Lecture 30

Programming vs. Power BI vs. Excel for Data Analysis

Byeong-Hak Choe

bchoe@geneseo.edu

SUNY Geneseo

December 2, 2024

Programming vs. Power BI vs. Excel for Data Analysis

R/Python Programming for Data Analysis

• Usage: Advanced data analysis and statistical computing
• Features:
  • Extensive libraries and packages (e.g., pandas, polars, NumPy, seaborn, tidyverse)
  • High flexibility and customization
  • Ideal for complex analyses and machine learning tasks
  • Machine Learning Libraries in Python: scikit-learn, TensorFlow, PyTorch )
  • Machine Learning Libraries in R: keras, torch )

R/Python Programming for Data Analysis

• Data Analysis Capabilities
  • Efficient handling of large datasets and complex transformations
  • Highly customizable and publication-quality graphics
  • Advanced statistical analysis and modeling
  • Machine learning and AI integration
  • Reproducible work through scripting and version control via Git
    • Reproducibility: Scripts ensure that tasks are performed consistently every time, reducing errors.

Power BI for Data Analysis

• Type: Business Intelligence and data visualization tool
• Features:
  • User-friendly interface with drag-and-drop functionality
  • Real-time data connectivity
  • Interactive dashboards and reports
• Data Analysis Capabilities
  • Data modeling and transformation with Power Query and DAX
  • User-friendly drag-and-drop interface
  • Real-time user-friendly data connectivity and interactive reports
  • Limited advanced statistical functions compared to programming languages

Excel for Data Analysis

• Type: Spreadsheet software
• Features:
  • Widely accessible and easy to use
  • Supports formulas, pivot tables, and basic charts
  • Suitable for basic data manipulation and small datasets
• Data Analysis Capabilities
  • Basic statistical functions and data analysis tools
  • Pivot tables for summarizing data
  • Limited scalability: Not ideal for handling large datasets, repeated tasks, or complex analyses
  • Limited interactivity: Lacking advanced capabilities for creating highly interactive data visualizations or dashboards

Combining Generative AI with These Tools

• Programming (R and Python)
  • AI-Assisted Coding: Code generation using AI (e.g., GitHub Copilot, ChatGPT); Code conversion using AI (e.g, R \(\rightleftharpoons\) Python)
• Power BI
  • AI Visuals: Built-in AI visuals like Key Influencers and Decomposition Tree
  • Integration with Azure AI: Incorporate Azure Cognitive Services for advanced analytics
• Excel
  • Ideas Feature: AI-powered insights (e.g., Microsoft 365 Copilot, Customized ChatGPT) and data analysis suggestions
  • Add-ins and Extensions: Utilize AI-powered add-ins for enhanced functionality

Programming vs. Power BI vs. Excel

Conclusion

• Tool Selection:
  • Choose Programming (R and Python) for advanced and customizable data analysis
  • Opt for Power BI for interactive visualizations and business intelligence reporting
  • Use Excel for basic data analysis and small-scale data tasks
• Generative AI Integration:
  • Enhances productivity and capabilities across all tools
  • Automates routine tasks and provides advanced analytical insights
  • Bridges the gap between technical complexity and user accessibility

Course Summary

Course Summary

• Introduction to Data Analytics
• Programming Basic for Data Analysis
• Data Preparation and Management
  • Technologies Used to Manage and Process Big Data
• Data Preparation and Management with R
• Career Session
• Data Storytelling
  • Data Visualization with R
  • Data Storytelling Project
• Programming vs. Power BI vs. Excel for Data Analysis
  • The Use of Generative AI

Course Summary

Student Course Evaluation (SCE)

• I have tried to improve your learning experience in this course.
• I value your feedback immensely.
• I request for your participation in the Student Course Evaluation (SCE).

• Take 10 minutes right now to complete the SCE.
• On your laptop, access the SCE form for DANL 101 as follows:
  1. Log in to Knightweb
  2. Click on the “Surveys” option
  3. Choose DANL 101 (class for which you want to fill out the SCE) and then complete the SCE survey.