Become an AI-Powered Data Analytics in Just 3 Months!
This 3-month structured training bridges the gap between theoretical knowledge and practical job-ready skills. Students and professionals will build real-world data analysis competencies using Microsoft Excel, SQL (with SQLite3), and Power BI. Every phase of the course is designed to make learners confident in handling, analyzing, and visualizing data — no prior experience required.
Job Support & Career Assurance
This program is designed not just to teach tools but to ensure employability. Learners who successfully complete all course requirements, projects with a minimum performance grade, satisfy eligibility criteria and career-readiness milestones are eligible for our Job Support Track, which includes:
- 100% Job Assistance & Placement Support through iPEC’s AI-Hire
- Career Readiness Bootcamp covering resume optimization, mock interviews, and LinkedIn profile building.
- Guaranteed Internship for eligible
Early Bird Advantage
Enroll Now and unlock an exclusive bonus package!
- Extra Project: AI-Powered Business Dashboard Challenge
- Bonus Module: Generative AI in Data Analytics
- Career Accelerator: Resume + Interview Prep Toolkit
- Special Recognition: Early Achiever Certificate Badge
Limited seats. Early access = Extra learning + Exclusive recognition
Who Should Join?
- College students from any stream aiming to break into analytics
- Early-career professionals seeking practical tools
- Non-programmers who want to analyze and visualize data
- Entrepreneurs and business learners needing reporting insights
- Candidates who want to gain skills and start as Freelancer
- Ladies who want to work from Home and ready to acquire skills
Key Features
- Industry-aligned, curated curriculum
- Hands-on learning with datasets and case studies
- Access to weekly quizzes, live problem solving, and a final capstone
What You Will Learn
Excel for Data Analysis
Understanding all features of workbook
- Basic to Advanced Formulas + shortcuts
- Data Cleaning, Lookup Functions, Conditional Logic
- PivotTables, Charts, Named Ranges
- Dynamic Dashboards for Summary Reports
- Excel with AI
SQL using SQLite3
Relational Database Planning & Design
- Database Creation & Insertion (ACID Properties)
- SELECT, JOIN, GROUP BY, Subqueries
- Aggregation, Filtering, Report Queries
- SQL with AI
Power BI
- Power Query & Power Pivot
- DAX Calculations and Data Modeling
- Interactive Dashboard Creation
- Power BI with AI
Projects You'll Complete
3 Mini Projects
- Using Excel
- Using Power BI
- Using SQL
One Capstone Project
Course Outcomes
- Analyze, interpret, and report using structured and semi-structured datasets
- Create dashboards that convey insights clearly and visually
- Develop SQL queries for extraction and reporting
- Use Power BI for building executive-level reports
- Build a portfolio of industry-grade mini projects
Daily Self-Study Expectations
| Student Type | Recommended Daily Self-Study | Weekly Total |
|---|---|---|
| Working Professionals | 1 - 1.5 hours | 6 - 9 hours |
| Students / Job Seekers | 2 - 3 hours | 12 - 18 hours |
| Career Switchers | 2 - 4 hours | 12 - 24 hours |
ASSESSMENT SCHEDULE
| Week | Assessment Type | Wt | Duration |
|---|---|---|---|
| Week 2 | Excel Quiz 1 | 2% | 30 mins |
| Week 4 | Excel Quiz 2 | 2% | 30 mins |
| Week 6 | Excel Dashboard Project | 15% | Weekend |
| Week 8 | SQL Quiz 1 | 2% | 30 mins |
| Week 10 | SQL Database Project | 15% | Weekend |
| Week 12 | Power BI Quiz | 2% | 30 mins |
| Week 12 | Power BI Dashboard Project | 10% | Weekend |
| Week 13 | Final Capstone Project | 20% | 1 week |
| Ongoing | Weekly Assignments | 20% | - |
| Ongoing | Participation & Attendance | 12% | - |
Hardware Requirements
- Laptop/Desktop (Windows 10+ recommended)
- Minimum 8GB RAM (16GB preferred)
- 50GB free disk space
- Stable internet (minimum 5 Mbps)
- Webcam (optional but recommended)
- Headphones with microphone
Software Installation
- Microsoft Excel (365/2021/2019)
- SQLite3 + DB Browser
- Power BI Desktop
- Zoom/Google Meet (for classes)
- VS Code (optional)
Accounts Setup
- Microsoft account
- LMS/Google Class login
- Discussion forum access
- Cloud storage (Google Drive/OneDrive)
AI-Powered Data Analytics: Excel, SQL & Power BI Essentials Detailed Module-Wise Syllabus 3 months
MODULE 1: Excel for Data Analysis (6 Weeks)
Week 1: Excel Fundamentals & Workbook Mastery
Day 1-2: Workbook Features & Interface
• Understanding Excel interface: Ribbon, Quick Access Toolbar, Formula Bar
• Workbook vs Worksheet management
• Working with multiple sheets: Navigation, grouping, copying
• Page layout and print settings
• Protecting worksheets and workbooks
• Sharing and collaboration features
• Version history and AutoSave
Day 3-4: Data Entry & Basic Formulas
• Data types: Text, Numbers, Dates, Time
• Cell referencing: Relative, Absolute ($), Mixed
• Basic arithmetic operators and order of operations
• Essential formulas: SUM, AVERAGE, COUNT, MIN, MAX
• SUMIF, COUNTIF, AVERAGEIF
• Text functions: CONCATENATE, LEFT, RIGHT, MID, LEN, TRIM
• Date functions: TODAY, NOW, DATE, YEAR, MONTH, DAY
Day 5: Keyboard Shortcuts & Productivity
• Navigation shortcuts (Ctrl+Arrow keys, Ctrl+Home/End)
• Selection shortcuts (Shift, Ctrl+Shift combinations)
• Formatting shortcuts (Ctrl+1, Ctrl+B/I/U)
• Formula shortcuts (F4 for absolute reference, F2 for edit)
• Data manipulation shortcuts (Ctrl+D/R, Alt+E+S)
• Creating custom shortcuts
• Speed techniques for data entry
Practice Assignment:
• Create a monthly expense tracker with basic formulas
• Implement multiple sheets for different expense categories
Week 2: Data Cleaning & Transformation
Day 1-2: Data Cleaning Fundamentals
• Identifying data quality issues: duplicates, blanks, inconsistencies
• Remove Duplicates feature
• Find & Replace with wildcards
• Text to Columns (delimiter and fixed-width)
• Flash Fill for pattern recognition
• TRIM, CLEAN, and SUBSTITUTE functions
• Handling errors: IFERROR, IFNA
• Data validation rules and dropdown lists
Day 3-4: Advanced Data Cleaning
• Conditional formatting for data quality checks
• Custom number formats and text formatting
• Working with inconsistent date formats
• Removing leading/trailing spaces and special characters
• Standardizing text case (UPPER, LOWER, PROPER)
• Power Query introduction for data transformation
• Get & Transform Data (basic operations)
Day 5: Data Organization Best Practices
• Structured table format vs range
• Converting ranges to Tables (Ctrl+T)
• Table design and formatting
• Structured references in formulas
• Sorting and filtering techniques
• Advanced filter with criteria range
• Removing blank rows efficiently
Practice Assignment:
• Clean a messy dataset with 1000+ rows
• Standardize customer data with inconsistent formats
Week 3: Lookup Functions & Conditional Logic
Day 1-2: Lookup Functions
• VLOOKUP: Syntax, exact and approximate match
• VLOOKUP limitations and best practices
• HLOOKUP for horizontal lookups
• INDEX function: Single and multi-dimensional arrays
• MATCH function: Finding positions
• INDEX-MATCH combination (superior to VLOOKUP)
• Handling errors in lookup formulas
Day 3-4: XLOOKUP & Advanced Lookups
• XLOOKUP syntax and advantages (Excel 365/2021)
• Lookup with multiple criteria
• Two-way lookups using INDEX-MATCH
• Dynamic array formulas: FILTER, SORT, SORTBY, UNIQUE
• Approximate match scenarios
• Looking up last occurrence
• Nested lookups
Day 5: Conditional Logic
• IF statements: Simple and nested
• AND, OR, NOT logical functions
• IFS function for multiple conditions (Excel 365/2021)
• SWITCH function
• Combining IF with lookup functions
• SUMIFS, COUNTIFS, AVERAGEIFS with multiple criteria
• Array formulas with conditional logic
Practice Assignment:
• Build a dynamic pricing calculator with multiple lookup tables
• Create an inventory management system using advanced lookups
Week 4: PivotTables & Data Analysis
Day 1-2: PivotTable Fundamentals
• Creating PivotTables from tables and ranges
• PivotTable Field List: Rows, Columns, Values, Filters
• Understanding PivotTable layout options
• Summarizing data: Sum, Count, Average, etc.
• Grouping data: Dates, numbers, text
• Sorting and filtering in PivotTables
• Refreshing PivotTable data
• PivotTable styles and formatting
Day 3-4: Advanced PivotTable Techniques
• Calculated Fields and Calculated Items
• Show Values As: % of Total, Difference, Running Total
• Slicers for interactive filtering
• Timelines for date filtering
• Multiple PivotTables from one source
• PivotTable from multiple tables (Data Model)
• Drill-down capabilities
• Consolidating data from multiple sheets
Day 5: PivotCharts & Visualization
• Creating PivotCharts from PivotTables
• Chart types suitable for different data
• Formatting and customizing PivotCharts
• Interactive PivotChart dashboards
• Combining slicers with PivotCharts
• PivotChart best practices
Practice Assignment:
• Analyze sales data across regions, products, and time periods
• Create an interactive report with slicers and PivotCharts
Week 5: Charts, Named Ranges & Advanced Features
Day 1-2: Chart Creation & Customization
• Chart types: Column, Bar, Line, Pie, Scatter, Area
• Selecting appropriate chart types for data
• Chart elements: Titles, Axes, Legends, Data Labels
• Formatting charts professionally
• Combination charts (dual-axis)
• Dynamic charts using named ranges
• Sparklines for inline visualization
• Conditional formatting with data bars, color scales, icon sets
Day 3: Named Ranges & Advanced Functions
• Creating and managing named ranges
• Using named ranges in formulas
• Dynamic named ranges with OFFSET
• Named ranges for chart data
• Table names and structured references
• INDIRECT function for dynamic references
• Advanced functions: AGGREGATE, SUBTOTAL
Day 4-5: Advanced Excel Features
• What-If Analysis: Goal Seek, Scenario Manager
• Data Tables for sensitivity analysis
• Working with large datasets: Freezing panes, split view
• Advanced filtering and sorting
• Custom Views for different perspectives
• Macros introduction (recording simple macros)
• Form controls for interactivity
Practice Assignment:
• Create a financial model with scenario analysis
• Build charts with dynamic named ranges
Week 6: Dynamic Dashboards & Excel with AI
Day 1-2: Dashboard Design Principles
• Dashboard layout and design best practices
• Choosing the right visualizations
• Color theory and professional formatting
• Creating a dashboard template
• Linking multiple charts and tables
• Using GETPIVOTDATA for dashboard cells
• Dashboard navigation and user experience
Day 3: Building Interactive Dashboards
• Combining PivotTables, charts, and slicers
• Form controls: Dropdown lists, option buttons, checkboxes
• Dynamic titles and labels using formulas
• Creating KPI indicators with conditional formatting
• Summary cards and metric displays
• Dashboard filters and parameter controls
• Mobile-friendly dashboard considerations
Day 4: Excel with AI Features
• Microsoft 365 Copilot in Excel (if available)
• Analyze Data feature: Insights and patterns
• Natural language queries in Excel
• AI-powered formula suggestions
• Data types: Stocks, Geography, Organizations
• Using Python in Excel (Excel 365)
• Forecasting with built-in AI tools
• Power Query AI features
Day 5: Dashboard Finalization & Best Practices
• Performance optimization for large dashboards
• Error handling and data validation
• Testing dashboard functionality
• Documentation and user instructions
• Distribution and sharing options
• Maintaining and updating dashboards
• Dashboard portfolio review
Final Project:
• Build a comprehensive business dashboard with:
o Multiple data sources
o Interactive filters and controls
o 5-7 key visualizations
o KPI summary section
o Professional formatting and design
MODULE 2: SQL using SQLite3 (4 Weeks)
Week 1: Database Fundamentals & Design
Day 1-2: Introduction to Databases
• What is a database? DBMS vs RDBMS
• Relational database concepts: Tables, rows, columns
• Primary keys and foreign keys
• Entity-Relationship (ER) diagrams
• Normalization: 1NF, 2NF, 3NF
• Understanding data redundancy and integrity
• Introduction to SQLite3: Installation and setup
• SQLite Browser/DB Browser installation
Day 3-4: Database Planning & Design
• Requirements gathering for database design
• Identifying entities and attributes
• Defining relationships: One-to-One, One-to-Many, Many-to-Many
• Creating ER diagrams for real-world scenarios
• Junction tables for Many-to-Many relationships
• Data types in SQLite: INTEGER, TEXT, REAL, BLOB, NULL
• Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK
• Best practices for table and column naming
Day 5: ACID Properties & Database Creation
• ACID Properties explained:
o Atomicity: All-or-nothing transactions
o Consistency: Data integrity rules
o Isolation: Concurrent transaction handling
o Durability: Permanent data storage
• Transactions: BEGIN, COMMIT, ROLLBACK
• CREATE DATABASE concepts (file-based in SQLite)
• CREATE TABLE syntax and examples
• ALTER TABLE: Adding/modifying columns
• DROP TABLE and database cleanup
Practice Assignment:
• Design a database schema for an e-commerce system
• Create ER diagram with at least 5 entities
• Implement the database in SQLite3
Week 2: Data Manipulation & Basic Queries
Day 1-2: Inserting and Updating Data
• INSERT INTO syntax: Single and multiple rows
• Inserting data into specific columns
• DEFAULT values and auto-increment
• UPDATE statement: Modifying existing records
• DELETE statement: Removing records safely
• TRUNCATE vs DELETE considerations
• Data import from CSV files
• Best practices for data insertion
Day 3-4: SELECT Statement Fundamentals
• Basic SELECT syntax: SELECT * FROM table
• Selecting specific columns
• Column aliases using AS
• DISTINCT keyword for unique values
• WHERE clause for filtering:
o Comparison operators (=, <>, <, >, <=, >=)
o Logical operators (AND, OR, NOT)
o IN operator for multiple values
o BETWEEN for range queries
o LIKE and wildcards (%, _) for pattern matching
o IS NULL and IS NOT NULL
• ORDER BY: ASC and DESC sorting
• LIMIT for result pagination
Day 5: Working with Data Types
• String functions: UPPER, LOWER, SUBSTR, LENGTH, TRIM
• Numeric functions: ROUND, ABS, RANDOM
• Date and time functions: DATE, TIME, DATETIME, STRFTIME
• Type casting and conversion
• CASE statements for conditional logic
• COALESCE for handling NULL values
Practice Assignment:
• Populate database with sample data (100+ records)
• Write 20 SELECT queries with various filtering conditions
• Create reports using date functions and string manipulation
Week 3: JOINs & Advanced Queries
Day 1-2: Understanding JOINs
• Why JOINs are necessary
• INNER JOIN: Matching records from both tables
• LEFT JOIN (LEFT OUTER JOIN): All from left + matching from right
• RIGHT JOIN concepts (simulated in SQLite)
• FULL OUTER JOIN concepts (simulated in SQLite)
• CROSS JOIN: Cartesian product
• Self-joins for hierarchical data
• Multiple table joins
• JOIN best practices and performance
Day 3-4: GROUP BY & Aggregation
• Aggregate functions:
o COUNT(*), COUNT(column), COUNT(DISTINCT)
o SUM, AVG, MIN, MAX
• GROUP BY clause: Grouping data for aggregation
• HAVING clause: Filtering grouped results
• Difference between WHERE and HAVING
• GROUP BY with multiple columns
• Grouping with JOINs
• Common aggregation patterns
• NULL handling in aggregations
Day 5: Subqueries
• What are subqueries (nested queries)?
• Subqueries in WHERE clause
• Subqueries in SELECT clause
• Subqueries in FROM clause (derived tables)
• Correlated vs non-correlated subqueries
• EXISTS and NOT EXISTS operators
• IN with subqueries
• Subquery performance considerations
• Common Table Expressions (CTE) using WITH
Practice Assignment:
• Write complex queries joining 3+ tables
• Create aggregated reports with GROUP BY and HAVING
• Implement subqueries for advanced filtering
Week 4: Advanced SQL & AI Integration
Day 1-2: Advanced Query Techniques
• UNION, UNION ALL for combining results
• INTERSECT and EXCEPT for set operations
• Window functions (if supported): ROW_NUMBER, RANK, DENSE_RANK
• Advanced date calculations
• Text manipulation and concatenation
• Working with JSON data (SQLite JSON functions)
• Creating views for reusable queries
• Indexes for performance optimization
Day 3: Complex Reporting Queries
• Sales analysis queries: Revenue by period, top products
• Customer analytics: Purchase frequency, lifetime value
• Inventory reports: Stock levels, reorder points
• Financial reports: Profit margins, expense breakdown
• Cohort analysis queries
• Year-over-year comparisons
• Running totals and moving averages
• Pivot-style reports using CASE statements
Day 4: SQL with AI
• Natural language to SQL conversion tools
• Using ChatGPT/Claude for SQL query generation
• AI-assisted query optimization
• Debugging SQL with AI assistance
• Generating test data with AI
• AI tools for database design suggestions
• SQL code documentation with AI
• Best practices for prompting AI for SQL help
Day 5: SQL Project & Best Practices
• Database normalization review
• Query optimization techniques
• Indexing strategies
• Security considerations: SQL injection prevention
• Backup and recovery strategies
• Database documentation
• Code formatting and style guidelines
• Version control for database schemas
Final Project:
• Build a complete database system with:
o Minimum 5 normalized tables
o 20+ complex queries demonstrating all concepts
o Business reports and analytics queries
o Documentation and ER diagram
o Sample data and test cases
MODULE 3: Power BI (2 Weeks)
Week 1: Power Query, Power Pivot & DAX
Day 1: Introduction & Power Query
• Power BI Desktop installation and interface
• Power BI ecosystem: Desktop, Service, Mobile
• Connecting to data sources:
o Excel files, CSV, databases
o Web data and APIs
o Folders with multiple files
• Power Query Editor interface
• Basic transformations:
o Removing columns and rows
o Changing data types
o Filtering and sorting
o Split columns and merge columns
• Applied Steps tracking
Day 2: Advanced Power Query
• Append queries (UNION equivalent)
• Merge queries (JOIN equivalent)
• Pivot and unpivot columns
• Grouping and aggregation in Power Query
• Conditional columns and custom columns
• Text, number, and date transformations
• Handling errors and nulls
• Parameters for dynamic queries
• Query organization and documentation
• Performance optimization with Query Folding
Day 3: Data Modeling & Power Pivot
• Importing data into Power BI data model
• Star schema vs snowflake schema
• Fact tables vs dimension tables
• Creating relationships:
o One-to-Many, Many-to-One
o Relationship cardinality and cross-filter direction
o Active vs inactive relationships
• Managing relationships in Model view
• Role-playing dimensions
• Data model best practices
• Calculated columns vs measures
Day 4: DAX Fundamentals
• Introduction to DAX (Data Analysis Expressions)
• DAX syntax basics
• Calculated columns:
o Simple calculations
o Using related tables with RELATED function
o When to use calculated columns
• Basic measures:
o SUM, AVERAGE, COUNT, MIN, MAX
o DISTINCTCOUNT, COUNTROWS
• Understanding row context vs filter context
• Implicit vs explicit measures
• Formatting measures
Day 5: Intermediate DAX
• CALCULATE function: The most important DAX function
• Filter functions:
o FILTER, ALL, ALLSELECTED
o REMOVEFILTERS, KEEPFILTERS
o VALUES, DISTINCT
• Time intelligence functions:
o TOTALYTD, TOTALQTD, TOTALMTD
o DATEADD, SAMEPERIODLASTYEAR
o Date table creation and marking
• Logical functions: IF, SWITCH, AND, OR
• Iterator functions: SUMX, AVERAGEX, COUNTX
• DIVIDE for safe division
Practice Assignment:
• Import and transform data from multiple sources
• Create a star schema data model
• Write 10+ DAX measures for business KPIs
Week 2: Visualizations, Dashboards & AI Features
Day 1: Visualization Fundamentals
• Power BI visualization types:
o Bar/Column charts, Line charts, Area charts
o Pie/Donut charts
o Tables and matrices
o Cards and multi-row cards
o Scatter and bubble charts
o Maps and filled maps
o Gauges and KPIs
• Choosing the right visualization
• Formatting visuals professionally
• Custom visuals from AppSource
• Visual interactions and cross-filtering
• Drill-down and drill-through features
• Tooltips customization
• Bookmarks for navigation
Day 2: Interactive Dashboard Creation
• Dashboard design principles for Power BI
• Page layout and canvas settings
• Slicers for filtering:
o List, dropdown, date slicers
o Numeric range slicers
o Slicer synchronization across pages
• Buttons for navigation and actions
• Report page navigation structure
• Using themes for consistent branding
• Mobile layout optimization
• Performance optimization tips
• Accessibility features
Day 3: Advanced Dashboard Features
• Advanced DAX for dashboards:
o Dynamic titles using measures
o Conditional formatting with rules
o Top N analysis
o What-if parameters
• Q&A visual for natural language queries
• Key Influencers visual
• Decomposition Tree visual
• Smart Narrative for automated insights
• Custom tooltips with report pages
• Field parameters for dynamic visuals
• Row-level security (RLS) basics
Day 4: Power BI with AI
• Power BI AI features overview
• Quick Insights for automatic pattern detection
• Copilot in Power BI (if available):
o Natural language report creation
o DAX formula assistance
o Summary generation
• AI visuals:
o Key Influencers
o Decomposition Tree
o Q&A visual setup and optimization
o Anomaly detection
• Python and R integration for advanced analytics
• Azure Machine Learning integration
• Automated Machine Learning (AutoML)
• Forecasting with AI
Day 5: Publishing & Collaboration
• Power BI Service overview
• Publishing reports to Power BI Service
• Workspaces and app workspaces
• Sharing reports and dashboards
• Creating and sharing apps
• Scheduled data refresh setup
• Gateway configuration basics
• Embedding reports in other applications
• Power BI Mobile app features
• Collaboration and commenting
• Version control and deployment pipelines
• Best practices for enterprise deployment
Final Project:
• Build a complete Power BI solution with:
o Multiple data sources connected via Power Query
o Well-designed star schema data model
o 15+ DAX measures with time intelligence
o Multi-page interactive dashboard
o AI-powered insights
o Professional design and formatting
o Publish to Power BI Service
-
Prgoramme Director : Fathima Afroz
BE, M.Tech ,Research @ NITK Surathkal - 22 Years of Experience in Academic & Industry
- Areas of Interest : Data Science , ML , NLP , Speech Recognition
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- No Coding Background Needed
- Exclusive Access to Resources
Extra Achievements
- Problem Solving Skills
- Optimization
- Algorithms
- File handling
- Interpretation of results
- Explainable AI
- Low code programming
Pre- requisite
- Any Graduation /Any UG Students
- No Coding Skills required
- No Age Limit
- Technical / Non-Technical
Open to any graduates and UG students from all disciplines.
No coding skills required; designed for beginners and non-programmers.
Suitable for all ages with no restrictions on learning or participation.
Technical / Non-Technical
Why IPEC
At iPEC, we pride ourselves on offering an offline learning experience that goes beyond the ordinary. Rooted in excellence, our methodology is tailored to provide students in India with the best possible education, enriched by the expertise of our experienced trainers.
Why Choose Us
Why iPEC for Offline Learning in India?
AI & ML Interactive Offline/ Online programs are intensive application oriented and based on real-world scenario. AI & ML foundation, master and professional, all are skill oriented, practical training program required for building various applications. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. These courses are designed for both for freshers and experienced professionals from variety of backgrounds. No prior knowledge of statistics or modelling is assumed.
iPEC has a history of producing successful graduates who have excelled in their careers.
Our offline learning approach focuses not only on academic excellence but also on the holistic development of each student, nurturing well-rounded professionals.
Stay ahead in your chosen field with our industry-relevant curriculum, constantly updated to align with current trends and demands.
Frequently Asked Questions
1. Is this a Job Assistance Program?
Yes. Learners who complete all modules, projects, and career-readiness milestones under the Job Assistance Track receive placement assurance through iPEC’s Hire Talent Placement Support Portal.
2. What kind of job roles can I get after this course?
You’ll be ready for following roles such as
IT roles | Non-IT / Cross-Sector roles |
Data Analyst | Business Analyst (in sectors like retail, pharma, manufacturing, supply chain) |
Business Intelligence (BI) Analyst / BI Developer | Market Research Analyst (using data + dashboards) |
Reporting Analyst / MIS Analyst | Financial |
SQL/Database Analyst | Operations Analyst |
Power BI / Dashboard Specialist | Sales Analytics |
Analytics Engineer (entry level) | Customer Insights Analyst (in non-tech firms) |
Data Visualization Specialist | Management Information Systems (MIS) Executive / Analyst |
Data Analytics Consultant | Performance Analyst (in manufacturing, logistics) |
AI-Assisted Insights Analyst | Reporting & Insights Associate (in HR, marketing, supply chain) |
ETL / Data Pipeline Analyst (with SQL + analytics) | Strategy & Insights Executive |
Junior Data Scientist (if you pick up enough AI/ML skills) |
Revenue Operations Analyst |
Analytics Product or Insight Specialist | / Risk Reporting Analyst |
3. Will I get placement support or interview preparation?
Yes. The program includes a Career Accelerator Bootcamp, resume and interview preparation sessions, and mock interview practice with mentors.
4. Do I need a technical background or coding skills?
Not at all. The program is designed for beginners — including non-coders and professionals from any stream.
5. How are the classes conducted?
You can choose between online, offline, or blended learning formats with live, interactive sessions led by faculty experts.
6. Will I work on real-world projects?
Absolutely! You’ll complete three mini-projects and one capstone project based on real-world datasets from business, finance, and marketing domains.
7. Is the AI module included in the course?
Yes. The program features an exclusive “AI for Data Analysis” module, showing how modern analytics tools use AI for insights and automation.
8. What is the Global Certification mentioned?
In addition to the iPEC Certificate, you can optionally appear for a Global AI & Data Analytics Certificate, recognized by international partners.
9. How soon can I expect to get a job after completion?
Many learners start getting interview calls within 4–6 weeks of completing the program, depending on their performance and career goals.
10. Is there any financial assistance or EMI option?
Yes. The course fee offers flexible payment options, and early-bird enrollment unlocks exclusive project and career bonuses.
11. How much salary can be expected if get job?
Below are indicative Salary Ranges /earnings based on current data. Use them as guides, not guarantees.
In India
Role | Entry / 0–2 yrs | Mid (3-5 yrs) | Senior (5+ yrs) |
Data Analyst | ~ ₹ 4-6 LPA (freshers) | ~ ₹ 8-12 LPA (Experience) | ~ ₹ 15-25 LPA (Experience) |
Business/Insights/BI Analyst | ~ ₹ 6-8 LPA | ~ ₹ 8-15 LPA | ~ ₹ 12-25 LPA |
Reporting / Dashboard / MIS Analyst (non-IT sector) | ~ ₹3-7 LPA depending on industry | ~ ₹7-12 LPA | ~ ₹12-20 LPA+ |
Abroad / Global benchmarks
In the UK: Data Analyst average ~ £36,291/year (~ entry) up to maybe £50,000+ for senior. Another UK breakdown: Entry ~£28k-35k, Mid ~£40k-55k+, Senior ~£60k-70k+.
In the US: Data Analyst average ~$84,655/year in one dataset.
Global table: Data Analyst ~ ₹6.97 lakh/year equivalent in India; USA ~$80k etc. (Source: Indeed, Glassdoor,
12. What are the key factors which influence the salary?
Since you’re coming from this course, your salary will depend a lot on:
- How many practical projects + portfolio you have (real-world case studies)
- How strong you are in the tools (Excel advanced, Power BI, SQL) and your AI insight module
- Whether you can speak business: communicate analytics to non-tech stakeholders
- Industry/domain you join (IT/tech and startup often pay higher than non-tech)
- Location (Tier-1 cities like Bengaluru, Delhi-NCR, Mumbai pay more)
- Experience (every year counts)
- Additional skills: g., Python/R, cloud, machine learning can boost salary significantly
13. Is there any Career Progression Path related to this course?
1. Data-Focused Track:
Data Analyst → BI Developer → Analytics Engineer → Data Scientist / AI Specialist
2. Business Track:
MIS Executive → Business Analyst → Strategy Analyst → Business Intelligence Manager
3. Creative / Freelance Track:
Power BI Freelancer → Dashboard Designer → Data Consultant → Trainer / Independent Analyst
14. In which sectors these roles exist?
This Programme learners aren’t restricted to IT; these roles exist in nearly every industry:
- Banking & Finance
- E-commerce & Retail
- Manufacturing & Supply Chain
- Healthcare & Pharma
- EdTech & E-Learning
- Telecom & Networking
- Consulting & Market Research
- Public Sector / Government Data Offices
- Startups & MSMEs
- NGOs & Nonprofits (Impact Reporting)
15. Is there any other roles apart from Analyst where I can work?
Below is a comprehensive, categorized list of non-analyst job roles (IT & Non-IT) that learners of AI- Powered Data Analytics course can pursue — along with a short description and indicative salary range (India + Abroad).
- Data & Technology Roles (Beyond Analyst)
Role | What They Do | Salary (India) | Salary (Abroad) |
Data Engineer (Entry-Level) | Build/manage SQL databases, clean and prepare data for analysis. | ₹6 – 10 LPA | $70k – 110k |
BI Developer / Dashboard Developer | Develop dashboards & reports in Power BI/Tableau for organizations. | ₹5 – 9 LPA | $65k – 100k |
Data Visualization Specialist | Design interactive data stories, infographics, and executive dashboards. | ₹5 – 8 LPA | $60k – 95k |
ETL / Data Operations Executive | Manage data extraction, transformation, loading, and quality checks. | ₹4 – 7 LPA | $55k – 85k |
AI Tools Assistant / Prompt Engineer (Entry) | Use AI-powered tools for report automation, data cleaning, or insight generation. | ₹4 – 8 LPA | $65k – 100k |
- Business, Finance & Operations Roles
Role | Core Work | Salary (India) | Salary (Abroad) |
Business Operations Executive | Use data dashboards to track KPIs, sales, and productivity metrics. | ₹4 – 7 LPA | $55k – 90k |
Financial Planning & Analysis (FP&A) Associate | Support budgeting, forecasting, and financial modeling using Excel & BI tools. | ₹6 – 10 LPA | $70k – 120k |
Supply Chain Coordinator / Analyst | Optimize inventory and logistics using Excel & Power BI. | ₹5 – 9 LPA | $65k – 105k |
Project Management / PMO Executive | Track project metrics, risks, and progress using dashboards. | ₹5 – 8 LPA | $65k – 100k |
Operations Manager (Data- Driven) | Lead process optimization using data insights. | ₹8 – 15 LPA | $90k – 150k |
- Marketing, Sales & Customer Insights Roles
Role | Core Work | Salary (India) | Salary (Abroad) |
Digital Marketing Analyst / Executive | Analyze campaign data, optimize ROI using Power BI/Excel. | ₹4 – 8 LPA | $60k – 100k |
CRM / Customer Success Specialist | Track and improve customer retention using dashboards and SQL. | ₹4 – 7 LPA | $55k – 95k |
Sales Operations Executive | Support pipeline tracking, forecasting, and sales metrics reporting. | ₹4 – 7 LPA | $55k – 90k |
Market Research Executive | Use AI and Excel to analyze consumer behavior, pricing, and product data. | ₹3.5 – 7 LPA | $55k – 85k |
- Strategic & Decision-Support Roles
Role | What They Do | Salary (India) | Salary (Abroad) |
Data-Driven Decision Support Officer | Assist management in making evidence- based decisions. | ₹6 – 10 LPA | $70k – 120k |
Strategy Associate / Insights Coordinator | Combine analytics and presentation to influence strategy. | ₹7 – 12 LPA | $80k – 130k |
Business Transformation Executive | Use analytics and AI to drive operational improvements. | ₹8 – 15 LPA | $90k – 150k |
- Creative & Emerging AI-Enhanced Roles
Role | Description | Salary (India) | Salary (Abroad) |
AI-Powered Excel / Power BI Consultant (Freelance) | Offer dashboard automation and data insight services to clients. | ₹5 – 12 LPA (equiv.) | $60k – 120k (equiv.) |
AI Data Assistant / Automation Associate | Implement AI tools like Copilot or ChatGPT for data cleaning & report generation. | ₹4 – 9 LPA | $65k – 110k |
No-Code App Builder / Data Automation Expert | Build automation workflows using Power Automate, Excel Macros, AI. | ₹6 – 10 LPA | $70k – 120k |
Freelance Data Consultant / Trainer | Conduct projects or training in Excel, Power BI, SQL. | ₹ variable | $ variable |
Data Storytelling & Communication Specialist | Convert complex data into compelling business narratives. | ₹6 – 12 LPA | $70k – 130k |
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Company Info.
Address: #234,1st Floor, SK Complex, Utharahalli Main Rd, 5th Stage, RR Nagar, Channasandra Bangalore – 560 098 Karnataka, India
Contact no.: +91 6366373030
Email: contact@ipecsolutions.com
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