NCR Course Module 2

Class 3 Lesson Plan

Data Transformation and Visualization

(Recording)

Register for the class here!

Facilitator: Chloe
Co-Facilitators: Nedlar & BGem
Guest Lecturer: None
Date: March 13, 2024
Time: 10 AM AST (2 PM UTC)
Duration: 1 hour
Platform: NCR Thread on Telegram

Target Audience:

• Enthusiasts keen on enhancing blockchain data skills.
• Researchers in blockchain applications.
• Data analysts seeking blockchain insights.
• Computer science students and academics.
• NEAR community members seeking technical knowledge.

Learning Objective: The aim is to familiarize participants with data transformation and visualization techniques using Python and GPT-4. (The case study focuses on EasyPoll data from the Near Protocol.)

Pre-Reading Material

• Homework and Assignments: Homework and Assignment Thread on TG
• Course Syllabus: NRC Course Syllabus
• Introduction to Data Visualization in Python: Data Visualization with Python
• GPT-4 Overview: What is GPT-4?
• Python Pandas Documentation: Pandas Documentation

Agenda

Time	Topic	Activity	Resource
0-5 mins	Introduction	Briefing on course outline and objectives.	ResearchWG on Telegram
5-15 mins	Reading CSV with Pandas	Hands-on Python tutorial for reading CSV files.	Data Visualization with Python
15-25 mins	GPT-4 for Data Analysis	Introduction to using GPT-4 for data interpretation.	GPT-4 Research Paper
25-40 mins	Lecture from Chloe	How to think about AI x Web3 in practice	NDC Chatbot / TG channel for AI summary of NDC chats / A.I. Tarot Card Readings
40-50 mins	Q&A	Open floor for questions.	Homework and Assignment Thread on TG
50-55 mins	Summary and Next Steps	Summary of the session and introduction to next lesson.	Next Class - 4: Advanced Data Evaluation Method With Ai

Activities Details

1. Python and Pandas Basics

   • Brief overview of Python and Pandas for data manipulation.
   ​​​​# Import Pandas library
   ​​​​import pandas as pd
   ​​​​
   ​​​​# Create a simple DataFrame
   ​​​​df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
   

2. Reading CSV with Pandas

   • Step-by-step guidance on loading a CSV file into a Pandas DataFrame.
   ​​​​# Reading a CSV file into a DataFrame
   ​​​​df = pd.read_csv('data.csv')
   ​​​​
   ​​​​# Display the first 5 rows of the DataFrame
   ​​​​df.head()
   

3. GPT-4 for Data Analysis

   • Exploration of GPT-4's data analysis capabilities. Discuss its utility in data interpretation.

4. Case Study: EasyPoll

   • Practical application focusing on processing and analyzing CSV files from EasyPoll.
   ​​​​# Read EasyPoll CSV data
   ​​​​df = pd.read_csv('easypoll_data.csv')
   ​​​​
   ​​​​# Text analysis will be conducted using the GPT-4 UI at this stage.
   ​​​​
   ​​​​# Visualization (e.g., bar plot of categories)
   ​​​​df['category'].value_counts().plot(kind='bar')