Concept Enumeration

Prompt

``` prompt I would like to develop a new course called "Introduction to Data Science with Python using AI".

I would like you to generate a list of all the core concepts
that we would cover in this course and the dependency
relationships between these concepts so we could order
them and create an learning graph. This course would assume
that students have basic Python programming experience. 
This course would be a class that covers the following topics:

1. What is data science? 
2. Summary of business needs for employees with data science skills. 
3. Review of Python concepts 
4. How generative AI can help you learn Python 
5. Basic Python libraries for handling data such as NumPy 
6. Python statistical libraries 
7. Python libraries for machine learning 
8. Python libraries for working with graph data 
9. Python libraries for visualization with a focus on Plotly 
10. Python libraries for NLP 
11. Python libraries for vector stores 
12. Python libraries for GenAI including LangChain and LlamaIndex

Please create a numbered list of the 250 most important topics 
that would be taught in this course. Begin with the prerequisite
concepts and then proceed to defining key terms. Then include
the core concepts used in Python data analysis.
Finally, close with some potential final project concepts 
that students could create. Think carefully about the ordering 
of the concepts from simple to more abstract. For each concept, 
return a conceptID followed by short name which would be the
label for the concept in a learning graph (concept dependency).
Feel free to use short names and abbreviations.

ChatGPT

Thought for 1m 34s

DS Definition - Definition of Data Science
DS Importance - Importance of Data Science
DS vs DA - Data Science vs. Data Analysis
DS Workflow - Data Science Workflow
DS Roles - Roles in Data Science
DS DataCollection - Data Collection Methods
DS DataCleaning - Data Cleaning Processes
DS DataExploration - Exploratory Data Analysis
DS FeatureEngineering - Feature Engineering
DS Modeling - Modeling and Algorithms
DS Evaluation - Model Evaluation Techniques
DS Deployment - Model Deployment Strategies
Business DS Needs - Business Needs for Data Science Skills
Business DS Impact - Impact on Decision Making
Business DS Examples - Business Applications of Data Science
Python DataTypes - Data Types in Python
Python DataStructures - Data Structures (List, Dict, Set)
Python Control - Control Structures (Loops, Conditionals)
Python Functions - Functions and Modules
Python OOP - Object-Oriented Programming
Python FileHandling - File Handling
Python Exceptions - Exception Handling
Python Debugging - Debugging Techniques
Python LibrariesIntro - Introduction to Python Libraries
AI Intro - Introduction to Generative AI
AI LearnPython - AI Tools for Learning Python
AI CodeAssistants - Using AI Assistants for Coding
AI LearningResources - AI-Generated Learning Resources
AI Ethics - Ethical Considerations of AI
NumPy Intro - Introduction to NumPy
NumPy Arrays - NumPy Arrays and Operations
NumPy Broadcasting - Broadcasting in NumPy
NumPy AdvancedIndexing - Advanced Indexing
NumPy MathFunctions - Mathematical Functions
Pandas Intro - Introduction to Pandas
Pandas DataFrames - DataFrames and Series
Pandas IO - Reading and Writing Data
Pandas Selection - Data Selection and Filtering
Pandas Cleaning - Data Cleaning in Pandas
Pandas MissingData - Handling Missing Data
Pandas Merging - Merging and Joining DataFrames
Pandas Grouping - Data Aggregation and Grouping
Pandas Pivot - Pivot Tables
Pandas TimeSeries - Time Series Data
Stats Intro - Introduction to Statistics
Stats Descriptive - Descriptive Statistics
Stats Inferential - Inferential Statistics
Stats Distributions - Probability Distributions
SciPy Intro - SciPy for Statistical Analysis
Statsmodels Intro - Statsmodels Library
Stats Hypothesis - Hypothesis Testing
Stats Regression - Regression Analysis
ML Intro - Introduction to Machine Learning
ML Supervised - Supervised Learning
ML Unsupervised - Unsupervised Learning
Sklearn Intro - scikit-learn Library
ML Preprocessing - Data Preprocessing
ML FeatureSelection - Feature Selection Techniques
ML CrossValidation - Cross-Validation Methods
ML Classification - Classification Algorithms
ML Regression - Regression Algorithms
ML Clustering - Clustering Algorithms
ML Evaluation - Model Evaluation and Validation
NetworkX Intro - NetworkX Library
NetworkX Create - Creating Graphs
NetworkX Algorithms - Graph Algorithms
NetworkX Visualize - Graph Visualization
Viz Importance - Importance of Data Visualization
Matplotlib Intro - Introduction to Matplotlib
Seaborn Intro - Introduction to Seaborn
Plotly Intro - Introduction to Plotly
Plotly Basic - Basic Plots with Plotly
Plotly Interactive - Interactive Visualizations
Plotly Dash - Plotly Dash for Web Apps
NLP Intro - Introduction to NLP
NLP TextPreprocessing - Text Preprocessing Techniques
NLP Tokenization - Tokenization Methods
NLP WordEmbeddings - Word Embeddings
NLTK Intro - NLTK Library
Spacy Intro - spaCy Library
NLP Sentiment - Sentiment Analysis
NLP TextClassify - Text Classification
VectorStores Intro - Introduction to Vector Stores
Faiss Intro - FAISS Library
Annoy Intro - Annoy Library
VectorStores Apps - Vector Store Applications
GenAI Intro - Introduction to Generative AI
LangChain Intro - LangChain Library
LangChain Pipelines - Creating Pipelines
LangChain COT - Chain-of-thought Prompting
LlamaIndex Intro - LlamaIndex Library
LlamaIndex Indexing - Indexing Data
LlamaIndex Querying - Querying Data
Project Ideas - Potential Final Projects Overview
Project DataAnalysis - Data Analysis Project
Project MLModel - Machine Learning Model Project
Project NLP - NLP Application Project
Project Visualization - Data Visualization Dashboard
Project GraphAnalysis - Graph Analysis Project
Project AIChatbot - AI-Powered Chatbot Project
Project Planning - Planning a Data Science Project
Project Dataset - Choosing Datasets
Project Teams - Team Collaboration
Project Presentation - Presenting Findings
Project Documentation - Project Documentation
Project Deployment - Deploying Projects
ML DeepLearning - Introduction to Deep Learning
TensorFlow Intro - TensorFlow Library
PyTorch Intro - PyTorch Library
DL NeuralNetworks - Neural Network Fundamentals
DL CNNs - Convolutional Neural Networks
DL RNNs - Recurrent Neural Networks
DL TransferLearning - Transfer Learning
DL NLP Transformers - Transformers Architecture
DL NLP BERT - BERT Model
TimeSeries Intro - Time Series Analysis
TimeSeries Decomposition - Time Series Decomposition
TimeSeries ARIMA - ARIMA Models
TimeSeries Forecasting - Forecasting Techniques
Recommender Intro - Recommender Systems
Recommender CollabFilter - Collaborative Filtering
Recommender ContentBased - Content-Based Filtering
Recommender Hybrid - Hybrid Recommenders
DataEng Intro - Introduction to Data Engineering
DataEng ETL - ETL Processes
DataEng DataWarehouses - Data Warehouses
DataEng DataLakes - Data Lakes
ML Reinforcement - Reinforcement Learning Basics
ML GANs - Generative Adversarial Networks
ML AutoML - Automated Machine Learning
ML Explainability - Model Explainability
BigData Intro - Introduction to Big Data
PySpark Intro - Introduction to PySpark
Cloud Intro - Cloud Computing Basics
AWS Intro - AWS Overview
Docker Intro - Introduction to Docker
Ethics AI - Ethical Considerations in AI
Privacy Data - Data Privacy and Security
Bias Fairness - Bias and Fairness in ML
Git Intro - Introduction to Git
GitHub Intro - Using GitHub
Viz BestPractices - Visualization Best Practices
Dask Intro - Dask for Parallel Computing
CaseStudies Intro - Case Studies Overview
Communication Skills - Communication Skills
CriticalThinking Skills - Critical Thinking
Leadership Skills - Leadership in Data Science
Final Review - Course Summary and Review
Next Steps - Next Steps in Learning
Course Evaluation - Course Feedback and Evaluation