Do I need a computer science degree to start Data Science?

No. The course begins with Python and computational foundations before moving into statistics and machine learning. Comfort with logical problem-solving and consistent practice matters more than a specific degree. Learners with no coding experience should expect to spend additional time on the early Python labs.

How much mathematics is required?

You will learn the applied mathematics needed to understand and evaluate models, including probability, statistics, vectors, gradients and optimisation intuition. The emphasis is on using the ideas correctly and explaining model behaviour rather than memorising long proofs.

Can I complete the course while working full-time?

Yes. The 10–12 month path is structured for working professionals as well as graduates, with live guidance, recorded sessions, checkpoints and project work that can be planned around a job. An advisor can confirm the current cohort schedule and weekly commitment before you enrol.

How is this different from the Data Analytics course?

Data Analytics focuses on business questions, SQL, spreadsheets and dashboards. Data Science goes further into Python programming, statistical modelling, machine learning, deep learning, Generative AI and deployment. Choose this course if you want to build and evaluate predictive or AI systems rather than primarily report and visualise business performance.

Will I learn both machine learning and Generative AI?

Yes. You first build durable foundations in statistics, supervised and unsupervised learning, then progress to deep learning and LLM applications. Generative AI is taught with retrieval, evaluation, guardrails and cost awareness so it strengthens rather than replaces core modelling skills.

Will I deploy models or only train them in notebooks?

You will move beyond notebooks by packaging models behind APIs, containerising services, tracking experiments and designing monitoring for drift, latency and quality. The goal is to understand the complete path from an experiment to a production-shaped system.

What will my portfolio contain?

Your portfolio can include predictive modelling, forecasting or recommendation work, a deep-learning project, an evaluated RAG application and an end-to-end capstone. Strong projects include code, data assumptions, evaluation choices, failure analysis, model cards and a clear explanation of impact.

What is included in 100% placement?

Eligible learners receive 100% placement, including role mapping, portfolio feedback, resume and LinkedIn preparation, mock technical interviews, referrals and continued opportunity guidance throughout the job search.

Data & AI programmeCareer-focused Data Science programme

Data Science Machine Learning & AI

Learn to turn data into predictive models, intelligent applications and production-ready machine learning systems.

guided programme: 0–12 months
applied projects: 0+
industry credential: Microsoft

Who this course is for

Different starting points.One path to production.

Whether you arrive from a degree, analytics, engineering or domain expertise, the course closes the same gaps: Python, statistics, modern ML, evaluation and deployment.

Graduate or final-year student
Build the programming, statistics and modelling depth employers expect beyond a degree.
Career direction
Junior data scientist or ML internship
Data or business analyst
Move from describing what happened to building predictive models and experiments.
Career direction
Data scientist or decision scientist
Software or data professional
Add machine learning, deep learning and production model skills to your technical base.
Career direction
Machine learning or applied AI engineer
Domain expert or researcher
Turn subject-matter knowledge into defensible, data-driven systems and experiments.
Career direction
Applied data science in your domain

The complete learning experience

Data Science is more thantraining a model.

Live technical learning, 45+ applied builds, production-minded engineering and interview preparation, connected through one rigorous journey.

Learn the foundations live

Understand the mathematics and code behind each method with practitioners who use them.

Live technical classes
Guided coding labs
Personal feedback

45+ applied projects

Turn concepts into experiments, models and systems across multiple real-world domains.

Model-building labs
Independent challenges
Technical reviews

Build the complete ML stack

Connect data preparation, modelling, evaluation, deployment and monitoring.

Python
scikit-learn
PyTorch
TensorFlow
MLflow

Turn depth into career proof

Package your strongest work into evidence technical interviewers can examine.

Code + model cards
Portfolio reviews
Mock interviews

By the end, you will be able to frame, build, evaluate and ship a Data Science solution, not only explain an algorithm.

Mentors across industry

Learn from practitioners shapingdata, products and AI systems.

Learn with the complete Interview Prep mentor network across Google, Microsoft, Apple, Uber and PayPal, bringing perspectives from Data Science, decision-making, products, finance and AI.

Data Science & Analytics

Megna Roy

Google, Amazon, Uber & Mu Sigma

SQLAdvanced AnalyticsDecision Strategy

AI Systems & Evaluation

Sofia Bennett

Generative AI, evaluation & agent systems

PythonPyTorchDocker

Senior Product Manager

Deepak Yadav

Product strategy & growth

Product StrategyRoadmapsGrowth

Decision Scientist

Mayank Mittal

Decision science & analytics

Decision ScienceAnalyticsBusiness Insights

Product Analytics & Growth

Marcus Reed

Product strategy, analytics & growth

Product StrategyAdvanced AnalyticsGrowth

Product Leader

Monojeet Sinha

14+ years in product leadership

Product StrategyProblem SolvingGrowth

Data Science & MLOps

Elena Kovács

Data science, MLOps & model operations

PythonTensorFlowDocker

Business Finance Analyst

Ekta Agarwal

13+ years in business finance

Financial PlanningBusiness FinanceStrategy

Data Scientist

Rishabh Gupta

7+ years in data science

Data ScienceAdvanced AnalyticsProduct Analytics

Data Engineering & Platforms

Kenji Sato

Data engineering, streaming & platforms

SparkAirflowKafka

Data Science curriculum

Learn the science. Build the system.

Nine connected modules take you from Python and probability to machine learning, LLM applications and production deployment. Open a module to inspect the work.

9 modules: Foundations to production
10–12 months: Live + guided practice
45+ projects: Models, systems and labs
Deployed capstone: Career-ready finish

Build a reliable programming foundation for data work, from readable Python to reproducible notebooks and version-controlled projects.

Learning outcomes

Write clean Python with functions, modules, classes and error handling
Work confidently with NumPy arrays and vectorised computation
Use pandas for transformation, joins, grouping and time-based data
Visualise distributions and relationships with Matplotlib and Seaborn
Debug notebooks and structure analysis as reusable Python projects
Track experiments and collaborate with Git and GitHub

Tools & methods

PythonNumPypandasJupyterGit

Use probability and inference to measure uncertainty, test claims and design experiments without overstating what the data proves.

Learning outcomes

Describe distributions, variance, covariance and sampling behaviour
Apply conditional probability and Bayes' theorem to real decisions
Estimate confidence intervals and interpret practical uncertainty
Choose and run appropriate hypothesis tests with clear assumptions
Design A/B tests, power checks and guardrail metrics
Recognise confounding, leakage, bias and causal limitations

Tools & methods

StatisticsProbabilityA/B testingSciPyPython

Turn raw relational data into model-ready features while preserving lineage, quality checks and the meaning of each observation.

Learning outcomes

Query relational data with joins, CTEs and window functions
Profile missingness, outliers, duplicates and inconsistent categories
Build leakage-safe transformations and reusable feature pipelines
Explore univariate, bivariate and multivariate patterns carefully
Handle imbalanced, temporal and high-cardinality datasets
Document data lineage, assumptions and reproducibility checks

Tools & methods

SQLPostgreSQLpandasFeature engineeringEDA

Train, compare and explain regression and classification models using evaluation choices that match the business cost of errors.

Learning outcomes

Build strong baselines before increasing model complexity
Train linear models, trees, random forests and gradient boosting
Use cross-validation, pipelines and principled hyperparameter search
Choose metrics for imbalance, ranking, calibration and cost sensitivity
Interpret feature effects with SHAP and model-specific diagnostics
Detect overfitting, leakage and unstable performance before release

Tools & methods

scikit-learnXGBoostSHAPOptunaML pipelines

Find structure when labels are limited and build models for segmentation, recommendations and time-dependent decisions.

Learning outcomes

Use clustering with meaningful validation and profile interpretation
Reduce dimensions for visualisation, compression and modelling
Build collaborative and content-based recommendation baselines
Engineer temporal features and use leakage-safe backtesting
Model trend, seasonality and external drivers in forecasts
Quantify forecast uncertainty and compare against naive baselines

Tools & methods

ClusteringRecommendationsForecastingTime seriesPython

Understand neural-network training, then apply modern architectures to image and text problems with responsible evaluation.

Learning outcomes

Build and train neural networks with PyTorch and TensorFlow
Diagnose optimisation, regularisation and generalisation behaviour
Use CNNs and transfer learning for image classification
Prepare text, embeddings and sequence models for NLP tasks
Fine-tune pretrained models with appropriate validation
Audit subgroup performance, failure cases and compute trade-offs

Tools & methods

PyTorchTensorFlowComputer visionNLPHugging Face

Build grounded language-model applications while treating retrieval, evaluation, safety and cost as core engineering work.

Learning outcomes

Understand tokens, embeddings, transformers and context windows
Design prompts and structured outputs for repeatable behaviour
Build retrieval-augmented generation with chunking and reranking
Evaluate faithfulness, relevance, latency and cost systematically
Add citations, guardrails and human-review paths
Compare prompting, retrieval and fine-tuning trade-offs

Tools & methods

LLMsRAGEmbeddingsVector searchEvaluation

Package a model as a dependable service and design the operational checks required after the first prediction.

Learning outcomes

Track data, code, parameters and model artefacts with MLflow
Serve predictions through FastAPI with validation and tests
Containerise services with Docker and automate quality checks
Deploy batch and real-time inference patterns on cloud platforms
Monitor drift, latency, quality and business outcomes
Plan retraining, rollback, security and responsible ownership

Tools & methods

MLflowFastAPIDockerCI/CDMonitoring

Own an end-to-end problem from discovery through deployment choices, then present the work with the depth technical interviews demand.

Learning outcomes

Frame a consequential problem, constraints and success criteria
Create a reproducible data and experimentation plan
Build, evaluate and document a complete modelling system
Write a model card, architecture note and recruiter-ready case study
Present technical trade-offs to both technical and business audiences
Practise Python, statistics, ML system design and case interviews

Tools & methods

CapstoneModel cardSystem designPortfolioInterview prep

Every module ends with evidence: code, an experiment, an evaluation report or a system you can explain, not only a completion tick.

Tools you will work with

From notebook to production system.

Use the programming, modelling, cloud and evaluation tools that connect modern Data Science work from raw data to a monitored service.

Python
Jupyter
NumPy
pandas
Polars
DuckDB
PostgreSQL
Git
Apache Spark

scikit-learn
TensorFlow
PyTorch
Hugging Face
XGBoost
SHAP
Optuna
OpenAI

MLflow
FastAPI
Docker
Kubernetes
Airflow
DVC
OpenTelemetry
AWS
Google Cloud
Microsoft Azure

OpenAI
Hugging Face
LangChain
LangGraph
Qdrant
MLflow
Pydantic
OpenTelemetry

Portfolio projects for 2026 roles

Build evidence,not leaderboard scores.

Each brief mirrors current hiring conversations: data contracts, strong baselines, evaluation, human review, observability and cost, not only model accuracy.

System brief
Financial risk
Graph-aware fraud decision engine
Combine transaction, device and network signals; calibrate cost-sensitive thresholds and explain alerts for investigators.
Portfolio output
Real-time scorer + investigation playbook
XGBoostGraph featuresSHAP
System brief
Supply chain
Probabilistic demand & inventory planner
Forecast hierarchical demand with prediction intervals, then simulate stockout and overstock decisions across SKUs.
Portfolio output
Forecast API + inventory simulator
PythonDuckDBMLflow
System brief
Personalisation
Two-stage recommendation & ranking service
Build candidate retrieval and learning-to-rank, then audit relevance, diversity, latency and cold-start performance.
Portfolio output
Ranking API + offline evaluation suite
PyTorchEmbeddingsFastAPI
System brief
Multimodal AI
Multimodal quality-inspection copilot
Combine vision detection and a vision-language model to explain defects and route uncertain cases to human review.
Portfolio output
Vision service + review workflow
PyTorchHugging FaceDocker
System brief
Customer growth
Uplift-driven retention decision engine
Estimate who benefits from an intervention instead of only predicting churn, with fairness and budget constraints.
Portfolio output
Uplift policy + experiment design
scikit-learnCausal MLSHAP
System brief
Agentic knowledge systems
Citation-grounded research agent
Use hybrid search, reranking and tool calls; evaluate answer quality, citations, latency, cost and unsafe actions.
Portfolio output
Agent app + regression evaluation suite
LangGraphQdrantMLflow
System brief
Industrial AI
Predictive-maintenance anomaly system
Detect early equipment risk from multivariate sensor streams and monitor lead time, drift and false alarms.
Portfolio output
Batch pipeline + monitoring dashboard
Time seriesAirflowOpenTelemetry
System brief
Production AI
Production ML platform capstone
Own data contracts, experiment tracking, serving, observability and rollback for one consequential ML product.
Portfolio output
Deployed system + architecture case study
FastAPIDockerOpenTelemetry

System brief
Financial risk
Graph-aware fraud decision engine
Combine transaction, device and network signals; calibrate cost-sensitive thresholds and explain alerts for investigators.
Portfolio output
Real-time scorer + investigation playbook
XGBoostGraph featuresSHAP
System brief
Supply chain
Probabilistic demand & inventory planner
Forecast hierarchical demand with prediction intervals, then simulate stockout and overstock decisions across SKUs.
Portfolio output
Forecast API + inventory simulator
PythonDuckDBMLflow
System brief
Personalisation
Two-stage recommendation & ranking service
Build candidate retrieval and learning-to-rank, then audit relevance, diversity, latency and cold-start performance.
Portfolio output
Ranking API + offline evaluation suite
PyTorchEmbeddingsFastAPI
System brief
Multimodal AI
Multimodal quality-inspection copilot
Combine vision detection and a vision-language model to explain defects and route uncertain cases to human review.
Portfolio output
Vision service + review workflow
PyTorchHugging FaceDocker
System brief
Customer growth
Uplift-driven retention decision engine
Estimate who benefits from an intervention instead of only predicting churn, with fairness and budget constraints.
Portfolio output
Uplift policy + experiment design
scikit-learnCausal MLSHAP
System brief
Agentic knowledge systems
Citation-grounded research agent
Use hybrid search, reranking and tool calls; evaluate answer quality, citations, latency, cost and unsafe actions.
Portfolio output
Agent app + regression evaluation suite
LangGraphQdrantMLflow
System brief
Industrial AI
Predictive-maintenance anomaly system
Detect early equipment risk from multivariate sensor streams and monitor lead time, drift and false alarms.
Portfolio output
Batch pipeline + monitoring dashboard
Time seriesAirflowOpenTelemetry
System brief
Production AI
Production ML platform capstone
Own data contracts, experiment tracking, serving, observability and rollback for one consequential ML product.
Portfolio output
Deployed system + architecture case study
FastAPIDockerOpenTelemetry

Code, evaluation, failure analysis and system decisions in every major brief.

Cross-domain builds · reproducible experiments · one production-minded portfolio

Data Science success stories

From model practice tocareer-ready systems.

See how learners used applied modelling, evaluation and production-minded capstones to build stronger Data Science career stories.

Aarav Mehta, Interview Prep learner — Aarav Mehta

Maya Thompson, Interview Prep learner — Maya Thompson

Nikhil Sharma, Interview Prep learner — Nikhil Sharma

Grace Liu, Interview Prep learner — Grace Liu

Kabir Malhotra, Interview Prep learner — Kabir Malhotra

Sara Khan, Interview Prep learner — Sara Khan

Ibrahim Adeyemi, Interview Prep learner — Ibrahim Adeyemi

Elena Petrova, Interview Prep learner — Elena Petrova

Rahul Mehta, Interview Prep learner — Rahul Mehta

Yuki Tanaka, Interview Prep learner — Yuki Tanaka

Data Science, ML & AI10–12 months track · 45+ industry projects · Awarded with Distinction

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Microsoft credentialData Science certificate

Sample previews. Learner details are added at issuance.

Data Science credentials

Show the depth behind your Data Science skills.

Pair your Interview Prep Data Science certificate with a recognised Microsoft credential and make your modelling, evaluation and production work easier to trust.

What hiring teams see

Make technical depth visible

Connect Python, statistics, machine learning, deep learning, GenAI and MLOps in one credible skills narrative.

Back it with 45+ applied projects

Back the certificate with reproducible experiments, model evaluation and production-minded systems you can defend.

Be ready when opportunity moves

Share a clear credential record whenever a recruiter, referral or hiring manager asks for proof.

For technical roles, a credential becomes stronger when the experiments, decisions and deployed thinking behind it are visible.

Recognised platformsMicrosoft

Programme fee