Data Analyst Using Ai

Course Overview

This comprehensive 3-month program transforms students into job-ready data analysts with expertise in leveraging AI tools. The course combines traditional data analysis techniques with cutting-edge AI applications to prepare professionals for the modern data landscape.

Month1: Foundation Data Analysis Fundamentals & Tools

Week 1: Introduction to Data Analysis & AI Tools

Days 1-2: Course Introduction

• Data analyst role in today’s AI-driven landscape
• Course roadmap and expectations
• Setting up learning environment and tools
• Introduction to AI-assisted data analysis

Days 3-5: Excel & Google Sheets for Data Analysis

• Advanced spreadsheet functions and formulas
• Pivot tables and data summarization
• Data validation and cleaning
• Using AI add-ons to automate spreadsheet tasks

Weekend Project: Sales data analysis using AI-powered features

Week 2: SQL Fundamentals

Days 6-7: Database Basics

• Relational database concepts
• Database structure and design
• SQL syntax and conventions

Days 8-10: SQL Queries

• SELECT statements and filtering
• Joins and relationships
• Aggregation functions
• Using AI-powered SQL generators

Weekend Project: Building a complex database query with AI assistance

Week 3: Python for Data Analysis I

Days 11-12: Python Basics

• Python syntax and data structures
• Control structures and functions
• Libraries and package management

Days 13-15: Python Data Analysis Libraries NumPy fundamentals

• Pandas for data manipulation
• Data loading and basic cleaning
• AI-assisted coding techniques for data analysis

Weekend Project: Data cleaning and transformation with AI-augmented Python

Week 4: Data Visualization Fundamentals

Days 16-17: Visualization Principles

• Data visualization best practices
• Choosing the right visualization
• Dashboard design principles

Days 18-20: Visualization Tools Matplotlib and Seaborn

• Plotly for interactive visualizations

Tableau fundamentals

Using AI to generate visualization code and enhance charts

Weekend Project: Creating an interactive dashboard with AI-generated visualizations

Month 2: Advanced Analysis & AI Integration

Week 5: Statistical Analysis

Days 21-22: Statistical Foundations

• Descriptive statistics
• Probability distributions
• Statistical inference

Days 23-25: Applied Statistics Hypothesis testing

• Correlation and regression analysis
• Statistical significance
• AI tools for statistical analysis and interpretation

Weekend Project: Market research analysis with AI-enhanced statistical methods

Week 6: Python for Data Analysis II

Days 26-27: Advanced Pandas

• Complex data transformations
• Time series analysis
• Data merging and reshaping

Days 28-30: Data Cleaning and Preparation

• Handling missing data
• Outlier detection and treatment
• Feature engineering
• AI-assisted data cleaning workflows

Weekend Project: ETL pipeline creation with AI tools

Week 7: Machine Learning for Data Analysts

Days 31-32: ML Fundamentals

• Types of machine learning problems
• Supervised vs. unsupervised learning
• Model evaluation metrics

Days 33-35: Practical ML Applications

• Predictive modeling with scikit-learn
• Classification and regression tasks
• Clustering for customer segmentation
• Using AutoML tools for model building

Weekend Project: Customer segmentation using AI-assisted clustering

Week 8: Advanced Visualization & Storytelling

Days 36-37: Advanced Dashboards

• Interactive dashboard creation
• Advanced Tableau techniques
• Power BI fundamentals

Days 38-40: Data Storytelling

• Crafting data narratives
• Presentation techniques
• Stakeholder communication
• Using AI to generate insights and narratives

  Weekend Project: End-to-end analysis project with AI-enhanced storytelling

Month 3: Professional Applications & Capstone Project

Week 9: AI Tools for Data Analysts

Days 41-42: Generative AI for Analysis Using LLMs for data exploration

• ChatGPT for code generation
• Prompt engineering for data tasks

Days 43-45: AI-Assisted Workflows

• Automated data pipelines
• AI-powered data cleaning Insight generation with AI
• Building AI-enhanced analysis templates

Weekend Project: Automating a data workflow with AI tools

Week 10: Business Intelligence & Decision Making

Days 46-47: BI Tools & Systems

• Business intelligence concepts
• BI tool comparison and implementation
• KPI development and tracking

Days 48-50: Decision Science From data to decisions

• A/B testing fundamentals
• Scenario analysis and forecasting
• AI for decision support systems

Weekend Project: BI dashboard with AI-generated insights

Week 11: Domain-Specific Applications

Days 51-52: Industry Applications Financial data analysis

• Marketing analytics
• Operations and supply chain analytics

Days 53-55: Advanced Use Cases

• Customer journey analysis
• Web analytics and user behavior
• Forecasting and time series applications
• Using domain-specific AI models

Weekend Project: Industry-specific analysis with specialized AI tools

 

 

 

Week 12: Capstone Project & Career Preparation

Days 56-60: Capstone Project

• End-to-end data analysis project
• Real-world dataset analysis
• AI-enhanced methodology implementation
• Presentation preparation

Days 61-65: Career Preparation Portfolio development

• Resume and LinkedIn optimization
• Technical interview preparation
• Data analyst job market trends
• AI tools for job search and application

Final Presentation:

• Capstone project showcase Learning Methods
• Interactive lectures and demonstrations
• Hands-on coding exercises
• Real-world case studies
• Group projects and peer reviews
• AI-assisted learning exercises
• Weekly office hours with instructors
• Online discussion forums

 

Tools & Technologies Covered

Data Analysis: Excel, Google Sheets, SQL, Python (basics)

Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly

Databases: MySQL, PostgreSQL, SQLite

AI Integration: ChatGPT, GitHub Copilot, AutoML tools, ML libraries Business Tools: Google Analytics, Looker, Monday.com

Version Control: Git, GitHub

Assessment Methods

• Weekly practical assignments (40%)
• Midterm project (20%)
• Capstone project (30%)
• Participation and peer reviews (10%)
• Post-Course Support
• 3-month access to course mentors

Job placement assistance

• Alumni community membership
• Continued access to course materials and updates
• AI assistant access for ongoing learning support