Data Analytics with AI

Module 1: Foundations of Data Analytics
What is Data Analytics?

• Analytics lifecycle: Define → Collect → Clean → Analyze → Visualize → Interpret
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Roles: Data Analyst vs Data Scientist vs AI Analyst.

Module 2: Introduction to Generative AI
What is Generative AI?

• LLMs (Large Language Models): GPT, Gemini, Claude, etc.
• Use cases in analytics: code generation, data explanation, automation
• Responsible use of AI and ethics

Module 3: Data Collection and Integration

• Data formats: CSV, Excel, JSON, APIs, and Databases
• ETL Tools: Python (requests, pandas), Power Query, SSIS
• Using AI prompts to generate API scripts and data transformation code
• Automating ETL workflows with AI

Module 4: Data Cleaning and preparation

• Handling missing values, duplicates, and inconsistent types
• Data Transformation Techniques
• Using ChatGPT to explain or write pandas code for cleaning
• AI-driven data profiling tools (e.g., Microsoft Fabric Data Wrangler)

Module 5: Data Visualization

• Best practices for Visualizations in Reports and dashboards
• Tools: Excel, Power BI, Tableau, Python (Matplotlib/Seaborn/Plotly)
• Using AI to suggest visuals based on data type
• Natural language to chart (e.g., “Show me the top 5 products by revenue”)

Module 6: SQL for Data Analytics

• Basic to advanced SQL Queries
• SQL Clauses, Constraints, Functions, Joins, subqueries, CTE, window functions, views,
• Indexes, Stored Procedures.
• Query Optimization
• Automation of Script execution (Job Creations)
• Use of AI copilots in SQL Server
  Natural Language to SQL with ChatGPT

Module 7: Python for Analytics + AI

• Python Basics: Datatypes, Operators, Control Flow Statements, Data structures, Built-in
• Functions, OOPS Concepts.
• Python with SQL Server
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• ChatGPT-assisted code generation and debugging
• Automating reports with Python and AI assistance

Module 8: Statistics for Data Analytics

• Statistics & Probability for Data Analytics
• Descriptive Statistics (mean, median, mode, standard deviation)
• Inferential Statistics (hypothesis testing, confidence intervals)
• Ask AI to explain statistical results in plain English
• Correlation and Regression Analysis
• Tools: Python (SciPy, stats models)
• Module 9: Machine Learning (Optional)
• Introduction to AI & Machine Learning
• Overview of AI and ML
• Types of Machine Learning
• Regression and Classification Algorithms
• Clustering Techniques

Module 10: Generative AI for Data Analysts

• Prompt engineering for data tasks
• Automating documentation, reporting, dashboards
• Creating data dictionaries, SQL explanations, or Python scripts with AI
• Tools: ChatGPT, Copilot, Gemini, Notion AI, ClickUp AI

Module 11: Carrer Guidance and Resume Building Preparation

• Career Preparation & Projects
• Resume and LinkedIn Optimization
• Interview Questions and Preparation
• Mock Interviews

Module 12: Capstone Project with Real Dataset Capstone Project Examples
Retail, Healthcare, Finance, Banking, HR

Module 13: Realtime Tools for Work allocation and Management

• Introduction to Cloud Platforms (AWS/GCP/Azure)
• DevOps Repos, Branches, Pull Request, Backlog Tasks creation,
• Agile Methodology Overview (Sprint Plan Activities)
• Development & Production Support Activities and tools (Azure Devops, Service Now)