Capacity Building Program for AI-Driven Climate Change Solutions

Description: This section introduces learners to Python programming essentials, covering syntax, control structures, functions, and basic data manipulation. It lays the foundation for data science by providing hands-on experience with Python libraries and Git for version control.

Learning Objectives: Master Python basics, understand key data structures, and manage code repositories using Git.

Prerequisite: Basic familiarity with programming concepts.

Week 1: Python Basics

Introduction to Python:

• Syntax, Data Types, and Operators

Control Structures:

• Loops and Conditional Statements

Functions and Data Structures:

• Functions: Definition, Parameters, and Returns
• Data Structures: Lists, Tuples, Dictionaries, and Sets

Week 2: Working with Data and Git

Working with Data:

• Reading/Writing Data: CSV, Excel, and JSON
• Basic Data Manipulation with Pandas

Introduction to Git:

• Git Commands: Clone, Commit, Push, and Pull
• Creating and Managing a GitHub Repository

Python Hands-On Project:

• Mini Project: Data Analysis and Visualization

Description: Learners will explore data collection techniques, including web scraping, and perform exploratory data analysis (EDA) to uncover patterns and insights. This section emphasizes the importance of data preprocessing and feature engineering.

Learning Objectives: Efficiently collect, preprocess, and analyze data to gain actionable insights.

Prerequisite: Python programming skills, and familiarity with data structures.

Week 3: Web Scraping and Data Collection

Web Scraping with Beautiful Soup:

• HTML Structure and Data Extraction
• Parsing and Collecting Static Web Data

Mini Project: Public Data Collection

Advanced Web Scraping with Scrapy and Splash:

• Scrapy Framework for Efficient Web Crawling
• Splash for Dynamic Content Scraping

Mini Project: Dynamic Data Scraping

Description: This section covers data visualization techniques and machine learning fundamentals, including supervised and advanced ML techniques. Learners will build, evaluate, and tune predictive models for climate-related applications.

Learning Objectives: Create informative visualizations and develop predictive models using machine learning.

Prerequisite: Experience with data analysis and Python programming.

Week 6: Data Visualization

Visualization Techniques:

• Using Matplotlib, Seaborn, and Plotly
• Creating Plots: Scatter, Line, Bar Charts, Histograms, and Heatmaps

Hands-on Project: Data Visualization for Climate Data

Week 7: Machine Learning Fundamentals

Supervised Learning:

• Regression and Classification Techniques

Model Evaluation and Tuning:

• Cross-Validation, Precision, Recall, and F1 Score

Hands-on Exercise: Building a Basic Predictive Model

Week 8: Advanced Machine Learning Techniques

Ensemble Methods:

• Random Forest and Gradient Boosting

Deep Learning Basics:

• Introduction to Neural Networks with TensorFlow/Keras

Project: Building an Advanced ML Model

Description: Learners dive into NLP techniques for text data analysis, exploring text preprocessing, sentiment analysis, and advanced NLP models. They will apply these techniques to climate data, generating valuable insights.

Learning Objectives: Analyze and extract insights from textual data using NLP techniques.

Prerequisite: Understanding of Python, data analysis, and basic ML concepts.

Week 9: Introduction to NLP

Text Preprocessing:

• Tokenization, Lemmatization, and Stemming

NLP Techniques:

• Sentiment Analysis and Topic Modeling

Hands-on Project: Basic NLP on Climate Data

Week 10: Advanced NLP Techniques

NLP Models:

• Word Embeddings, Named Entity Recognition, and Text Classification

Project: Advanced NLP Application for Climate Analysis

Description: This section focuses on developing interactive web applications using Streamlit and managing data science projects on GitHub. Learners will learn how to present their work effectively and collaborate on complex projects.

Learning Objectives: Build web applications for data projects and manage them using GitHub.

Prerequisite: Proficiency in Python, basic data visualization, and understanding of Git commands.

Week 11: Streamlit Application Development

Streamlit Basics:

• Building Interactive Web Applications
• Deploying Data Science Projects

Project: Creating a Streamlit App for Climate Data Visualization

Week 12: GitHub and Project Management

GitHub for Collaboration:

• Advanced Git Commands, Branching, and Pull Requests

Project Management Techniques: Issue Tracking, Task Management

Final Project: Managing a Complete Data Science Project with GitHub and Streamlit

Week 1: Introduction to Natural Language Processing

• Text Preprocessing: Tokenization, Stemming, and Lemmatization
• Sentiment Analysis and Text Classification
• Hands-on Exercise: Analyzing Climate-Related Text Data

Week 1: Building NLP Models

• Bag of Words and TF-IDF
• Implementing Word Embeddings with Word2Vec
• Mini Project: Topic Modeling on Climate Data

Week 2: Dashboard Development

• Designing Interactive Dashboards with Plotly and Dash
• Visualizing Textual Data and Model Outputs
• Hands-on Exercise: Building a Climate Data Dashboard

Week 2: Final Project

• End-to-End Project: Deploying a Climate Data Dashboard with NLP Insights

Week 3: Introduction to Credit Risk Scoring

• Financial Risk Assessment Fundamentals
• Data Collection and Preprocessing for Credit Scoring
• Mini Project: Data Preparation for Credit Risk Analysis

Week 3: Building Credit Scoring Models

• Logistic Regression for Binary Classification
• Decision Trees and Ensemble Methods for Risk Scoring
• Hands-on Exercise: Developing a Credit Risk Model

Week 4: Integrating Climate Data

• Climate Risk Factors Impacting SMEs
• Merging Financial and Climate Data for Comprehensive Scoring
• Project: Enhancing Credit Risk Models with Climate Data

Week 4: Final Project

• End-to-End Project: Deploying a Climate-Integrated Credit Scoring System