AI and Data Analytics

This module introduces learners to the end-to-end role of a Data Analyst in real companies. Students understand how raw data is converted into business decisions across domains like IT, finance, healthcare, and e-commerce.

Key Topics Covered

• What Data Analysts actually do in organizations
• Difference between Data Analyst, Business Analyst & AI Analyst
• Types of data: structured, semi-structured & unstructured
• Data analytics lifecycle: collect → clean → analyze → visualize → decide
• Understanding KPIs, metrics, and business questions.

   

  Outcome
  Clear understanding of industry expectations & job roles

Learners master Excel as a business analytics tool, not just a spreadsheet. Focus is on handling large datasets, creating insights, and reports used by managers.

Key Topics Covered

• Data cleaning and formatting for analysis
• Advanced formulas: IF, COUNTIF, SUMIFS, XLOOKUP, INDEX-MATCH
• Sorting, filtering & conditional formatting
• Pivot tables & pivot charts for reporting
• Creating professional MIS & summary reports
  
  Mini Project
  Sales Performance & Revenue Analysis Report
  
  Outcome
  Ability to work on Excel-based analyst roles & internships

This module focuses on writing real-world SQL queries used by data teams. Learners work with relational databases and practice interview-oriented problem solving.

Key Topics Covered

• Database fundamentals & table relationships
• SELECT, WHERE, ORDER BY, GROUP BY, HAVING
• JOINS (INNER, LEFT, RIGHT, FULL)
• Subqueries & Common Table Expressions (CTEs)
• Query optimization & analytics use-cases
• SQL interview scenarios & case questions
  
  Project
  Customer & Order Analytics using SQL
  
  Outcome
  Confidently extract insights from production-level databases

Learners use Python as a core analytics language to clean, analyze, and visualize datasets. Strong focus on hands-on practice and real datasets.

Key Topics Covered

• Python fundamentals for data analytics
• Working with NumPy arrays & Pandas DataFrames
• Data cleaning, handling missing values & duplicates
• Exploratory Data Analysis (EDA)
• Data visualization using Matplotlib & Seaborn
• Importing/exporting CSV, Excel & JSON data
  
  Project
  EDA on Real Business Dataset
  
  Outcome
  Perform end-to-end analysis using Python

Statistics is taught from a practical business lens, focusing on interpretation rather than heavy mathematics.

Key Topics Covered

• Descriptive statistics (mean, median, variance, std dev)
• Probability concepts for analytics
• Correlation & trend analysis
• Hypothesis testing & business inference
• Understanding data bias & outliers
  
  Outcome
  Make confident, data-backed decisions

Learners design interactive dashboards used by leadership teams for decision-making.

Key Topics Covered

• Connecting multiple data sources
• Data modeling & relationships
• DAX fundamentals
• Creating KPIs & metrics
• Designing interactive dashboards
• Storytelling with data & insights
• Executive Dashboard using Power BI
  
  Outcome
  Build job-ready dashboards for business users

This module introduces AI-powered analytics workflows used in modern teams to increase productivity.

Key Topics Covered

• Using AI tools for data cleaning & analysis
• AI-assisted EDA & report generation
• Prompt engineering for data tasks
• Automating insights & summaries
• Ethical use of AI in analytics
  
  Mini Project
  AI-Powered Business Insight Report
  
  Outcome
  Work faster using AI as a co-pilot

This final module focuses on portfolio building & placements.

Capstone Project Options (Choose Any 2)

• Retail Sales & Customer Behavior Analysis
• HR Analytics: Attrition & Performance
• Financial Data Analysis & Dashboard
• Marketing Campaign Performance Analytics
  
  Career Preparation

• Resume & LinkedIn optimization
• Project-based portfolio building
• SQL & Python interview questions
• Mock interviews & case studies
  
  Outcome
  Job-ready for Data Analyst / AI Data Analyst roles
  
  Program Outcomes
  By the end of this program, learners will:

• Work confidently with Excel, SQL, Python & Power BI
• Apply AI tools for analytics & reporting
• Build 3–4 real-world projects
• Crack entry to mid-level data analyst interviews
• Be ready for analytics roles across industries