I do not have a technical or coding background. Can I still keep up?

Yes. The course is designed for recent graduates, career switchers and professionals from business or operations backgrounds. It begins with the foundations and builds toward SQL, dashboards, business analysis and portfolio work, so prior programming experience is not required.

I am not strong at mathematics. Will that hold me back?

You do not need advanced mathematics. The course focuses on practical concepts such as percentages, averages, comparisons, distributions and introductory statistics, with emphasis on interpreting results correctly rather than memorising complex proofs.

Can I complete the course while working full-time?

The learning journey is designed for working professionals as well as graduates. Live guidance, recorded sessions and a structured six-month path let you learn without putting your career on pause. An advisor can confirm the current cohort schedule before you enrol.

I am unsure whether Data Analytics or Data Science is the right path for me. What is the difference?

Data Analytics focuses on answering business questions with spreadsheets, SQL, statistics, dashboards and clear communication. Data Science usually goes further into programming, machine learning and predictive modelling. This course is the better fit if you want to turn business data into decisions and pursue analyst-focused roles.

Will I learn isolated tools, or the complete analyst workflow?

You will learn Excel, SQL, Power BI, Tableau and GenAI-assisted workflows as connected parts of one process: framing a question, preparing and querying data, finding patterns, building dashboards and explaining a recommendation.

Will I finish with portfolio evidence, or only a certificate?

You will complete business-focused projects across sales, customers, operations, marketing and reporting. Your strongest work is developed into documented case studies with queries, dashboards, methods and recommendations that you can explain in interviews.

Will using Generative AI weaken my core analytics skills?

No. Generative AI is taught as an accelerator, not a substitute for analysis. You will use it to draft queries, document data, explore hypotheses and tailor summaries while validating important outputs against the source data and keeping human judgement in control.

What is included in 100% placement?

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

Career-focused programmeCareer-focused analyst programme

Data Analytics with Generative AI

Master the modern analyst stack and build work you can confidently defend in an interview.

guided programme: 0 months
placement: 0%
industry credential: Microsoft

Who this course is for

Start where you are.Build what matters next.

Choose the situation closest to where you are today. Each starting point leads into the same practical analytics foundation, shaped around the experience you already bring.

Recent graduate or fresher
Build core skills and credible portfolio proof.
Career direction
Entry-level analyst roles
Career switcher
Turn your existing domain knowledge into analytical evidence.
Career direction
Analyst or operations roles
Working professional
Upskill through guided projects without putting your career on pause.
Career direction
More analytical responsibility
Business or operations professional
Move from tracking KPIs to explaining patterns and recommending action.
Career direction
Business or product analytics

The complete learning experience

A course should connect the work,not just cover the tools.

Live learning, 36+ domain-specific projects, the complete analyst stack and career preparation, connected in one guided journey.

Learn live

Learn with practitioners who connect each skill to real analyst work.

Live classes
Questions answered
Personal feedback

36+ domain-specific projects

Build practical experience with realistic business problems and datasets.

Domain-specific scenarios
Independent practice
Review and improve

Build with the complete analyst stack

Use each tool where it belongs, then connect them in one workflow.

Excel + Power Query
SQL
Power BI
Tableau
GenAI

Turn work into career proof

Turn your strongest work into evidence you can present to recruiters.

Portfolio review
Resume + LinkedIn
Interview practice

By the end, you will not just know the tools. You will think like an analyst and deliver business impact.

Mentors who do the work

Learn from leaders shapingdata, products and AI.

Get practical guidance from practitioners across Google, Microsoft, Apple, Uber and PayPal, grounded in the decisions, systems and trade-offs they handle every day.

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

Course curriculum

Your path from raw data to real decisions.

Eight practical modules build one complete analyst workflow. Open any module to see what you will learn, use and produce.

8 modules: Connected skill path
6 months: Live + guided practice
36+ projects: Portfolio-building work
Capstone + interviews: Career-ready finish

Translate business uncertainty into questions, metrics, data requirements and a practical analysis plan.

Learning outcomes

Turn ambiguous business requests into answerable analytics questions
Define KPIs, calculation rules, data grain and success criteria
Distinguish dimensions, measures and leading versus lagging indicators
Audit data types, missing values, duplicates and source reliability
Identify bias, privacy and ethical interpretation risks
Write an analysis plan with assumptions, checks and stakeholder outcomes

Tools & technologies

ExcelKPI frameworkData qualityAnalytics brief

Turn untidy spreadsheets into reliable, reusable analysis without repeating manual cleanup every month.

Learning outcomes

Structure reliable datasets with Excel Tables and named ranges
Use lookups, conditional logic and error handling confidently
Clean, standardise and validate inconsistent business data
Build pivot-table analysis, charts and management summaries
Import, merge and transform files with Power Query
Create documented refresh workflows with practical QA checks

Tools & technologies

ExcelPower QueryPivotTablesXLOOKUPData validation

Move from a question to a trustworthy query and explain exactly how the result was produced.

Learning outcomes

Read schemas and recognise keys, relationships and table grain
Filter, aggregate and segment data for business questions
Combine datasets with joins, subqueries and common table expressions
Use window functions for ranking, retention and time-based analysis
Write date logic for cohorts, trends and period comparisons
Validate, document and package reusable analytical queries

Tools & technologies

SQLPostgreSQLCTEsWindow functionsDate logic

Describe what happened, compare groups and recognise when the data does not support a confident conclusion.

Learning outcomes

Describe distributions, variability, skew and meaningful outliers
Choose appropriate averages, percentiles and comparison baselines
Recognise sampling bias, uncertainty and misleading summaries
Separate correlation, causation and plausible alternative explanations
Compare cohorts, trends and groups with defensible methods
Run basic hypothesis tests and communicate their limitations

Tools & technologies

StatisticsExcelHypothesis testingCohort analysis

Convert cleaned data into a model and dashboard that supports repeated decision-making.

Learning outcomes

Design a clean star schema with reliable table relationships
Create measures, calculated columns and reusable DAX logic
Build time-intelligence calculations and period comparisons
Design KPIs, filters, drill-throughs and useful interactions
Apply accessible hierarchy, formatting and dashboard navigation
Publish, validate and hand off a decision-ready Power BI report

Tools & technologies

Power BIDAXPower QueryData modellingPower BI Service

Explore patterns visually, choose the right chart and lead a stakeholder from evidence to action.

Learning outcomes

Build calculated fields, parameters, sets and reusable views
Select charts that match the question and comparison being made
Create clear visual hierarchy, annotations and accessible layouts
Design interactive filters, actions and guided exploration paths
Sequence evidence into a focused analytical story
Present executive recommendations with context and next actions

Tools & technologies

TableauCalculated fieldsParametersDashboard actionsStorytelling

Use GenAI as an accelerator while keeping human judgement, source data and verification in control.

Learning outcomes

Write analytical prompts with explicit context, constraints and outputs
Draft SQL, formulas and documentation without surrendering judgement
Use GenAI to explore hypotheses and identify missing questions
Generate audience-aware summaries from verified analysis
Test responses for hallucinations, leakage, bias and calculation errors
Maintain prompt logs, source traceability and human approval controls

Tools & technologies

GenAISQLExcelPrompt designValidation checklist

Bring the complete workflow together in a business problem you can explain from source data to recommendation.

Learning outcomes

Scope a capstone around a credible business decision
Plan the data model, analysis, dashboard and validation approach
Produce documented queries, metrics and decision-ready visuals
Write a recruiter-friendly case study with methods and trade-offs
Present a concise portfolio walkthrough and answer follow-up questions
Practise SQL, analytics and business-case interview scenarios

Tools & technologies

PortfolioInterview prepSQLPower BITableauCase study

Tools you will work with

Real tools for real analyst work.

Work across spreadsheets, databases, BI platforms, developer tools, cloud products and AI assistants used throughout modern analytics teams.

Excel
Power Query
SQL
PostgreSQL
Power BI
DAX
Tableau
Microsoft Fabric

MySQL
Google Sheets
BigQuery
Looker Studio
Google Analytics
Snowflake
Jupyter
Python

Pandas
NumPy
Git
GitHub
VS Code
Microsoft Azure
AWS
Google Cloud

ChatGPT
Claude
Gemini
Microsoft Copilot
Perplexity
GitHub Copilot
NotebookLM
Hugging Face

Portfolio projects

Build proof,not practice files.

Portfolio briefs built around the work modern analysts are expected to explain: trusted metrics, live signals, AI-assisted workflows and clear business decisions.

Portfolio brief
Retail intelligence
AI-assisted revenue intelligence
Build a governed sales model, ask natural-language questions and verify the SQL, DAX and narrative before publishing.
Portfolio output
Executive dashboard + verified-answer log
SQLPower BICopilot
Portfolio brief
Real-time operations
Anomaly control room
Monitor live service metrics, surface unusual patterns and define the analyst review that follows every alert.
Portfolio output
Live monitor + alert-response playbook
FabricKQLPower BI
Portfolio brief
Customer analytics
Retention early-warning system
Connect cohort health, product behaviour and support signals to identify which customer groups need attention first.
Portfolio output
Churn diagnostic + intervention brief
SQLPythonPower BI
Portfolio brief
Supply chain
Stockout risk control tower
Combine orders, inventory and supplier lead times to expose stockout risk, delays and practical reorder priorities.
Portfolio output
Control-tower dashboard + action plan
Power QuerySQLPower BI
Portfolio brief
FinOps
Cloud spend governance dashboard
Allocate platform cost, forecast run-rate and compare unit economics so teams can act on waste without losing context.
Portfolio output
Cost model + savings opportunity backlog
BigQuerySQLPower BI
Portfolio brief
Growth analytics
Marketing incrementality lab
Compare campaign cohorts, conversion lift and spend efficiency to separate useful growth signals from vanity metrics.
Portfolio output
Funnel dashboard + experiment readout
GA4SQLTableau
Portfolio brief
Data governance
Data quality observability scorecard
Track freshness, completeness, duplicates and metric drift, then turn failures into a visible triage workflow.
Portfolio output
Quality scorecard + issue register
SQLPythonPower BI
Portfolio brief
Enterprise BI
Executive metrics semantic layer
Create trusted definitions for revenue, active customers and margin so self-service analysis stays consistent.
Portfolio output
Metric dictionary + certified dashboard
SQLSemantic modelBI

Portfolio brief
Retail intelligence
AI-assisted revenue intelligence
Build a governed sales model, ask natural-language questions and verify the SQL, DAX and narrative before publishing.
Portfolio output
Executive dashboard + verified-answer log
SQLPower BICopilot
Portfolio brief
Real-time operations
Anomaly control room
Monitor live service metrics, surface unusual patterns and define the analyst review that follows every alert.
Portfolio output
Live monitor + alert-response playbook
FabricKQLPower BI
Portfolio brief
Customer analytics
Retention early-warning system
Connect cohort health, product behaviour and support signals to identify which customer groups need attention first.
Portfolio output
Churn diagnostic + intervention brief
SQLPythonPower BI
Portfolio brief
Supply chain
Stockout risk control tower
Combine orders, inventory and supplier lead times to expose stockout risk, delays and practical reorder priorities.
Portfolio output
Control-tower dashboard + action plan
Power QuerySQLPower BI
Portfolio brief
FinOps
Cloud spend governance dashboard
Allocate platform cost, forecast run-rate and compare unit economics so teams can act on waste without losing context.
Portfolio output
Cost model + savings opportunity backlog
BigQuerySQLPower BI
Portfolio brief
Growth analytics
Marketing incrementality lab
Compare campaign cohorts, conversion lift and spend efficiency to separate useful growth signals from vanity metrics.
Portfolio output
Funnel dashboard + experiment readout
GA4SQLTableau
Portfolio brief
Data governance
Data quality observability scorecard
Track freshness, completeness, duplicates and metric drift, then turn failures into a visible triage workflow.
Portfolio output
Quality scorecard + issue register
SQLPythonPower BI
Portfolio brief
Enterprise BI
Executive metrics semantic layer
Create trusted definitions for revenue, active customers and margin so self-service analysis stays consistent.
Portfolio output
Metric dictionary + certified dashboard
SQLSemantic modelBI

Dashboard, documented logic and recommendation in every brief.

Cross-domain briefs · modern tools · one decision-first portfolio

Data Analytics success stories

From learning the tools todoing analyst work.

Meet learners who turned SQL, dashboards and decision-ready portfolio work into credible moves across analytics and product roles.

Daniel Kim, Interview Prep learner — Daniel Kim

Maya Thompson, Interview Prep learner — Maya Thompson

Meera Joshi, Interview Prep learner — Meera Joshi

Amara Okafor, Interview Prep learner — Amara Okafor

Pooja Reddy, Interview Prep learner — Pooja Reddy

Ibrahim Adeyemi, Interview Prep learner — Ibrahim Adeyemi

Priya Desai, Interview Prep learner — Priya Desai

Grace Liu, Interview Prep learner — Grace Liu

Neha Verma, Interview Prep learner — Neha Verma

Divya Menon, Interview Prep learner — Divya Menon

Data Analytics with Generative AI6 months track · 36+ industry projects · Awarded with Distinction

Built to be verifiedID · issue date · award

Ready for the first scanClear proof in seconds

Microsoft credentialData Analytics certificate

Sample previews. Learner details are added at issuance.

Data Analytics credentials

Turn analytical skill into proof hiring teams can scan.

Pair your Interview Prep Data Analytics certificate with a recognised Microsoft credential and show the SQL, dashboard and decision work behind your skills.

What hiring teams see

Signal practical analytics depth

Make Excel, SQL, Power BI, Tableau and GenAI capability visible in one focused credential story.

Back it with 36+ applied projects

Connect the certificate to dashboards, analyses and business decisions you can explain in interviews.

Be ready when opportunity moves

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

When analyst applications look alike, verified proof of practical work gives hiring teams a reason to look again.

Recognised platformsMicrosoft

Programme fee