Course Duration
01
4 Months
Structured learning path
Data Analytics + Generative AI
Excel • SQL • Power BI • Tableau • Python • Business Analytics • GenAI
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Curriculum
02
18 Modules
Step-by-step learning
Hands-On
03
Real-Time Projects
Build practical applications
Career Support
04
Interview Prep
Placement-focused guidance
Complete Curriculum
Data Analytics Learning Roadmap
18 practical modules planned across a 16-week learning path. Click any module to view the topics covered.
Modules
Month 1
Month 2
Month 3
Month 4
Learning Plan
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
Introduction to Data Analytics & Business Intelligence
- What is Data Analytics and why organizations use it
- Descriptive, diagnostic, predictive and prescriptive analytics
- Data analyst roles, responsibilities and career paths
- Business intelligence concepts and analytical thinking
- Real-time analytics workflow from raw data to decision making
Excel Fundamentals for Data Analytics
- Excel interface, data types and formatting
- Formulas, functions and named ranges
- Sorting, filtering and conditional formatting
- Lookup functions: XLOOKUP, VLOOKUP and INDEX-MATCH
- Data validation and productivity shortcuts
Advanced Excel & Dashboarding
- Pivot Tables and Pivot Charts
- Advanced formulas and dynamic arrays
- Power Query fundamentals in Excel
- Interactive dashboards, slicers and KPI cards
- Business reporting using real-world datasets
SQL Fundamentals
- Relational database concepts and table structures
- SELECT, WHERE, ORDER BY and DISTINCT
- Aggregate functions and GROUP BY
- HAVING, CASE statements and data filtering
- Hands-on SQL queries for business analysis
Advanced SQL for Analytics
- INNER, LEFT, RIGHT and FULL joins
- Subqueries and Common Table Expressions
- Window functions and ranking
- Date, string and analytical functions
- Complex business reporting queries
Data Cleaning & Transformation
- Identifying missing, duplicate and inconsistent data
- Standardizing formats and business rules
- Data profiling and quality checks
- Transforming raw datasets for reporting
- Building repeatable data-cleaning workflows
Statistics for Data Analysts
- Mean, median, mode and variance
- Standard deviation and distributions
- Percentiles, quartiles and outliers
- Correlation and statistical relationships
- Using statistics for business decisions
Exploratory Data Analysis
- Understanding distributions and patterns
- Univariate and bivariate analysis
- Trend and variance analysis
- Outlier detection and business interpretation
- Turning EDA findings into actionable insights
Power BI Fundamentals
- Power BI Desktop interface and workflow
- Importing Excel, CSV, SQL and web data
- Power Query Editor basics
- Creating charts, tables, matrices and slicers
- Building a complete interactive report
Data Modeling & DAX in Power BI
- Relationships and star-schema fundamentals
- Fact and dimension tables
- Calculated columns and measures
- Core DAX functions and filter context
- Time intelligence and KPI calculations
Advanced Power BI Dashboards
- Drill-through, bookmarks and tooltips
- Dynamic titles, parameters and advanced visuals
- Dashboard UX and storytelling principles
- Row-level security fundamentals
- Publishing and sharing Power BI reports
Tableau for Data Visualization
- Tableau interface and data connections
- Dimensions, measures and calculated fields
- Filters, parameters and sets
- Interactive dashboard development
- Business storytelling with Tableau
Python for Data Analytics
- Python fundamentals for analysts
- NumPy and Pandas basics
- Reading Excel, CSV and JSON files
- Filtering, grouping and transforming data
- Automating repetitive analytics tasks
Python Visualization & Analytical Reporting
- Matplotlib and Seaborn fundamentals
- Creating business charts and distributions
- Correlation analysis and heatmaps
- Automated reporting with Python
- Combining Python insights with BI dashboards
Business Analytics & KPI Design
- Defining business questions and measurable KPIs
- Sales, finance, HR and operations analytics
- Customer and marketing analytics
- Funnel, retention and cohort analysis concepts
- Executive dashboard design and decision support
Generative AI for Data Analysts
- Using ChatGPT, Copilot and Gemini for analytics
- AI-assisted Excel formulas and SQL generation
- Prompt engineering for data analysis
- Generating dashboard narratives and executive summaries
- Validating AI-generated analytical outputs responsibly
Cloud, Automation & Real-Time Analytics
- Connecting analytics tools to cloud data sources
- Introduction to AWS analytics services
- Automating reports and refresh workflows
- Real-time dashboard and streaming analytics concepts
- Version control and documentation for analytics projects
Capstone Projects & Placement Preparation
- End-to-end real-time Data Analytics capstone project
- Excel, SQL and Power BI integrated business dashboard
- Python-based analytical project
- Portfolio, GitHub, resume and LinkedIn preparation
- Case-study interviews, mock interviews and placement preparation
Select a module to view the detailed topics.
Hands-on Learning
Capstone Projects
Practice real application flows while building projects you can discuss during interviews.
We Learn – Groceries Application
We Learn – Food Application
We Learn – Banking Application
We Learn – Delivery Application
We Learn – Airlines Application
We Learn – LMS Application
Career Preparation
Build Job-Ready Profiles
Present your skills professionally across the platforms recruiters commonly review.
GitHub
Showcase code, projects, repositories and consistent development work.
Build a stronger professional identity and improve recruiter visibility.
Portfolio
Present your projects, technical skills and practical experience clearly.
AI Skills
Learn the AI Tools Used by Modern Developers
Use AI to code faster, solve problems, build projects, prepare for interviews and stay ready for modern software jobs.
✦ChatGPT
AIClaude
GeminiGitHub Copilot
Cursor AI
PerplexityAIOpenAI API
LangChain
🤗Hugging Face
Replit AIWindsurf AI
AI Agents
AI for Coding
Write, debug and understand code faster with modern AI coding assistants.
AI for IT Jobs
Prepare resumes, interviews, projects and workplace tasks using AI.
AI for Career Growth
Stay updated with tools used by developers, startups and product teams.
Student Outcomes
Our Alumni
Surya
Infosys
Farhath
L&T
Manasa
Capgemini
Sana Lekana
Tech Mahindra
Ganesh
Genpact
Gnaneshwar
Amazon
Ravi
Hexaware
Harinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
Amazon
Common Questions
Frequently Asked Questions
Everything you need to know about our Data Analytics Training Program.
You will learn Excel, advanced Excel dashboards, SQL, data cleaning, statistics, exploratory data analysis, Power BI, DAX, Tableau, Python analytics, business analytics, Generative AI and cloud-based analytics concepts.
Yes. The curriculum starts with Data Analytics and Business Intelligence fundamentals, then moves into Excel and SQL before progressing to Power BI, Python and advanced analytics topics.
Yes. The curriculum includes formulas, lookups, Pivot Tables, Power Query, dashboards, SQL joins, subqueries, CTEs, window functions and business reporting queries.
Yes. It covers Power BI Desktop, Power Query, data modeling, DAX, interactive dashboards, row-level security and publishing, along with Tableau dashboards and business storytelling.
Yes. You will learn Python fundamentals for analysts, NumPy and Pandas basics, working with Excel, CSV and JSON files, filtering and grouping data, visualization and reporting automation.
Yes. The curriculum includes sales, finance, HR, operations, customer and marketing analytics, funnel and retention concepts, KPI design and executive dashboards.
Yes. One module covers ChatGPT, Copilot and Gemini for analytics, AI-assisted Excel formulas and SQL generation, prompt engineering, dashboard narratives and validating AI-generated outputs.
The final module includes an end-to-end Data Analytics capstone, an integrated Excel-SQL-Power BI dashboard, a Python analytical project, portfolio and GitHub preparation, resume and LinkedIn work, case-study interviews and mock interviews.
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