Mentorship Program

★★★★★

(1,240 ratings) 8,500+ Student

6 Month Mentorship Program in Data Analytics with Gen AI & Agentic AI

From Basic to Advanced. Six specialized tracks, one a month — Advanced Excel | SQL for Data Analytics | Power BI, Basic to Advanced | Python for Data Analytics | Statistics & Business Analytics | GenAI & Agentic AI for Data Analytics. The methodology is Learn → Practice → Analyze → Build → Present → Troubleshoot → Automate → Interview, across 100+ hands-on labs, 60+ assignments, 25+ mini projects, 6 major capstones and 1 integrated enterprise capstone. No prior advanced Excel experience is required — Month 1 starts at fundamentals and the programme finishes with natural-language analytics, RAG and an agentic AI data analyst.

Radical Technologies

8,500+ English 6 Tracks · 6 Months · Excel to Agentic AI

Online / Classroom

6 Month Mentorship Program in Data Analytics

With GenAI & Agentic AI — From Basic to Advanced

Duration 6 Months (6 Specialized Tracks)

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Mentorship Certificate + Global Certification Roadmap

Tools you'll master

Microsoft Excel Power Query Power Pivot DAX SQL Server PostgreSQL MySQL Oracle Azure SQL Power BI Python Pandas NumPy Matplotlib Seaborn SciPy Jupyter LLMs AI APIs RAG Embeddings Vector Databases LangChain LangGraph LlamaIndex Function Calling AI Agents

Next batch: 10/08/2026 · Online

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Why This Course?

6 specialized tracks completed over 6 months, one track a month

Methodology: Learn → Practice → Analyze → Build → Present → Troubleshoot → Automate → Interview

100+ hands-on labs and exercises across the six tracks

60+ assignments and 25+ mini projects

6 major capstones plus 1 integrated enterprise capstone

GenAI, RAG and Agentic AI applied directly to business analytics

Business analytics portfolio and mock interviews throughout the programme

Prerequisites

Basic Computer Knowledge

Basic Arithmetic

No Prior Advanced Excel Experience Required

Programme Overview

6 specialized tracks, one a month — Advanced Excel, SQL for Data Analytics, Power BI, Python for Data Analytics, Statistics & Business Analytics, and GenAI & Agentic AI. Each month ends in a major outcome, from Advanced Excel Data Analyst through to AI-Enabled Data Analyst.

6 months

Programme Duration

Specialized Tracks

Total Modules

4.8★

Average Rating

50K+

Students Trained

Month 1 — Advanced Excel

A job-oriented Advanced Excel programme covering data preparation, formulas, lookups, PivotTables, dashboards, automation and business reporting. Outcome: Advanced Excel Data Analyst.

XLOOKUP INDEX & MATCH PivotTables Power Query Power Pivot Dashboards

Month 2 — SQL for Data Analytics

SQL from fundamentals through advanced analytical querying, data exploration, business reporting and query optimization. Outcome: SQL Data Analyst.

Joins CTEs Window Functions Cohort Analysis Indexes Query Optimization

Month 3 — Power BI, Basic to Advanced

Complete Power BI training covering data preparation, data modeling, DAX, visualization, dashboards, reporting, publishing and advanced analytics. Outcome: Power BI Analyst.

Power Query Star Schema DAX Time Intelligence Row-Level Security Power BI Service

Month 4 — Python for Data Analytics

Use Python to perform data cleaning, exploratory analysis, statistical analysis, visualization and automation. Outcome: Python Data Analyst.

NumPy Pandas Matplotlib Seaborn EDA Report Automation

Month 5 — Statistics & Business Analytics

The statistical and business reasoning skills required to convert raw data into actionable business insights. Outcome: Business / Data Analyst.

Hypothesis Testing Regression A/B Testing KPI Design Churn Analysis Forecasting

Month 6 — GenAI & Agentic AI for Data Analytics

How GenAI and Agentic AI enhance analytics through natural-language analytics, automated reporting, insight generation, RAG, AI-assisted visualization and intelligent workflows. Outcome: AI-Enabled Data Analyst.

Prompt Engineering Natural-Language-to-SQL RAG AI Agents Automated Reporting AI Security

Who is this programme for?

Whether you're a fresher, an MIS or reporting executive, a business or domain professional, a SQL user or already working in IT — this mentorship is built to take you into a Data Analyst, Business Analyst, BI Analyst, Power BI Developer, Python Data Analyst or GenAI Data Analyst role.

Students & Freshers

No prior advanced Excel experience needed — Month 1 starts at fundamentals and builds towards Data Analyst and Reporting Analyst roles.

MIS & Reporting Executives

Move from manual reporting into MIS Analyst, BI Analyst and Business Intelligence Analyst roles with SQL and Power BI.

Business & Domain Teams

Step into Business Analyst, Marketing Data Analyst, Financial Data Analyst and Operations Data Analyst roles.

SQL & Database Users

Grow into SQL Data Analyst, Power BI Developer and Data Visualization Analyst roles.

Aspiring Python Analysts

Target Python Data Analyst, Product Data Analyst and BI & Analytics Consultant roles through Pandas, EDA and automation.

AI-Focused Analysts

Target GenAI Data Analyst, AI-Assisted Business Analyst and Agentic AI Analytics Specialist roles with RAG and agents.

Course Curriculum

6 months · 6 specialized tracks

6 tracks • 36 modules • one track a month, with labs, assignments and a capstone in each

Month 01 of 06 6 Modules Month 1 · Basic to Advanced

Advanced Excel

A job-oriented Advanced Excel program covering data preparation, formulas, lookups, PivotTables, dashboards, automation and business reporting.

Course Content

— Prerequisites

— Module 1: Fundamentals

Topics:

Excel interface and workbook management
Data types
Rows, columns and tables
Data entry and validation
Relative and absolute references
Basic formulas
Spreadsheet best practices
Business data structures

— Module 2: Advanced Excel Formulas

Topics:

IF / IFS
SUMIF / SUMIFS
COUNTIF / COUNTIFS
AVERAGEIF / AVERAGEIFS
XLOOKUP
VLOOKUP / HLOOKUP
INDEX & MATCH
IFERROR
TEXT functions
DATE functions
Logical functions
Dynamic array formulas

— Module 3: Data Preparation

Topics:

Data cleaning
Remove duplicates
Text-to-columns
Flash Fill
Data validation
Conditional formatting
Data transformation

— Module 4: Analysis

Topics:

PivotTables
PivotCharts
Slicers
What-if analysis
Goal Seek
Scenario analysis
Forecasting
KPI analysis

— Module 5: Power Query & Power Pivot

Topics:

Importing data
Transformation
Merge
Append
Data modeling
Relationships
DAX fundamentals

— Module 6: Dashboards

Topics:

Interactive dashboards
KPI cards
Charts
Slicers
Management reports

— Hands-On Labs

Labs:

Excel Data Cleaning Lab
Advanced Formula Lab
XLOOKUP & INDEX-MATCH Lab
Dynamic Array Lab
Conditional Formatting Lab
Data Validation Lab
PivotTable Analysis Lab
PivotChart Lab
Power Query ETL Lab
Power Pivot Data Model Lab
DAX Fundamentals Lab
What-If Analysis Lab
Excel Forecasting Lab
Interactive Dashboard Lab
Management Reporting Lab

— Assignments

Assignments:

Sales data cleaning
Employee analytics
Advanced lookup assignment
Sales KPI analysis
PivotTable report
Power Query transformation
Data-model assignment
Executive dashboard

— Mini Projects

Mini Projects:

Sales Performance Dashboard
Employee Attrition Analysis
Financial Expense Dashboard
Inventory Analytics Dashboard

— Capstone Enterprise Excel Business Intelligence Dashboard

— Real-Time Job Scenarios

Scenarios:

Monthly sales reporting
Employee performance analysis
Budget vs actual analysis
Inventory analysis
Customer segmentation
Management KPI reporting

— Troubleshooting Scenarios

Troubleshooting:

Incorrect lookup results
Duplicate records
Broken formulas
PivotTable refresh issue
Power Query transformation error
Incorrect totals
Date-format problems

— Industry Tools

Tools:

Microsoft Excel
Power Query
Power Pivot
DAX
CSV
SQL data sources
SharePoint/OneDrive

— Best Practices

Best Practices:

Structured tables
Consistent data types
Formula auditing
Avoid hard-coded values
Data validation
Reusable Power Query transformations
Dashboard usability

Mock Interviews

› Excel formulas
› XLOOKUP
› PivotTables
› Power Query
› Dashboards
› Business scenarios

— Certification Names

Month 02 of 06 7 Modules Month 2 · Basic to Advanced

SQL for Data Analytics

Learn SQL from fundamentals through advanced analytical querying, data exploration, business reporting and query optimization.

Course Content

— Prerequisites

— Module 1: Fundamentals

Topics:

Relational databases
Tables
Rows and columns
Primary keys
Foreign keys
Relationships
OLTP vs OLAP
SQL syntax
Data types

— Module 2: SQL Fundamentals

Topics:

SELECT
WHERE
ORDER BY
DISTINCT
LIMIT/TOP
CASE
NULL handling

— Module 3: Data Aggregation

Topics:

GROUP BY
HAVING
Aggregate functions
Business metrics

— Module 4: Joins

Topics:

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN
Self joins
Cross joins

— Module 5: Advanced SQL

Topics:

Subqueries
CTEs
Recursive CTE concepts
Views
Temporary tables
Window functions
Ranking
Running totals
Moving averages
Lead/lag
Percentile analysis

— Module 6: Analytics

Topics:

Customer analysis
Sales analysis
Cohort analysis
Retention
Revenue analysis
Conversion analysis

— Module 7: Optimization

Topics:

Indexes
Execution plans
Query optimization
Performance troubleshooting

— Hands-On Labs

Labs:

SQL Fundamentals Lab
Filtering & Sorting Lab
Aggregation Lab
SQL Join Lab
CASE Statement Lab
Subquery Lab
CTE Lab
Window Function Lab
Ranking Analysis Lab
Running Total Lab
Customer Cohort Lab
Sales Analytics Lab
Data Quality SQL Lab
Query Optimization Lab
Business KPI SQL Lab

— Assignments

Assignments:

Customer database analysis
Sales reporting queries
Advanced joins
CTE analysis
Window-function assignment
Customer retention analysis
Revenue analysis
Query optimization

— Mini Projects

Mini Projects:

E-Commerce Sales Analytics
Customer 360 SQL Analysis
Employee Analytics System
Financial Transaction Analytics

— Capstone Enterprise SQL Business Analytics Platform

— Real-Time Job Scenarios

Scenarios:

Find top-performing products
Identify inactive customers
Calculate monthly revenue
Analyze customer retention
Identify duplicate transactions
Calculate employee performance
Build management KPI queries

— Troubleshooting Scenarios

Troubleshooting:

Incorrect join results
Duplicate rows
NULL-related errors
Slow queries
Incorrect aggregations
Date calculation problems
Missing records

— Industry Tools

Tools:

SQL Server
PostgreSQL
MySQL
Oracle Database
Azure SQL
SSMS
DBeaver
pgAdmin

— Best Practices

Best Practices:

Clear SQL formatting
Correct joins
Avoid unnecessary subqueries
Use indexes appropriately
Validate business logic
Query performance analysis
Reusable CTEs/views

Mock Interviews

› SQL joins
› CTEs
› Window functions
› Business queries
› Optimization
› Real-world datasets

— Certification Names

Certifications:

Microsoft Azure Data Fundamentals
Microsoft Power BI Data Analyst certification
Oracle SQL certifications
PostgreSQL / SQL industry certifications

Month 03 of 06 6 Modules Month 3 · Basic to Advanced

Power BI — Basic to Advanced

Complete Power BI training covering data preparation, data modeling, DAX, visualization, dashboards, reporting, publishing and advanced analytics.

Course Content

— Prerequisites

— Module 1: Fundamentals

Topics:

Business Intelligence
Power BI ecosystem
Power BI Desktop
Power BI Service
Data sources
Data models
Reports vs dashboards
Data visualization principles

— Module 2: Power Query

Topics:

Data import
Data cleaning
Transformations
Merge
Append
Parameters
Data profiling

— Module 3: Data Modeling

Topics:

Tables
Relationships
Cardinality
Star schema
Dimension/fact tables
Date tables
Model optimization

— Module 4: DAX

Topics:

Calculated columns
Measures
CALCULATE
FILTER
SUMX
AVERAGEX
COUNTX
ALL
ALLEXCEPT
VALUES
Time intelligence
YTD / MTD / QTD
Previous year
Growth analysis

— Module 5: Visualization

Topics:

Bar/column charts
Line charts
Pie/donut
Maps
Matrix
Tables
KPI
Cards
Decomposition tree
Waterfall
Drill-down
Drill-through
Tooltips
Bookmarks

— Module 6: Advanced Power BI

Topics:

Row-Level Security
Power BI Service
Workspaces
Publishing
Scheduled refresh
Gateway
Performance Analyzer
Report optimization
Sharing and collaboration

— Hands-On Labs

Labs:

Power BI Desktop Lab
Data Import Lab
Power Query Transformation Lab
Data Modeling Lab
Star Schema Lab
Relationships Lab
DAX Measures Lab
CALCULATE Lab
Time Intelligence Lab
KPI Dashboard Lab
Interactive Visualization Lab
Drill-Through Lab
Bookmark & Navigation Lab
Row-Level Security Lab
Power BI Service Lab
Scheduled Refresh Lab
Performance Optimization Lab

— Assignments

Assignments:

Sales Power BI report
Data cleaning
Star-schema modeling
DAX KPI analysis
Time-intelligence report
Customer analytics
HR analytics
Executive dashboard

— Mini Projects

Mini Projects:

Sales & Revenue Dashboard
HR Analytics Dashboard
Customer 360 Dashboard
Financial Performance Dashboard
Supply Chain Dashboard

— Capstone Enterprise Power BI Business Intelligence Solution

— Real-Time Job Scenarios

Scenarios:

Build monthly management dashboard
Sales performance reporting
Customer segmentation
Employee attrition reporting
Budget vs actual analysis
Regional performance analysis
Executive KPI reporting

— Troubleshooting Scenarios

Troubleshooting:

Incorrect DAX result
Relationship problem
Circular dependency
Refresh failure
Gateway failure
Slow report
Incorrect filter context
RLS issue

— Industry Tools

Tools:

Power BI Desktop
Power BI Service
Power Query
DAX
SQL Server
Excel
SharePoint
Azure
Fabric concepts

— Best Practices

Best Practices:

Star schema
Proper data modeling
Measures over unnecessary calculated columns
Consistent KPI definitions
Optimized visuals
Row-level security
Incremental refresh where appropriate
Report performance monitoring

Mock Interviews

› Power Query
› Data modeling
› DAX
› Relationships
› Dashboards
› RLS
› Power BI troubleshooting

— Certification Names

Month 04 of 06 6 Modules Month 4 · Basic to Advanced

Python for Data Analytics

Use Python to perform data cleaning, exploratory analysis, statistical analysis, visualization and automation.

Course Content

— Prerequisites

— Module 1: Fundamentals

Topics:

Python environment
Variables
Data types
Conditions
Loops
Functions
Lists/dictionaries
Modules
Exceptions
File handling

— Module 2: NumPy

Topics:

Arrays
Indexing
Vectorization
Mathematical operations

— Module 3: Pandas

Topics:

Series
DataFrames
Data import
Data cleaning
Missing values
Duplicate handling
Filtering
GroupBy
Merge
Join
Concatenation
Pivot tables

— Module 4: Data Visualization

Topics:

Matplotlib
Seaborn
Charts
Distribution plots
Correlation visualization
Business dashboards

— Module 5: Analytics

Topics:

EDA
Correlation
Outlier detection
Trend analysis
Segmentation
Feature preparation

— Module 6: Automation

Topics:

Excel automation
CSV automation
API data extraction
Report generation
Automated analytics

— Hands-On Labs

Labs:

Python Analytics Environment Lab
NumPy Lab
Pandas DataFrame Lab
CSV Data Analysis Lab
Data Cleaning Lab
Missing Data Lab
Duplicate Data Lab
Pandas GroupBy Lab
Merge & Join Lab
EDA Lab
Matplotlib Visualization Lab
Seaborn Analytics Lab
Outlier Analysis Lab
API Data Analytics Lab
Automated Reporting Lab

— Assignments

Assignments:

Customer data cleaning
Sales EDA
Employee analytics
Product analysis
Data visualization
Outlier analysis
API analytics
Automated reporting

— Mini Projects

Mini Projects:

E-Commerce Data Analysis
Customer Segmentation Analysis
Employee Analytics
Financial Data Analysis

— Capstone Python-Based Business Analytics Platform

— Real-Time Job Scenarios

Scenarios:

Analyze customer transactions
Find sales trends
Detect outliers
Segment customers
Analyze employee performance
Automate recurring reports
Combine multiple business datasets

— Troubleshooting Scenarios

Troubleshooting:

Pandas memory issue
Missing values
Data-type mismatch
Incorrect merge
API failure
Visualization issue
Large dataset processing

— Industry Tools

Tools:

Python
Pandas
NumPy
Matplotlib
Seaborn
Jupyter Notebook
VS Code
APIs
Excel
SQL

— Best Practices

Best Practices:

Clean reusable code
Data validation
Modular functions
Efficient Pandas operations
Documentation
Version control
Reproducible analysis

Mock Interviews

› Python
› Pandas
› NumPy
› EDA
› Data cleaning
› Visualization
› Analytics scenarios

— Certification Names

Certifications:

Python Institute certifications
Microsoft Data Analyst certifications
IBM Data Analyst certifications
Google Data Analytics-related credentials

Month 05 of 06 5 Modules Month 5 · Basic to Advanced

Statistics & Business Analytics

Develop the statistical and business reasoning skills required to convert raw data into actionable business insights.

Course Content

— Prerequisites

Prerequisites:

Basic mathematics
Excel
SQL
Basic data analysis concepts

— Module 1: Fundamentals

Topics:

Descriptive statistics
Population vs sample
Mean
Median
Mode
Range
Variance
Standard deviation
Percentiles
Probability fundamentals
Distributions

— Module 2: Statistics

Topics:

Descriptive statistics
Probability
Normal distribution
Binomial distribution
Sampling
Central Limit Theorem
Confidence intervals
Hypothesis testing
p-values
Statistical significance

— Module 3: Advanced Analytics

Topics:

Correlation
Regression
Linear regression
Multiple regression
ANOVA concepts
Chi-square testing
A/B testing
Outlier analysis

— Module 4: Business Analytics

Topics:

KPI design
Revenue analytics
Customer analytics
Marketing analytics
Sales analytics
Financial analytics
Operations analytics
Supply-chain analytics
Customer segmentation
Churn analysis
Forecasting concepts

— Module 5: Decision Analytics

Topics:

Root-cause analysis
What-if analysis
Trend analysis
Scenario analysis
Business recommendations

— Hands-On Labs

Labs:

Descriptive Statistics Lab
Probability Lab
Distribution Analysis Lab
Sampling Lab
Confidence Interval Lab
Hypothesis Testing Lab
Correlation Lab
Regression Lab
A/B Testing Lab
Outlier Analysis Lab
Sales KPI Analysis Lab
Customer Churn Analysis Lab
Marketing Campaign Analysis Lab
Revenue Forecasting Lab
Business Decision Analysis Lab

— Assignments

Assignments:

Sales statistical analysis
Customer behavior analysis
A/B testing
Regression analysis
Churn analysis
Marketing campaign evaluation
KPI design
Business recommendation report

— Mini Projects

Mini Projects:

Customer Churn Analytics
Marketing Campaign Effectiveness
Sales Forecasting
Customer Segmentation
Business KPI Analysis

— Capstone Enterprise Business Analytics & Decision Support Project

— Real-Time Job Scenarios

Scenarios:

Determine why sales declined
Evaluate marketing campaign performance
Analyze customer churn
Compare two business strategies
Identify high-value customers
Forecast sales
Identify operational bottlenecks
Measure employee productivity

— Troubleshooting Scenarios

Troubleshooting:

Misleading averages
Outlier distortion
Correlation vs causation
Incorrect sampling
Statistical significance issue
Biased data
Incorrect KPI interpretation

— Industry Tools

Tools:

Excel
Power BI
Python
Pandas
NumPy
SciPy
Jupyter
SQL

— Best Practices

Best Practices:

Define business problem first
Validate data
Choose appropriate statistical methods
Avoid misleading visualizations
Distinguish correlation from causation
Communicate uncertainty
Connect insights to measurable business outcomes

Mock Interviews

› Statistics
› Probability
› Hypothesis testing
› Regression
› A/B testing
› KPIs
› Business cases

— Certification Names

Certifications:

Google Data Analytics Professional Certificate
Microsoft Power BI Data Analyst Associate
IBM Data Analyst Professional Certificate
Tableau / analytics industry certifications

Month 06 of 06 6 Modules Month 6 · Basic to Advanced

GenAI & Agentic AI for Data Analytics

Learn how GenAI and Agentic AI can enhance data analytics through natural-language analytics, automated reporting, insight generation, RAG, AI-assisted visualization and intelligent analytics workflows.

Course Content

— Prerequisites

Prerequisites:

Excel
SQL
Power BI
Basic Python
Basic statistics

— Module 1: Fundamentals

Topics:

AI vs ML vs GenAI
LLM fundamentals
Prompt engineering
Structured outputs
Function calling
Embeddings
Vector databases
RAG
AI agents
Agent workflows
AI-assisted analytics
Responsible AI
Data privacy and security

— Module 2: GenAI for Data Analytics

Topics:

Prompt engineering for analysts
AI-assisted SQL
AI-assisted Python
AI-assisted Excel
AI-assisted Power BI
Natural-language data analysis
Automated insight generation
Automated report writing
Data summarization
Executive briefing generation

— Module 3: RAG for Analytics

Topics:

Document ingestion
Chunking
Embeddings
Vector search
Metadata
Retrieval
Context generation
RAG evaluation
Enterprise knowledge assistants

— Module 4: Agentic AI

Topics:

AI agents
Agent planning
Tool calling
Function calling
Memory
Multi-step workflows
Human-in-the-loop
Agent orchestration

— Module 5: AI Analytics Applications

Topics:

Natural-language-to-SQL
AI Data Analyst
AI dashboard assistant
Automated KPI analysis
AI anomaly detection
AI report generation
Business-question answering
Automated root-cause analysis
AI-powered customer analysis

— Module 6: AI Security

Topics:

Prompt injection
Data leakage
Sensitive data protection
Access control
Hallucination
Output validation
Agent permissions
Tool security

— Hands-On Labs

Labs:

LLM Analytics Assistant Lab
Prompt Engineering Lab
AI SQL Assistant Lab
AI Python Analytics Lab
AI Excel Analysis Lab
AI Power BI Insight Lab
Natural-Language-to-SQL Lab
Automated KPI Analysis Lab
AI Report Generation Lab
Embeddings Lab
Vector Database Lab
RAG Analytics Assistant Lab
Business Knowledge RAG Lab
AI Anomaly Analysis Lab
Analytics Agent Tool-Calling Lab
Multi-Step Analytics Agent Lab
AI Root-Cause Analysis Lab
AI Security Testing Lab

— Assignments

Assignments:

Prompt engineering for analytics
AI-generated SQL validation
Natural-language analytics
Automated executive report
RAG business assistant
AI KPI interpretation
AI anomaly analysis
Analytics agent
AI security assessment

— Mini Projects

Mini Projects:

GenAI SQL Analytics Assistant
AI-Powered Business Insights Assistant
RAG-Based Business Knowledge Assistant
AI KPI Analysis & Reporting Assistant
Agentic Data Analytics Assistant

— Capstone Capstone 1 — GenAI Business Analytics Assistant

Project Flow:

User Question → LLM → SQL/Data → Analysis → Visualization/Insight → Business Explanation

Capabilities

› Natural-language questions
› SQL generation
› KPI analysis
› Trend analysis
› Automated summaries
› Business recommendations
› Data-source/context grounding

— Capstone Capstone 2 — Agentic AI Data Analyst

Project Flow:

User → AI Agent → Data Sources → SQL/Python → Analysis → Visualization → Insight → Report

The Agent Can

› Understand business questions
› Select appropriate analytical tools
› Generate SQL
› Perform Python analysis
› Analyze KPIs
› Detect anomalies
› Explain findings
› Generate management reports
› Request human approval where appropriate

— Real-Time Job Scenarios

Scenarios:

Management asks: "Why did sales fall this month?"
Executive requests a weekly KPI report
Analyst needs natural-language SQL
Business user wants customer segmentation
AI detects an unusual revenue pattern
Marketing team wants campaign analysis
Finance team requests variance analysis
Management asks for automated business insights
Analyst needs to query multiple business datasets
AI assistant must answer questions from company documentation

— Troubleshooting Scenarios

Troubleshooting:

AI generates incorrect SQL
Hallucinated business insight
Wrong KPI interpretation
RAG retrieves irrelevant information
Vector search mismatch
Prompt injection
Sensitive business data exposure
Agent selects incorrect tool
API rate limit
AI-generated report contains unsupported claims
Incorrect data visualization recommendation

— Industry Tools

Tools:

Python
SQL
Excel
Power BI
OpenAI/Azure OpenAI
Databricks AI capabilities
LangChain
LangGraph
LlamaIndex
Vector Databases
REST APIs
Jupyter

— Best Practices

Best Practices:

Validate AI-generated SQL
Ground insights in actual data
Never treat AI output as automatically correct
Protect confidential business data
Implement role-based access
Use RAG for enterprise knowledge
Maintain source/context traceability
Validate business calculations
Human review for important decisions
Restrict agent tool permissions
Monitor AI outputs and errors

Mock Interviews

› GenAI
› RAG
› AI SQL
› Analytics agents
› Prompt engineering
› AI dashboards
› AI security

— Certification Names

Certifications:

Microsoft Azure AI Engineer Associate
AWS Certified AI Practitioner
Google Cloud Generative AI credentials
Microsoft Power BI Data Analyst Associate
Google Data Analytics Professional Certificate

Tools & Technologies

Every tool and library listed here is installed, configured and used in a hands-on lab session.

Advanced Excel

Microsoft Excel

Analysis & Reporting

Power Query

Data Preparation & ETL

Power Pivot

Data Modeling

DAX

Calculations & Measures

SQL

SQL Server

Enterprise RDBMS

PostgreSQL

Open-Source RDBMS

MySQL

Relational Database

Oracle Database

Enterprise Database

Azure SQL

Managed Cloud Database

BI & Visualization

Power BI Desktop

Report Development

Power BI Service

Publishing & Sharing

Power Query

Transformations

DAX

Measures & Time Intelligence

Python

Analysis & Automation

Pandas

DataFrames & Cleaning

NumPy

Numerical Computing

Matplotlib

Charting

Seaborn

Statistical Visualization

Jupyter Notebook

Interactive Analysis

Statistics

SciPy

Statistical Testing

Pandas

Descriptive Statistics

Excel

What-If & Forecasting

Power BI

KPI & Trend Analysis

GenAI & Agentic AI

LLMs & AI APIs

Natural-Language Analytics

RAG & Embeddings

Grounded Business Answers

Vector Databases

Semantic Search

LangChain

LLM Application Framework

LangGraph

Agent Orchestration

Function Calling

Agent Tool Integration

27+

Tools & Libraries

36+

Hands-On Modules

Specialized Tracks

Months

Six months, six outcomes — and an analytics portfolio you can present to management.

A major capstone every month, two in Month 6, and one integrated final capstone — from an Excel BI dashboard and a SQL analytics platform to a Power BI solution, a Python analytics platform, a decision-support project, a GenAI analytics assistant and an agentic AI data analyst.

PROJECT // 01

Enterprise Excel Business Intelligence Dashboard

→Raw Data → Power Query → Data Model

→Analysis → KPIs

→Interactive Dashboard → Management Report

Month 1 Capstone Foundation

Outcome: Advanced Excel Data Analyst

Take raw business data through Power Query and a data model to KPIs, an interactive dashboard and a management report.

Sales Performance Dashboard

Employee Attrition Analysis

Financial Expense Dashboard

Inventory Analytics Dashboard

Stack Excel Power Query DAX

PROJECT // 02

Enterprise SQL Business Analytics Platform

→Database → Data Exploration

→SQL Transformations → KPI Queries

→Advanced Analytics → Business Insights

Month 2 Capstone Foundation

Outcome: SQL Data Analyst

Explore a business database, build the transformations and KPI queries, then push into cohort, retention and revenue analytics.

E-Commerce Sales Analytics

Customer 360 SQL Analysis

Employee Analytics System

Financial Transaction Analytics

Stack SQL CTEs Window Functions

PROJECT // 03

Enterprise Power BI Business Intelligence Solution

→Multiple Sources → Power Query → Data Model

→DAX → KPI

→Interactive Dashboard → Power BI Service

Month 3 Capstone Intermediate

Outcome: Power BI Analyst

Multiple sources modelled into a star schema, measured with DAX, and published to the Power BI Service as a governed executive dashboard.

Sales & Revenue Dashboard

HR Analytics Dashboard

Customer 360 Dashboard

Financial Performance Dashboard

Supply Chain Dashboard

Stack Power BI DAX RLS

PROJECT // 04

Python-Based Business Analytics Platform

→Raw Data → Cleaning → EDA

→Statistical Analysis → Visualization

→Business Insights → Automated Report

Month 4 Capstone Intermediate

Outcome: Python Data Analyst

Clean, explore and analyse business data in Python, then visualise the findings and generate the report automatically.

E-Commerce Data Analysis

Customer Segmentation Analysis

Employee Analytics

Financial Data Analysis

Stack Pandas NumPy Seaborn

PROJECT // 05

Enterprise Business Analytics & Decision Support Project

→Business Problem → Data → Statistical Analysis

→KPI → Visualization → Insight

→Recommendation → Business Decision

Month 5 Capstone Advanced

Outcome: Business / Data Analyst

Start from the business problem, not the data — analyse it statistically, express it as KPIs, and finish with a recommendation a manager can act on.

Customer Churn Analytics

Marketing Campaign Effectiveness

Sales Forecasting

Customer Segmentation

Business KPI Analysis

Stack Statistics Python Power BI

PROJECT // 06

GenAI Business Analytics Assistant

→User Question → LLM → SQL / Data

→Analysis → Visualization / Insight

→Business Explanation

Month 6 — Capstone 1 Advanced

A business question in, a grounded answer and explanation out

An LLM that turns a natural-language question into SQL, runs the analysis, visualises the result and explains it in business terms — grounded in the actual data source.

Natural-language questions

SQL generation

KPI analysis

Trend analysis

Automated summaries

Business recommendations

Data-source / context grounding

Stack LLM SQL RAG

PROJECT // 07

Agentic AI Data Analyst

→User → AI Agent → Data Sources

→SQL / Python → Analysis → Visualization

→Insight → Report

Month 6 — Capstone 2 Advanced

An agent that picks its own tools — and asks before it matters

The agent interprets the business question, chooses the right analytical tool, generates SQL or Python, analyses KPIs, detects anomalies, explains the findings and produces a management report.

Understand business questions

Select appropriate analytical tools

Generate SQL and perform Python analysis

Analyze KPIs and detect anomalies

Explain findings

Generate management reports

Request human approval where appropriate

Stack LangGraph Agents Python

PROJECT // 08

Enterprise Data Analytics & Agentic AI Platform

→Excel / CSV / SQL / APIs → Cleaning & Preparation

→SQL Analytics → Python Analytics → Statistics

→Power BI Model → Executive Dashboard

→GenAI Assistant → RAG Knowledge Base → Agentic AI Analyst

Integrated Final Capstone Flagship

Every track combined into one business analytics environment

Excel, CSV, SQL and API sources through cleaning and preparation, SQL and Python analytics, statistics and business analysis, a Power BI semantic model and executive dashboard, then a GenAI analytics assistant, a RAG knowledge base and an agentic AI data analyst on top.

Business requirements document and data dictionary

Data-cleaning framework and SQL analytics layer

Python EDA notebook and statistical analysis

KPI framework and Power BI semantic model

Executive and customer analytics dashboards

Business insights report

GenAI SQL assistant, RAG business assistant and agentic AI analyst

AI-generated management report, AI security assessment and final business presentation

Stack Power BI Python LangChain

All 8 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — this mentorship maps to the Power BI Data Analyst Associate (PL-300), Google Data Analytics Professional Certificate, IBM Data Analyst, Azure Data Fundamentals and Azure AI Engineer tracks, empowering individuals to stay ahead in the ever-evolving data analytics landscape.

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