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AI Powered Data Analytics Mastery

20

Bhevin

AI Powered Data Analytics Mastery – Course Content Outline Introduction Section 1: Data Analytics Fundamentals Section 2: Generative AI Tools …

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AI Powered Data Analytics Mastery – Course Content Outline

Introduction

  • Welcome & Orientation
  • Overview of AI in Data Analytics
  • Setting Up: Python, Anaconda, SQL, Power BI

Section 1: Data Analytics Fundamentals

  • Data analysis workflow & storytelling
  • Business applications of data analytics
  • Excel basics for analytics

Section 2: Generative AI Tools for Analytics

  • Using ChatGPT, Claude, Perplexity, Grok for data processing
  • AI-driven prompt engineering
  • Automation tools (n8n, Julius, Eraser)

Section 3: Vibe Coding & Python Essentials

  • Python for analytics: syntax, data types, functions, OOP
  • Libraries: NumPy, Pandas for data wrangling
  • Practical vibe coding for project prototyping

Section 4: SQL & Database Management

  • Writing & optimizing queries
  • Joins, window functions, AI-assisted data cleaning
  • Hands-on: Connecting Python, Power BI with databases

Section 5: Exploratory Data Analysis (EDA) with AI

  • Automating EDA with AI tools
  • Data visualization best practices
  • AI recommended KPIs & chart selection

Section 6: Data Visualization with Power BI

  • Dashboard building: design & customization
  • Integrating AI for deeper insights
  • DAX basics & advanced Power BI features

Section 7: Predictive Analytics & Machine Learning

  • Building ML models: KNN, Decision Trees, Random Forests
  • Using AI tools for feature engineering
  • Interpreting predictive model outputs

Section 8: No-Code Data Analytics Solutions

  • Using n8n, Julius, Eraser for automated workflows
  • Building analytics pipelines without code

Section 9: Microsoft Fabric & Integration

  • Overview and connecting Power BI with Fabric
  • Copilot integration for enhanced analytics

Section 10: Real-World Projects & Case Studies

  • Inventory management dashboard (Power BI + AI)
  • Student engagement dashboard (AI tools + visualization)
  • Data transformation, cleaning, and KPI selection (ChatGPT, Grok, Perplexity)
  • Automating common analytics workflows

Section 11: Interview Preparation & Career Skills

  • Common SQL & Python interview questions
  • Business storytelling with dashboards
  • Tips for succeeding in the data analytics job market

Course Resources

  • 31+ hours of on-demand lectures
  • 22+ articles
  • 40+ downloadable resources
  • Lifetime access, certificate on completion

Requirements

  • Basic computer and spreadsheet skills
  • Introductory Python/SQL knowledge (provided prep modules)
  • Willingness to learn and experiment with AI tools

Course outcome:
You’ll gain practical skills in AI-powered data analytics, including hands-on experience with Python, SQL, and Power BI, while leveraging AI tools for automation, visualization, predictive analytics, and more. This structure provides a powerful blend of theory, practical projects, and career readiness.

Course Currilcum

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