# NCR Course Module 2
## Class 3 Lesson Plan
## Data Transformation and Visualization

[(Recording)](https://t.me/ResearchWG/129/4488)

>Register for the class [here](https://near.social/research-collective.near/widget/NCR.v1)!

**Facilitator**: [Chloe](https://near.social/mob.near/widget/MyPage?accountId=chloe.near)
**Co-Facilitators**: [Nedlar](https://near.social/mob.near/widget/ProfilePage?accountId=15870c8972a9fe6cdb7dfc2df835740108e8674cc170a091cd0ece0b9e4f6cfa) & [BGem](https://near.social/mob.near/widget/ProfilePage?accountId=bheegem.near)
**Guest Lecturer**: None
**Date**: March 13, 2024
**Time**: 10 AM AST (2 PM UTC)
**Duration**: 1 hour
**Platform**: [NCR Thread on Telegram](https://t.me/ResearchWG/181)

**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](https://t.me/ResearchWG/1436)
- **Course Syllabus**: [NRC Course Syllabus](https://hackmd.io/@doulos819/NRC)
- **Introduction to Data Visualization in Python**: [Data Visualization with Python](https://realpython.com/analyzing-obesity-in-england-with-python/)
- **GPT-4 Overview**: [What is GPT-4?](https://openai.com/research/gpt-4)
- **Python Pandas Documentation**: [Pandas Documentation](https://pandas.pydata.org/docs/)

### Agenda

| Time | Topic | Activity | Resource |
|-----------|------------------------------|-------------------------------------------------------|------------------------------------------------------|
| 0-5 mins | Introduction | Briefing on course outline and objectives. | [ResearchWG on Telegram](https://t.me/ResearchWG/181)|
| 5-15 mins | Reading CSV with Pandas | Hands-on Python tutorial for reading CSV files. | [Data Visualization with Python](https://realpython.com/analyzing-obesity-in-england-with-python/) |
| 15-25 mins| GPT-4 for Data Analysis | Introduction to using GPT-4 for data interpretation. | [GPT-4 Research Paper](https://openai.com/research/gpt-4)|
| 25-40 mins| Lecture from Chloe | How to think about AI x Web3 in practice | [NDC Chatbot](https://ndc-chatbot.nearhub.club/) / [ TG channel for AI summary of NDC chats ](https://t.me/ndcsummaries) / [A.I. Tarot Card Readings](https://tarotbot.app) |
| 40-50 mins| Q&A | Open floor for questions. | [Homework and Assignment Thread on TG](https://t.me/ResearchWG/1436) |
| 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](https://hackmd.io/@doulos819/ncr-04)|

### 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')