Contact Us
Artificial Intelligence

Leveraging AI for Data Analysis and Visualization

Group Training
Course Outline

Overview

This course examines how data analysts can utilize AI tools such as LLMs (Large Language Models) and other specialized tools to accelerate, automate and enhance their analyses, spreadsheets, programs, visualizations and presentations.

Duration

2 Days

Who Should Take This Course

Audience

  • Familiarity with data analysis fundamentals (e.g., SQL, Python, spreadsheets).
  • Basic understanding of visualization tools like Power BI, Tableau, or matplotlib.

Why You Should Take This Course

Learning Objectives

In the duration of this course, students will gain:
  • Mastery of AI tools for accelerating data analysis and visualization workflows.
  • Proficiency in Python, SQL, and AI-assisted spreadsheet tools for data tasks.
  • Confidence in creating advanced visualizations and data narratives with AI.
  • Awareness of ethical considerations and biases in AI-driven analyses.

Course Outline

Leveraging AI for Data Analysis and Visualization

Day 1: Foundations of AI-Driven Data Analysis

Morning Session (4 hours)

  • Introduction to AI in Data Analysis (0.5 hour)

    • Role of AI in modern data workflows

    • Overview of key tools: LLMs, automated analysis platforms, and visualization assistants

    • Benefits: speed, accuracy, and insights generation

  • Using LLMs for Data Queries and Insights (1.5 hours)

    • How LLMs support SQL queries, exploratory data analysis (EDA), and scripting

    • Hands-on: generating SQL queries for filtering and aggregation

    • Hands-on: using Python (pandas) for EDA with LLM guidance

    • Best practices for prompt engineering

  • Data Cleaning and Transformation with AI (1 hour)

    • AI-powered tools for data cleaning (OpenRefine, Python libraries)

    • Automating data transformation tasks with LLMs

    • Hands-on: cleaning a messy dataset using Python and AI suggestions

  • Accelerating Spreadsheet Tasks with AI (1 hour)

    • AI-powered spreadsheet features (Excel Copilot, Google Sheets AI)

    • Automating formulas, conditional logic, and predictive modeling

    • Hands-on: solving complex spreadsheet problems using AI tools

Afternoon Session (4 hours)

  • AI-Assisted Statistical Analysis (2 hours)

    • Performing statistical tests and generating summaries with AI

    • Hands-on: using Python libraries (statsmodels, scipy) with AI support

    • Automating hypothesis testing and regression analysis

  • Exploring Data Patterns and Trends with AI (2 hours)

    • Identifying correlations, anomalies, and clusters with AI

    • Tools and techniques: Python (scikit-learn, matplotlib), LLM-guided analysis

    • Hands-on: applying clustering algorithms using AI-generated Python scripts

Day 2: AI-Powered Data Visualization and Storytelling

Morning Session (4 hours)

  • Automating Data Visualization with AI (2 hours)

    • Creating visualizations using AI tools (Tableau GPT, Power BI Copilot, matplotlib with LLMs)

    • Hands-on: automating chart generation in Python

    • AI-driven dashboard customization in Power BI

  • Advanced Visualization Techniques (2 hours)

    • AI-assisted recommendations for chart types, color schemes, and annotations

    • Creating multi-dimensional and interactive visualizations

    • Hands-on: building advanced dashboards in Power BI or Tableau

Afternoon Session (4 hours)

  • Storytelling with Data Using AI (2.5 hours)

    • Turning insights into narratives with AI-generated summaries

    • AI tools for presentations (PowerPoint Copilot, Canva AI)

    • Hands-on: generating a report summary with LLMs

    • Integrating data visualizations into presentations

  • Responsible AI Use in Data Analysis (1 hour)

    • Understanding bias in data and AI-generated insights

    • Ensuring transparency and accountability in analysis workflows

    • Case study: identifying and correcting bias in AI-driven analyses

Search UMBC Training Centers