Skillmaxx Academy
Data analyst reviewing a business dashboard and predictive-model output

Career-focused analytics · BI · Applied machine learning

AI Data Analyst

Build the skills, portfolio and interview confidence to compete for data roles.

Train across Excel, SQL, Python, Power BI and applied machine learning through 12 employer-relevant projects and structured career preparation.

₹35,999

inclusive of GST

MRP ₹59,999

You save ₹24,000 (40% off)

EMI starting from ₹4,000/month. Eligibility and terms apply.

View Curriculum
80 live, instructor-led hours
12 portfolio-ready builds
Analytics, BI and applied ML
Resume and portfolio preparation
Interview preparation
Online + Gurgaon classroom

Course fit

A serious path into modern analytics roles

Start with spreadsheet familiarity, then build the coding, analytical judgment, dashboards and predictive-model experience expected in practical data work.

Starting level
Basic spreadsheet familiarity is useful.
Coding
No previous Python, SQL or machine-learning experience is required.
Quantitative foundation
Comfort with numbers, charts and structured problem-solving is expected; the required statistics and model intuition are developed in the programme.
Laptop
A Windows-capable laptop is recommended for Power BI Desktop, Python and local ML workflows.
Practice
Plan six to eight hours per week outside live classes during the Python, SQL and ML phases.

Applied machine learning

Move from analysis to responsible prediction

Build on data preparation, Python and applied statistics to train, compare and explain machine-learning models for practical business problems.

Choose the right problem
Identify the target, available evidence, business cost of errors and whether ML is appropriate at all.
Prepare trustworthy data
Clean data, engineer useful features and prevent information leakage.
Train and evaluate models
Compare practical models using metrics that match the business decision.
Explain and use predictions
Interpret results, communicate limitations and turn model output into action.

Statistics and model intuition are developed alongside hands-on modelling, with consistent quantitative practice expected.

What employers expect

Learn the work, not just the tools

Develop the end-to-end capability to frame a business question, prepare reliable data, analyse it, build decision-ready dashboards, train predictive models and defend your conclusions.

1
Frame the right business question
Translate a business problem into measurable questions, targets, metrics and a structured analysis plan.
2
Prepare reliable data
Clean, combine and validate messy data using spreadsheets, Power Query, SQL and Python.
3
Analyse data with Python and SQL
Build reproducible notebooks and queries that reveal patterns, drivers and exceptions.
4
Apply statistics with practical judgment
Understand variation, uncertainty, relationships and experiment results without relying on unsupported conclusions.
5
Build decision-focused dashboards
Use Power BI, DAX and visual design to communicate performance and priorities.
6
Create predictive models
Build regression, classification and clustering workflows with scikit-learn.
7
Evaluate and explain ML responsibly
Select appropriate metrics, detect overfitting and leakage, explain model behaviour and state limitations.
8
Present an end-to-end portfolio
Publish documented SQL, Python, BI and applied-ML work that you can explain in interviews and project discussions.

Your next career move

Build from where you are. Prepare for where you want to go.

College students and fresh graduates
Build practical analytics, dashboard and predictive-model experience before applying for entry-level data roles.
Working analysts and reporting professionals
Extend spreadsheet and BI work into Python, SQL, model evaluation and predictive analytics.
Career switchers
Follow a structured route from business data and dashboards to applied machine learning and portfolio evidence.
Finance, marketing, HR, sales and operations teams
Analyse performance, identify drivers, forecast outcomes and communicate recommendations using familiar business datasets.
Managers and business owners
Ask better questions from data and evaluate AI-assisted analysis or predictive models without surrendering judgment.

No previous Python, SQL or machine-learning experience is required. The programme builds progressively, with regular hands-on practice between sessions.

Career opportunities

Career paths this programme prepares you to pursue

Target analytics and BI opportunities across marketing, sales, operations, finance, customer intelligence and technology, with pathways into applied ML and data science.

Analytics and business intelligence roles
  • Data Analyst / Junior Data Analyst
  • AI Data Analyst
  • Business Data Analyst
  • BI / Power BI Analyst
  • Reporting / MIS Analyst
  • SQL Data Analyst
  • Data Visualisation Analyst
  • Analytics Associate
  • Marketing / Sales / Operations Data Analyst
  • Customer Insights / CRM / Retail Analytics Analyst
  • Predictive Analytics Analyst
Applied ML and data science pathways
  • Product Data Analyst
  • Decision Science Analyst
  • Machine Learning / AI-ML Analyst
  • Junior / Associate Data Scientist
  • Junior AI/ML Engineer
  • Junior Machine Learning Engineer

Your profile assessment helps identify the strongest first targets for your background and the capability gaps to close next.

Your job-ready portfolio

Graduate with proof of what you can do

When an interviewer asks what you can do, answer with documented work that shows how you think, build, validate and communicate.

1
Clean and analyse real data
Cleaned datasets, documented transformation steps, SQL query sets and reproducible Python notebooks.
2
Build dashboards that guide decisions
Decision-focused Power BI dashboards, DAX measures, data models and a Tableau insight story.
3
Create and explain predictive models
Regression, classification and segmentation workflows with evaluation metrics, model explanations and stated limitations.
4
Present an end-to-end business case
An explainable Streamlit ML application, a cross-tool capstone and selected GitHub-ready project documentation.

Select, refine and document your strongest builds as portfolio case studies that make the problem, method, evidence and recommendation easy to understand.

From course to career

Turn your learning into a focused job search

Structured career preparation helps you choose realistic roles, present your strongest evidence and walk into interviews ready to explain your work.

  1. 1

    Profile and role assessment

    Map your education, experience, current skills and career objective to the roles that offer the strongest fit.

  2. 2

    Resume building

    Turn your experience and course projects into concise, outcome-focused resume points for your target roles.

  3. 3

    Portfolio building

    Shape your strongest builds into case studies that make your problem-solving and technical capability easy to assess.

  4. 4

    Interview preparation

    Practise your project walkthroughs, technical decisions, business recommendations and role-relevant interview questions.

You leave with a clearer target-role strategy, stronger application material and a practised interview story.

Career growth potential

See how earning potential grows with skills and experience

Use your experience band to explore blended India-market benchmarks across analytics, business intelligence and applied-ML roles.

Launch your data career
0-2 years
Market median
₹5.5 LPA
Typical average
₹6.5 LPA
Earning range
₹3-12 LPA+
Move into specialist roles
Around 5 years
Market median
₹12 LPA
Typical average
₹13.5 LPA
Earning range
₹7-24 LPA+
Lead higher-value decisions
Around 10 years
Market median
₹21 LPA
Typical average
₹24 LPA
Earning range
₹12-40 LPA+

Blended India-market benchmarks across the career paths shown above; reviewed September 2026.

Actual compensation varies by role, employer, location, experience and demonstrated capability.

Your project portfolio

Build the work you will discuss in interviews

Complete 12 guided, employer-relevant workflows using datasets, queries, notebooks, dashboards and predictive models. Refine your strongest work into clear portfolio case studies.

Illustrative guided build for AI-Assisted Analysis Brief
Guided foundationBuild 1
AI-Assisted Analysis Brief
Convert a loosely defined business problem into questions, data requirements, validation checks and a presentation plan.
ChatGPTGemini

You leave with: A reusable analysis brief with verification checkpoints.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Excel and Power Query Cleaning Workbook
Guided foundationBuild 2
Excel and Power Query Cleaning Workbook
Clean, combine and profile spreadsheet data using Power Query, pivots, charts and formula assistance.
ExcelPower Query

You leave with: An analysis-ready workbook with a documented cleaning trail.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Google Sheets Decision Tracker
Guided foundationBuild 3
Google Sheets Decision Tracker
Build a collaborative KPI workflow with formulas, charts, Gemini assistance and an executive summary.
Google SheetsGemini

You leave with: A shared tracker that turns live data into decisions.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Python Exploratory Analysis Notebook
Portfolio analytics buildBuild 4
Python Exploratory Analysis Notebook
Use pandas, NumPy and visualisation libraries to investigate a dataset and document caveats and recommendations.
PythonJupyterpandas

You leave with: A reproducible notebook with evidence, caveats and recommendations.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for SQL Business Analysis
Portfolio analytics buildBuild 5
SQL Business Analysis
Query a relational sales or customer database using joins, aggregations, subqueries and window functions.
SQLSQL Server

You leave with: A documented query set that answers business questions.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Power BI Executive Dashboard
Portfolio analytics buildBuild 6
Power BI Executive Dashboard
Transform source data, define DAX measures and build an interactive performance dashboard.
Power BIDAXPower Query

You leave with: An executive dashboard organised around decisions.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Tableau Insight Story
Portfolio analytics buildBuild 7
Tableau Insight Story
Create an interactive visual story with filters, parameters, hierarchy and a concise narrative.
Tableau

You leave with: A visual explanation of a finding and its implication.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Customer Churn Predictor
Applied-ML portfolio buildBuild 8
Customer Churn Predictor
Prepare customer data, train classification models, compare metrics and identify actionable risk.
Pythonpandasscikit-learn

You leave with: A classification workflow that connects prediction to retention action.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Sales or Demand Prediction
Applied-ML portfolio buildBuild 9
Sales or Demand Prediction
Build a regression model, compare a baseline, inspect errors and explain the limits of the forecast.
Pythonscikit-learn

You leave with: A defensible prediction workflow using business-appropriate regression metrics.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Customer Segmentation Engine
Applied-ML portfolio buildBuild 10
Customer Segmentation Engine
Use K-means and conceptual PCA to create, profile and name useful customer segments.
Pythonscikit-learn

You leave with: Actionable segments tied to differentiated business treatment.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Explainable ML Decision App
Applied-ML portfolio buildBuild 11
Explainable ML Decision App
Serve a trained model through Streamlit and show predictions, probabilities, important features and caveats.
Streamlitscikit-learnSHAP

You leave with: A small usable ML application rather than an isolated notebook.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Retail Customer Intelligence Capstone
Portfolio capstoneBuild 12
Retail Customer Intelligence Capstone
Pull data with SQL, clean and explore it in Python, segment customers, predict churn, publish results in Power BI and document recommendations.
SQLPythonscikit-learnPower BIGitHub

You leave with: An end-to-end portfolio capstone from raw data to executive recommendation.

Illustrative project output. Interfaces and platform versions may change.

Your learning platform

Live classes end. Your learning workspace remains available.

Review analytics, statistics, ML and BI through recordings, datasets, starter notebooks, SQL files, dashboard assets, model-evaluation checklists, project briefs and LMS doubts support.

Approx. one-year LMS access

Course dashboard

See your course, live sessions and learning access in one place.

1 of 3
Course dashboard in the SkillmaxX learning platform
Structured learning path in the SkillmaxX learning platform
Learning progress in the SkillmaxX learning platform

Live + LMS

How your learning works

1
Prepare in the LMS
Review prerequisites, starter files, required accounts and setup instructions.
2
Learn the concept live
Understand the reasoning, architecture and practical use case before building.
3
Watch the complete demonstration
See the instructor build, test and troubleshoot the workflow.
4
Build alongside the instructor
Implement the workflow using your laptop and the supplied course files.
5
Test and improve
Validate outputs, investigate failures and document your decisions.
6
Revisit and extend
Use recordings and LMS resources to adapt the build for your portfolio.
Munir Suri, Founder and Lead Instructor at SkillmaxX Academy

Your instructor

Connect analytical tools, predictive models and business judgment

Munir Suri

Founder & Lead Instructor - SkillmaxX Academy

Engineer | IIM Kozhikode alumnus | Former Director, A.T. Kearney | Full Stack Development, IIT Madras | Digital Marketing, ISB Hyderabad

Munir Suri brings together management education, technology capability and transformation experience. His teaching connects analytical and predictive methods with the questions managers and operating teams actually need to answer.

Live sessions focus on reasoning, validation, model evaluation and communication as well as tool use, so learners can explain how a conclusion or prediction was reached and where its limitations lie.

Munir leads the analytics workflow, AI-assistance, applied-ML and portfolio sessions, supported by the structured LMS learning path.

Structured progression

Detailed 80-hour curriculum

Move from AI and spreadsheet foundations through Python, applied statistics, SQL, business intelligence, practical machine learning and portfolio publishing. 315 structured learning units are included.

Phase 1 · AI-assisted analytics foundations · 6 hours

Phase 2 · Spreadsheet analysis and data preparation · 9 hours

Phase 3 · Python for data analysis · 9.5 hours

Phase 4 · Applied statistics for analytics and ML · 10 hours

Phase 5 · SQL and databases · 10 hours

Phase 6 · Applied machine learning · 16.5 hours

Phase 7 · BI and modern analytics · 15.5 hours

Phase 8 · Portfolio and career projects · 3.5 hours

Analytics + applied-ML stack

Learn the workflow from raw data to prediction and decision

Core platforms receive hands-on use. AI assistants support question framing, coding, debugging and documentation, but learners remain responsible for validation, evaluation and judgment.

Core hands-on stack

EA
Excel and Power QueryCleaning, modelling and spreadsheet analysis
GS
Google Sheets and GeminiCollaborative analysis and reporting
PJ
Python, Jupyter, pandas and NumPyReproducible analysis and data preparation
SA
SQL and SQL ServerRelational business analysis
SL
scikit-learnRegression, classification, clustering and evaluation
PB
Power BI and DAXDecision-focused business intelligence
MS
Matplotlib, Seaborn, Plotly and TableauAnalytical visualisation and storytelling
SA
Streamlit and GitHubML application delivery, documentation and portfolio publishing

Additional tools and awareness

AI assistance

ChatGPTGeminiClaudeCopilotCodex

ML extensions

XGBoostSHAPjoblibpickle

AI spreadsheet self-study

BricksRowsFormulaBot

Cloud analytics awareness

SnowflakeAWSAzure

Platform interfaces and model libraries change. The programme preserves the analysis, evaluation and communication outcomes while updating specific tool steps when required.

A complete programme

What your course fee includes

Approximately 80 hours of live instructor-led learning
12 guided analytics and applied-ML builds
Session recordings
Approximately one year of LMS access
Datasets, starter notebooks, SQL files, dashboard assets and project briefs
Model-evaluation checklists and model-card templates
LMS doubts support
Profile assessment and role-targeting support
Resume, portfolio and interview preparation
Completion certificate for eligible learners

Requirements and costs

Prepare for consistent hands-on practice

Basic comfort with spreadsheets, files, folders and internet-based tools.

A Windows-capable laptop that can run Excel, Power BI Desktop, Python/Jupyter and Streamlit workflows.

No prior Python, SQL or ML experience is required.

Comfort with school-level mathematics, percentages, charts and structured problem-solving is expected; the required applied statistics and model intuition are taught in the programme.

Plan six to eight hours of practice per week during the Python, SQL and ML phases.

Third-party tool costs
The course fee does not include optional Microsoft, AI-assistant or cloud subscriptions. Free or learner-managed alternatives are used where practical, and required accounts are listed before each lab.

Course completion

SkillmaxX completion certificate

Complete the published learning, practical work and course requirements to receive a SkillmaxX Academy completion certificate.

The certificate records completion of the programme. Your guided builds and documented project work provide the practical evidence behind it.

Sample SkillmaxX Academy completion certificate

Course fee

Invest in job-ready analytics capability

Course fee

₹35,999

inclusive of GST

MRP ₹59,999

You save ₹24,000 (40% off)

EMI starting from ₹4,000/month. Eligibility and terms apply.

Your fee includes approximately 80 live hours, 12 guided builds, recordings, approximately one year of LMS access, datasets and starter files, LMS doubts support, profile assessment, resume and portfolio guidance, interview preparation and a completion certificate for eligible learners.

EMI options may be available for eligible learners. No payment is required to request a course guidance call.

Live online and Gurgaon classroom formats are available. Your preferred mode and batch will be confirmed with you after admission.

Questions before joining

Frequently asked questions

Prepare to compete for your next data role

Build the practical skills, employer-facing portfolio and interview story to pursue opportunities across analytics, BI and applied machine learning.

80 live hours · 12 portfolio builds · Structured career preparation