AI - Data Analyst

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AI - Data Analyst

Master data analysis, visualization, dashboards & AI-assisted analytics — and become the analyst companies rely on for data-driven decisions.

• Learn Modern Data Analysis + AI Tools

• Build Real Business Dashboards & Data Projects

• Mentorship from Industry Data Analysts

• Placement-Focused Internship

View Curriculum

About Program

The AI Data Analyst Internship Program is designed for final-year VTU students who want to develop strong analytical, visualization, and AI-assisted data skills. The internship begins with foundations in Excel, SQL, and Python, then advances into Power BI dashboards, exploratory data analysis, business intelligence, and AI-driven insight generation. Students work with real-world datasets across domains and learn to clean, preprocess, analyze, and visualize data effectively. With hands-on mentorship and project-oriented learning, learners build portfolio-ready dashboards and analytical models. By the end of the program, students are job-ready for roles in data analytics, BI, data engineering (junior), and AI-assisted analysis.

Key Features

Learn Modern Data Analysis + AI Tools

Gain expertise in Excel, SQL, Python, Power BI, and AI-powered analytics — the exact skillset employers expect from data analysts today.

Build Real Business Dashboards & Data Projects

Work with real datasets to build dashboards, perform business analysis, and generate insights used in Finance, Marketing, HR, and Operations.

Mentorship from Industry Data Analysts

Receive guidance from professionals who work on real-world business intelligence, dashboards, forecasting, and automation.

Placement-Focused Internship

Get resume support, LinkedIn optimization, GitHub project portfolio, and interview training for data analyst and BI roles.

Program Content

Module 1 – Excel for Data Analytics (Beginner to Advanced)

Topics Covered:

• Excel interface & shortcuts

• Functions: SUM, IF, VLOOKUP, INDEX-MATCH

• Conditional formatting

• Pivot tables & pivot charts

• Data cleaning techniques

• Lookup operations

• Data validation

• Creating automated Excel dashboards

Module 2 - SQL for Data Analysis

Topics Covered:

• SQL basics: SELECT, WHERE, ORDER BY

• Joins: INNER, LEFT, RIGHT, FULL

• Aggregations: COUNT, SUM, AVG

• Group By & Having

• Subqueries & CTEs

• Window functions (intro)

• Data extraction from large tables

• Query optimization basics

Module 3 - Python for Data Analysis

Topics Covered:

• Python basics (variables, lists, dicts)

• Working with Jupyter Notebook

• Pandas for data manipulation

• NumPy for numerical operations

• Matplotlib & Seaborn for visualization

• Data cleaning with Pandas

• Handling missing values & outliers

• Automating basic analytics tasks

Module 4 - Exploratory Data Analysis (EDA)

Topics Covered:

• Understanding distributions & trends

• Visualizing relationships between variables

• Correlation heatmaps

• Statistical summaries

• Data profiling reports

• Outlier detection

• Identifying hidden patterns

• EDA storytelling best practices

Module 1 - Power BI for Business Intelligence

Topics Covered:

• Power BI interface

• Importing & transforming data (Power Query)

• Data modeling basics

• Creating visuals: bar, line, KPIs

• Slicers & filters

• Designing dashboards

• Publishing reports

• DAX formulas (intro)

Module 2 - Business Analytics & Domain Understanding

Topics Covered:

• Understanding KPIs across industries

• Marketing analytics (CAC, ROI, funnels)

• Finance analytics (PnL, forecasting)

• HR analytics (attrition, hiring)

• Operations analytics

• Customer segmentation

• Profitability analysis

• Case studies from real companies

Module 3 - AI for Data Analytics

Topics Covered:

• Using ChatGPT/Gemini for analysis

• Insight generation using AI

• AI-assisted EDA

• Automated summarization

• Querying data using natural language

• AI-driven forecasting

• Converting dashboards into narratives

• Building AI-assisted analytical workflows

Module 4 - Automation & Reporting

Topics Covered:

• Automating Excel tasks

• Automated PDF report generation

• Scheduling BI reports

• Building automated email reports

• Creating reusable templates

• Data refresh automation

• Documentation best practices

• End-to-end reporting workflows

Module 5 - Data Ethics, Quality & Governance

Topics Covered:

• Data quality dimensions

• Responsible AI & analytics

• Anonymization & privacy basics

• Audit trails & access control

• Compliance (GDPR/Indian DPDP intro)

• Data verification techniques

• Bias detection basics

• Importance of data governance

Module 1 - Product Thinking for Data Solutions

Topics Covered:

• Identifying real-world business problems

• Mapping data to business decisions

• Designing dashboards with purpose

• Creating problem statements

• User flow mapping for data products

• Visual storytelling with data

Skills

PythonSQL