ECAS Institute Training Courses
Big Data, AI & Machine Learning, and Climate Modeling
Big Data in Environmental Applications
Course Background
The digital age has led to an explosion of environmental data from satellites, sensors, citizen science, and climate models. ECAS Institute’s introductory course provides foundational knowledge on Big Data concepts, tools, and applications in environmental science.
Objectives
Define Big Data and its 5 Vs within an environmental context.Identify common sources of environmental Big Data.Understand challenges and opportunities in environmental data analysis.Gain familiarity with Big Data processing and storage architectures.Recognize entry-level tools for data exploration and visualization.Modules
| No | Module | Details |
|---|
| 1 | Introduction to Big Data: The 5 Vs | Definition, characteristics, relevance, and ecosystem overview. |
| 2 | Sources of Environmental Big Data | Remote sensing, IoT sensors, GIS, citizen science, social media, climate models. |
| 3 | Challenges & Opportunities | Data quality, storage, governance, privacy, real-time monitoring, predictive modeling, case studies. |
| 4 | Big Data Processing & Storage | Distributed computing, cloud concepts, data lakes, data warehouses, relevant formats. |
| 5 | Big Data Analytics & Tools | Overview of analytics, machine learning concepts, Hadoop/Spark, visualization tools. |
| 6 | Case Studies & Future Trends | Climate change, biodiversity, smart cities, AI integration, ethical considerations. |
Artificial Intelligence (AI) & Machine Learning (ML) in Environmental ScienceCourse Background
AI and ML are revolutionizing environmental science by enabling predictive modeling, pattern recognition, and data-driven decision-making. This course bridges theory and practical application in environmental datasets.
Objectives
Understand AI/ML fundamentals and relevance to environment.Apply supervised, unsupervised, and deep learning algorithms.Use AI/ML tools for prediction, classification, and feature engineering.Evaluate model performance and interpret outputs.Explore real-world environmental applications and ethical considerations.Modules
| No | Module | Details |
|---|
| 1 | Introduction to AI, ML & Environmental Data | Definitions, workflow, datasets, and importance of AI/ML. |
| 2 | Supervised Learning | Regression, classification, key algorithms. |
| 3 | Unsupervised Learning & Beyond | Clustering, dimensionality reduction, ensemble methods, intro to deep learning. |
| 4 | Data Preprocessing & Feature Engineering | Data cleaning, transformation, geospatial processing. |
| 5 | Practical AI/ML Tools | Python environment, libraries, cloud-based platforms overview. |
| 6 | Evaluating Models | Metrics for regression & classification, overfitting, model interpretation. |
| 7 | Applications | Climate prediction, biodiversity, pollution monitoring, resource management. |
| 8 | Ethical Considerations & Future | Bias, privacy, accountability, AI for SDGs, emerging trends. |
Machine Learning for Climate Modeling and Prediction
Course Background
Machine Learning provides cutting-edge tools to analyze complex climate datasets and enhance predictions. This course equips professionals with practical ML skills to improve climate models and forecast extreme events.
Objectives
Understand ML concepts relevant to climate science.Apply supervised and unsupervised learning to climate datasets.Use deep learning models for complex climate phenomena.Evaluate strengths and limitations of ML in climate modeling.Develop predictive models for extreme events and climate trends.Modules
| No | Module | Details |
|---|
| 1 | Introduction to ML for Climate Science | AI/ML overview, challenges, paradigms. |
| 2 | Data Preprocessing & EDA | Climate data sources, cleaning, normalization, exploratory analysis. |
| 3 | Unsupervised Learning | Clustering, dimensionality reduction, anomaly detection. |
| 4 | Supervised Learning (Regression) | Predict continuous variables, time series forecasting, case studies. |
| 5 | Supervised Learning (Classification) | Predict categorical outcomes, logistic regression, decision trees, SVMs. |
| 6 | Deep Learning | Neural networks, CNNs for imagery, RNNs/LSTMs for time-series, physics-informed ML. |
| 7 | Model Evaluation & Interpretability | Advanced evaluation, Explainable AI, uncertainty quantification. |
| 8 | Applications & Future Directions | Renewable energy, environmental monitoring, climate policy, emerging trends. |
Contact & Registration
For registration or tailor-made courses, contact: info@ecasiafrica.org
Payment should be sent to our bank account before the training start date, with proof of payment sent to the same email.
About ECAS Institute
ECAS Institute delivers independent training, research, and consulting services focused on climate change, renewable energy, biodiversity conservation, agriculture, and food systems. Based in Nairobi, Kenya, ECAS operates across Africa and globally, partnering with organizations in the UK, Denmark, Italy, Sweden, Germany, and the USA.
