparison. Registration, consent, counselling follow-up, application updates, and enrollment are managed securely in BharatCampus ONE.