HackMD
NCR Course Module 2
Class 3 Lesson Plan
Data Transformation and Visualization
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
-
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']}) -
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() -
GPT-4 for Data Analysis
- Exploration of GPT-4's data analysis capabilities. Discuss its utility in data interpretation.
-
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')
