Track 2: AI ImplementationIntermediate LevelSkill 3 of 3

Data Analysis & AI-Assisted Insights

Transform raw data into actionable insights using AI-powered analysis tools

3 weeks Duration
12-15 hours/week
Multiple Projects

Prerequisites

Required Background

Track 1 completion or equivalent AI literacy

  • Commitment to 12-15 hours/week of study and practice
  • Strong foundation in previous track skills
  • Access to necessary tools and platforms

Learning Outcomes

Upon successful completion of this course, you will be able to:

1

Perform data analysis using AI-powered tools (ChatGPT Code Interpreter, Julius AI)

2

Create data visualizations and dashboards with AI assistance

3

Clean, transform, and prepare data for analysis

4

Generate statistical insights and predictive models

5

Communicate data findings through reports and presentations

6

Apply AI analysis to business decision-making

Content Outline

Week 8: Data Fundamentals & AI-Assisted Analysis
Week 8
  • Data literacy: Understanding data types, formats, and structures
  • Data cleaning with AI: Handling missing values, outliers, and inconsistencies
  • ChatGPT Code Interpreter: Uploading data and generating analysis
  • Claude for data analysis: Comparative strengths and use cases
  • Exploratory data analysis: Descriptive statistics and initial insights
  • Project: Analyze a business dataset and generate insights report
Week 9: Visualization & Dashboard Creation
Week 9
  • Data visualization principles: Choosing the right chart types
  • AI-powered visualization tools: Julius AI, DataGPT, Tableau with AI
  • Creating interactive dashboards: Connecting data sources and updating automatically
  • Storytelling with data: Presenting insights effectively
  • Automated reporting: Scheduling and distributing data reports
  • Project: Build an automated executive dashboard
Week 10: Predictive Analysis & Business Intelligence
Week 10
  • Introduction to predictive analytics: Forecasting and trend analysis
  • AI-assisted statistical analysis: Correlation, regression, and significance testing
  • Business intelligence applications: Sales forecasting, customer segmentation, churn prediction
  • Interpreting AI-generated analysis: Critical evaluation of results
  • Communicating technical findings to non-technical audiences
  • Project: Create a predictive model for a business scenario

Ready to Master Data Analysis & AI-Assisted Insights?

Join the I-Gamify AI Academy and advance your AI expertise. This course is part of Track 2: AI Implementation.