01
Data Science Foundations
The mental model behind every analysis you will run.
- The data science lifecycle
- Types of data
- Descriptive vs inferential thinking
- Reproducibility
UNLOX Global Programs · Data Science
Data Science & Decision Intelligence
Become the Data Scientist Who Can Analyse,Model & Decide.
In collaboration with
Master applied data science by working with real datasets, building statistical and machine learning models, running experiments and turning analysis into decisions a business can actually act on.
- Project-Based
- Mentor Supported
- Portfolio Driven
- IBM Learning Experience
How data science work happens here
- 01QuestionDefine the decision the analysis actually needs to support.
- 02DataCollect, clean and structure the data behind that decision.
- 03ExploreUnderstand patterns, relationships and data quality.
- 04ModelBuild statistical or machine learning models on the problem.
- 05ValidateTest rigour, bias, significance and generalisation.
- 06CommunicateTurn findings into a decision-ready narrative.
- 07Decide & MonitorShip the decision surface and track real outcomes.
Portfolio output
Evidence, not completion
- Structured Data Analysis ToolkitPublished analysis notebook + data quality reportFoundational
- Statistical Insight EngineStatistical analysis report + reproducible notebookFoundational
- Customer Behaviour Analytics PlatformDeployed analytics platform + segmentation reportApplied
In collaboration with
Built, not watched
Every stage ends in something you shipped.
Reviewed by mentors
Human feedback on architecture and decisions.
IBM learning experience
Industry learning inside the program.
Portfolio as proof
Documented work you can show and defend.
Data Science & Decision Intelligence — Global Program
One structured program, delivered to learners worldwide
- Learning Model
- Project-Based
- Delivery
- Online
- Access
- Global
- Mentorship
- Timezone-Friendly
- Language
- English
- Portfolio
- Production-Style Projects
- IBM Component
- Included
- Eligibility
- Profile Review
Viewing from India
Next global cohort
Build alongside ambitious learners across borders.
Join a structured global learning experience designed around projects, mentor feedback and measurable proof of work.
Cohort intake
Next intake — September 2026
Applications are reviewed on a rolling basis. Cohort size is limited so mentor review stays meaningful.
- Next Cohort
- Next intake — September 2026
- Format
- Online + Project-Based
- Access
- Global
- Language
- English
- Mentor Support
- Timezone-Friendly · Asia / IST
- Applications
- Open
This is not another statistics course
Don't Just Learn Data Science. Decide With It.
Traditional data science learning
vsThe UNLOX way
- Watch statistics lecturesQuestion
- Copy notebooksExplore
- Run sample datasetsModel
- Complete quizzesValidate
- Receive certificateCommunicate
- Still unsure how to turn analysis into a business decisionDeploy
then it compoundsMonitorIterateProve
Knowing statistics and machine learning is useful. Knowing how to turn data into a decision someone will act on is what matters.
Domain overview
What You'll Actually Master
Data science is not one notebook or one model. This program teaches you how data, statistics, machine learning and decision-making work together end to end.
02
Python & SQL for Data Science
The working languages of applied data science, used the way analysts and scientists use them.
- Python data workflows
- Pandas & NumPy
- SQL querying
- Notebook-to-report workflow
03
Data Wrangling
Most analysis failures are data failures. This is where you prevent them.
- Cleaning
- Joining and reshaping
- Missing data
- Outliers
- Data quality checks
04
Statistics & Probability
The reasoning that keeps a conclusion honest.
- Distributions
- Hypothesis testing
- Confidence intervals
- Correlation vs causation
05
Exploratory Data Analysis & Visualisation
Seeing what the data is actually telling you before you model it.
- EDA workflows
- Chart selection
- Dashboards
- Storytelling with data
06
Machine Learning for Decisions
Models built to support a decision, not just score well in a notebook.
- Regression
- Classification
- Clustering
- Feature engineering
- Model evaluation
07
Time Series & Forecasting
Reasoning about the future from patterns in the past.
- Trend and seasonality
- Forecasting models
- Backtesting
- Forecast uncertainty
08
Experimentation & A/B Testing
Proving what actually changed the outcome.
- Experiment design
- A/B testing
- Statistical significance
- Guardrail metrics
09
Business Intelligence & Reporting
Getting insight in front of the people who decide.
- KPI design
- Dashboards
- Automated reporting
- Stakeholder communication
10
Decision Intelligence
Turning prediction into an explainable, approvable recommendation.
- Decision frameworks
- Scenario analysis
- Optimisation basics
- Human-in-the-loop review
11
AI-Assisted Analytics
Using modern AI tools as part of the data science workflow.
- AI-assisted EDA
- Natural-language querying
- Automated summarisation
- Model explanation
12
Deployment & Monitoring
Running data products where decision-makers actually see them.
- Model serving
- Dashboard deployment
- Monitoring drift
- Data pipeline reliability
Data science system architecture
The system you learn to build
Select a part of the system to see what you actually understand, what you build with it, and the project that proves it. You leave understanding the whole decision pipeline — not only a model score.
Getting raw data into a usable, trustworthy shape.
- Ingestion
- Cleaning
- Joining and reshaping
- Missing data
- Data quality checks
Build: A clean, validated dataset ready for analysis.
Proven in: Customer Behaviour Analytics Platform
Projects you will build
Your portfolio is the proof
Each project is scoped like real work: a context, a problem, deliverables and an artifact you can show.
Deliverables
— Business analysis requirement document
— Architecture diagram
— Deployed production data science product
Deliverables
— Working data analysis notebook/tool
— Data quality report
— Exploratory findings summary
Deliverables
— Working statistical analysis tool
— Three tested hypotheses with statistical evidence
— Confidence-interval and significance reporting
Deliverables
— Working analytics platform
— Customer segmentation model
— Interactive dashboard
Deliverables
— Working churn prediction model
— Model evaluation report
— Feature importance explanation
Deliverables
— Working forecasting tool
— Backtested forecast accuracy report
— Forecast confidence ranges
Deliverables
— Working experimentation platform
— Experiment design document
— Statistical test results + significance analysis
Deliverables
— Working BI dashboard system
— Automated data refresh pipeline
— KPI definition document
Deliverables
— Working recommendation engine
— Prediction + explanation layer
— Scenario comparison view
Deliverables
— Deployed data product
— Data and model drift monitoring dashboard
— Pipeline reliability checks
How it works
One loop, repeated until it becomes instinct
Step 01 · Understand the Question
Read the brief the way a data scientist reads a business request.
Curriculum
A structured build path, stage by stage
Every stage names what you learn, what you build with it, and the outcome it produces.
In collaboration with
IBM learning experience components are mapped into the stages below.
The language, tooling and mental model everything else depends on.
Python, SQL & the Data Science Lifecycle
Module 1Learn
Python foundations
SQL querying
Data science lifecycle
Reproducible workflows
Build
Structured Data Analysis Toolkit
Output
You can structure and execute a data analysis workflow independently.
Configurable per curriculum
Learning perks
Everything working on you at once
Support, review, practice and proof are one connected system — the cards move on their own; hover or tap one to see the product behind it.
AI learning support
BLU
Duplicate webhook retries are creating duplicate transactions.
Before changing code: is your payment handler idempotent?
Next step → key each event by its provider event id.
AI learning support01
BLU
Explains concepts, helps debug analysis and guides project thinking.
- Debug prompts, not answers
- Explains the why behind a fix
- Available while you build, 24/7
Asks the next diagnostic question instead of pasting a fix.
Build work02
Projects
Scoped like real work: a brief, constraints, deliverables and a shipped artifact.
- Real brief with constraints
- Scoped deliverables per stage
- Deployed, documented artifact
10 briefs, one flagship build.
Human review03
Mentorship
Human review of methodology, evaluation and modelling decisions.
- Written review on every submission
- Architecture and code-quality feedback
- Direction on what to fix next
Every submission returns with a written review report.
Interview readiness04
PrepFree
AI interview and career preparation.
- Mock interviews on your own builds
- Scored on framing and depth
- Repeat until answers are sharp
Scored on clarity, framing, depth and trade-offs.
Proof of work05
Portfolio
A reviewed, documented, deployed body of work you can show without explaining it away.
- Live deployed links
- Decision log per project
- Review history attached
Live links, decisions log and review history per project.
In collaboration with IBM06
IBM learning experience
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
- IBM learning components mapped in
- Industry-aligned tooling context
- Recognised credential on completion
Structured industry learning · Applied technical exposure · Assessment component
Career support07
Opportunities
Role mapping, portfolio positioning and interview practice tied to your build history.
- Role directions mapped to projects
- Portfolio positioning support
- Interview practice tied to your work
5 role directions mapped to your projects.
Checkpoints08
Assessments
Progress is measured on working systems and defended decisions, not quiz scores.
- Stage gates between builds
- Measured on working systems
- Defend your decisions, not quizzes
Stage gates before you move to the next build.
AI learning support01
BLU
Explains concepts, helps debug analysis and guides project thinking.
- Debug prompts, not answers
- Explains the why behind a fix
- Available while you build, 24/7
Asks the next diagnostic question instead of pasting a fix.
Build work02
Projects
Scoped like real work: a brief, constraints, deliverables and a shipped artifact.
- Real brief with constraints
- Scoped deliverables per stage
- Deployed, documented artifact
10 briefs, one flagship build.
Human review03
Mentorship
Human review of methodology, evaluation and modelling decisions.
- Written review on every submission
- Architecture and code-quality feedback
- Direction on what to fix next
Every submission returns with a written review report.
Interview readiness04
PrepFree
AI interview and career preparation.
- Mock interviews on your own builds
- Scored on framing and depth
- Repeat until answers are sharp
Scored on clarity, framing, depth and trade-offs.
Proof of work05
Portfolio
A reviewed, documented, deployed body of work you can show without explaining it away.
- Live deployed links
- Decision log per project
- Review history attached
Live links, decisions log and review history per project.
In collaboration with IBM06
IBM learning experience
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
- IBM learning components mapped in
- Industry-aligned tooling context
- Recognised credential on completion
Structured industry learning · Applied technical exposure · Assessment component
Career support07
Opportunities
Role mapping, portfolio positioning and interview practice tied to your build history.
- Role directions mapped to projects
- Portfolio positioning support
- Interview practice tied to your work
5 role directions mapped to your projects.
Checkpoints08
Assessments
Progress is measured on working systems and defended decisions, not quiz scores.
- Stage gates between builds
- Measured on working systems
- Defend your decisions, not quizzes
Stage gates before you move to the next build.
Global learning experience
An IBM learning experience, inside an UNLOX program
The program includes an IBM learning experience component alongside the UNLOX AI curriculum. UNLOX delivers the program, the AI projects, the mentorship and the portfolio; the IBM component adds additional structured industry learning exposure.
In collaboration with
UNLOX delivers
- The program and curriculum
- Industry-style project briefs
- Mentor reviews and feedback loops
- Portfolio and career preparation
In collaboration with
The IBM component adds
- Structured industry learningAdditional learning content delivered as part of the IBM learning experience component of this program.
- Applied technical exposureGuided practical material that complements the UNLOX AI project work rather than replacing it.
- Assessment componentWhere included, assessment and completion criteria are stated in the program terms.
Where it sits in your journey
- 1UNLOX AI Program
- 2AI Project Learning
- 3IBM Learning Experience
- 4Portfolio
- 5Career Preparation
Environment details are confirmed in the program documentation shared on application.
Exact scope of the IBM learning experience component, platform details and any certification wording follow the approved program documentation. Nothing here implies IBM employment, placement, internship or a degree.
Credentials
Two credentials: one for the course, one for the work
Every learner receives a course completion credential and a project completion credential — each independently verifiable.
Sample credential shown for illustration. Learner name, project title and verification link are unique to each issued certificate.
IBM learning experience component
Course completion certificate
Issued on unlox.skillsnetwork.site, powered by IBM Developer Skills Network. Verifiable by QR and certificate URL.
- What it represents
- Completion of the IBM learning experience included in this program, with a passing grade on the course assessments.
- How it is earned
Complete the included IBM learning experience component
Receive a passing grade on the course assessments
- How to use it
- Adds verifiable industry learning exposure alongside your UNLOX program record.
QR verification Unique certificate URL
Transformation
The shift this program is built to produce
Capability is the outcome — and it is visible in what you can build, review and defend at the end.
Before
01Where most learners start
- Uses spreadsheets and dashboards casually
- Copies notebooks
- Understands statistics and ML separately
- Has no deployed data product
- Cannot explain evaluation or methodology
During
02What changes while you build
- Structured data science curriculum
- Builds multiple analytical systems
- Validates findings statistically
- Receives mentor reviews
- Learns deployment
- Documents decisions
After
03How you operate at the end
- Multiple deployed data science projects
- Understands modern analytics and decision architecture
- Can discuss modelling and statistical trade-offs
- Can evaluate analytical rigour
- Has documented portfolio evidence
- Can defend own analytical decisions
What exists at the end
- 10 data science builds, deployed and documented
- Evaluation reports per system
- Methodology and architecture diagrams
- A flagship decision intelligence platform
Role explorer
Pick a role. See what you'd be able to do.
Preparation here is capability-based: each role lists what you can actually do, and the project evidence that proves it.
Data Scientist
Turns raw data into modelled, decision-ready insight.
You can
- Turn a business question into an analytical plan
- Clean, explore and model real-world data
- Evaluate and explain model output before it ships
- Communicate findings to non-technical stakeholders
StatisticsMachine learningEDACommunication
Proven through
- Customer Churn Prediction System
- Customer Behaviour Analytics Platform
Portfolio evidence
- Deployed analysis
- Repository
- Evaluation record
- Methodology documentation
Proof of work
You leave with a dossier, not a certificate alone
Everything you build is packaged so a reviewer can evaluate it in minutes.
portfolio.unlox.com/dossier
Deployed data products
Systems running at a URL, not notebook screenshots.
Reviewed by mentorsVerifiable credentialsShareable in one link
Industry ecosystem
Built around
real industry exposure.
Projects, reviews, career preparation and industry interactions connect what you learn to how work actually happens.
UNLOX / Industry ecosystem
LiveLearner
Projects
Mentors
Industry
Portfolio
Career
● Learner
○ Projects
○ Mentors
○ Industry
○ Portfolio
○ Career
Companies in the UNLOX hiring network
Data Science & Decision Intelligence
UNLOX Global Program
₹65,000
Shown for India · INR. Change your country in the header to see local pricing.
Scholarship opportunities may be available for eligible learners.
In collaboration with
What you invest in
- Structured applied data science curriculum
- Mentor methodology and evaluation review
- Ten portfolio-grade data science builds
- IBM learning experience component
- AI interview and career preparation
What you leave with
- Deployed data science systems
- Evaluation reports and methodology documentation
- The ability to defend your analytical decisions
- Credentials and proof of work
Global learner scholarship
Your geography shouldn't limit what you can build.
Scholarship opportunities may be available for eligible learners joining selected UNLOX Global Program cohorts.
Profile-based evaluation
Scholarship eligibility may depend on learner profile and cohort availability.
Selected cohorts
Scholarship availability can vary between programs and intakes.
Global applicants
Eligible international applicants can request an assessment.
Scholarship availability, eligibility and award value are subject to program terms, learner profile and cohort availability.
FAQ
Questions people ask before applying
Yes. The program is open to international learners. Sessions are scheduled to work across major timezone groups, and every live session is recorded so you can catch up if a slot does not suit you.
Apply
Talk to the admissions team
Share your details and our team will connect with you about the Data Science & Decision Intelligence program — live session schedule, 4-month structure, fees and the next intake.
Applications open · Global program
You don't need another course.You need something you've built.
Join UNLOX Data Science & Decision Intelligence and turn learning into projects, projects into proof, and proof into career readiness.
- Global Access
- Timezone-Friendly
- Project-Based
- Mentor Supported
- IBM Learning Experience
- Portfolio Driven
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Data Science & Decision Intelligence
UNLOX Global Program
Check your eligibility in 2 minutes.
Share a few details and our team reviews your profile, cohort fit and scholarship options before any commitment.
Reviewing applications from India · Asia / IST
In collaboration with
More about this program
Data Science & Decision Intelligence at UNLOX
What is applied data science?
Applied data science is the practice of turning raw data into decisions. It covers cleaning and exploring data, applying statistics and machine learning, validating results and communicating findings clearly enough for someone to act on them.
The job is less about knowing every algorithm name and more about being able to take a business question, choose an approach, analyse it, prove it, and explain why the recommendation should be trusted.
What will you learn in this Data Science program?
The UNLOX Global Data Science & Decision Intelligence program covers data science foundations, Python and SQL, data wrangling, statistics and probability, exploratory data analysis, machine learning, time series forecasting, experimentation and A/B testing, business intelligence, decision intelligence and deployment and monitoring.
Each area is taught through building. Learning is verified by what you can produce, reviewed by mentors, and documented as portfolio evidence.
Career directions after the program
Learners target roles such as data scientist, data analyst, machine learning analyst, decision intelligence analyst and business intelligence / analytics engineer. Each role expects evidence: deployed analyses, evaluation records and the ability to defend technical decisions.
Every role you might target is backed by at least one data science system you built, evaluated and documented yourself.
Learning methodology
Learning is project-based. You define the decision question, prepare data, explore it, choose an analytical or modelling approach, build, validate, review, improve, deploy, monitor, document and publish. BLU AI support and human mentor review run alongside every stage.