1. Introduction to Python
· What is Python?
· Why Python for Text Analytics?
· Setting up Python Environment (e.g., Anaconda, Jupyter Notebook)

2. Python Basics
· Variables and Data Types
· Operators and Expressions
· Basic Input and Output

3. Control Flow
· Conditional Statements (if, elif, else)
· Loops (for and while)
· Break and Continue Statements

4. Collections and Data Structures
· Lists, Tuples, and Sets
· Dictionaries
· Working with Collections (accessing, updating, iterating)

5. Functions
· Defining and Calling Functions
· Parameters and Return Values
· Scope and Lifetime of Variables

6. File Handling
· Reading and Writing Files in Python
· Text File Operations

7. Introduction to Libraries
· Overview of Essential Libraries (e.g., NumPy, Pandas, Matplotlib)
· Installing and Importing Libraries
· Basic Usage of Pandas for Data Manipulation

8. Practice Exercises and Mini-Project (Data Collection on social media)
· Solving simple coding challenges
· Building a basic text data processing script

By the end of Day 1, participants will have a solid understanding of Python fundamentals and be well-prepared for Day 2, where they will delve into social media text analytics using the Python skills acquired during this introductory workshop.

1. Recap of Day 1
· Quick review of Python basics covered on Day 1
· Addressing any questions or clarifications from participants

2. Introduction to Social Media Text Analytics
· Understanding the significance of text analytics in social media data
· Types of data sources (tweets, posts, comments, etc.)
· Challenges and opportunities in social media text analysis

3. Preprocessing Text Data for Analysis
· Cleaning and handling text data (removing noise, handling special characters, etc.)
· Tokenization and text normalization
· Stopword removal and stemming/lemmatization

4. Sentiment Analysis
· Understanding sentiment analysis and its applications in social media
· Using pre-trained sentiment analysis models and libraries (e.g., NLTK, TextBlob)
· Building a simple sentiment analysis pipeline in Python

5. Topic Modeling
· Introduction to topic modeling techniques (e.g., Latent Dirichlet Allocation - LDA)
· Applying LDA to extract topics from social media text
· Visualizing and interpreting topic models with Python (e.g., using Gensim, pyLDAvis)

6. Named Entity Recognition (NER)
· Identifying and extracting named entities (e.g., people, organizations, locations) from text
· Utilizing spaCy library for NER in Python

7. Social Media Data Visualization
· Creating interactive visualizations of text analysis results
· Plotting word clouds, bar charts, and heatmaps using Matplotlib, Seaborn, or Plotly

8. Sentiment and Topic Analysis of Social Media Data
· Applying the learned techniques to analyze a sample dataset from social media
· Drawing insights and conclusions from the analyzed data

9. Q&A and Wrap-up
· Addressing participant questions and doubts
· Summarizing the key takeaways and next steps for participants' continued learning