Module 1: Foundations of Environmental Data and R Setup

• Overview of common environmental data types (sensor data, laboratory results, remote sensing grids)
• Introduction to the R ecosystem and the RStudio IDE
• Installing and understanding the core Tidyverse packages for data science
• R fundamentals: Vectors, DataFrames, control flow, and functions
• Practical session: Setting up the RStudio environment and performing initial data loading and inspection of a water quality monitoring dataset using the readr and dplyr packages.

Module 2: Data Acquisition and Preprocessing with R

• Efficient data wrangling using dplyr verbs (mutate, filter, group_by, summarize)
• Handling missing or censored environmental data (imputation vs. removal, detection limits)
• Cleaning and standardizing observational data and metadata
• Data transformation techniques (log transformation, z-scoring) for statistical modeling
• Practical session: Cleaning a large air quality dataset, correcting anomalous readings, and standardizing pollutant measurements using dplyr and appropriate R functions.

Module 3: Geospatial Analysis of Environmental Data

• Introduction to spatial data in R using the sf (Simple Features) package
• Importing, plotting, and manipulating vector data (points, lines, polygons)
• Spatial joining and overlay analysis to integrate environmental samples with land use data
• Basic mapping and visualization of pollution hotspots using ggplot2 and tmap
• Practical session: Mapping the locations of noise pollution sensors, overlaying them onto a city boundary, and calculating the proximity of sensors to major roadways.

Module 4: Time Series Modeling for Environmental Monitoring

• Introduction to time series objects in R and handling datetime data
• Time series decomposition: Identifying trend, seasonality, and residual components
• Implementing Autoregressive Integrated Moving Average (ARIMA) models for forecasting
• Advanced time series analysis for high-frequency sensor data
• Practical session: Building and evaluating a SARIMA model to forecast daily average temperature or river flow rates, incorporating seasonal components.

Module 5: Statistical Analysis and Modeling in R

• Performing inferential statistics: Hypothesis testing (t-tests, ANOVA) for site comparison
• Implementing Linear Regression to model relationships between pollutants and drivers (e.g., traffic)
• Utilizing Generalized Linear Models (GLMs) for count data (e.g., species counts) or binary outcomes
• Interpretation of model coefficients, , and statistical significance in environmental context
• Practical session: Running a Multiple Linear Regression model to identify the meteorological and traffic variables that significantly influence localized  concentrations.

Module 6: Advanced Machine Learning for Environmental Prediction

• Introduction to supervised learning for classification and regression tasks
• Implementing Tree-Based methods (Random Forest, Gradient Boosting) for complex ecological modeling
• Feature engineering for robust environmental prediction (e.g., lagged variables, spatial features)
• Cross-validation and evaluation metrics for assessing model accuracy (RMSE, ROC-AUC)
• Practical session: Training a Random Forest classifier to predict habitat suitability for a specific endangered species based on land cover and climate variables.

Module 7: Introduction to Power BI for Environmental Reporting

• Connecting Power BI Desktop to various data sources (R scripts, CSV, Databases, Web APIs)
• Utilizing the Power Query Editor (M language) for visual data transformation and cleaning
• Introduction to the three views in Power BI: Report, Data, and Model
• Best practices for structuring environmental data for efficient reporting
• Practical session: Importing R-processed time series and spatial data into Power BI, cleaning and unpivoting tables using Power Query.

Module 8: Data Modeling and Advanced DAX for Metrics

• Establishing efficient data models: Fact and Dimension tables for environmental tracking
• Understanding and managing relationships between disparate datasets (e.g., monitoring stations, regulatory limits)
• Introduction to Data Analysis Expressions (DAX): Calculated columns vs. measures
• Implementing complex DAX measures for regulatory metrics (e.g., 90th percentile pollutant concentration)
• Practical session: Building a star schema model in Power BI and writing DAX measures to calculate rolling average pollutant levels and compare them to national standards.

Module 9: Creating Interactive Environmental Dashboards

• Principles of effective dashboard design for environmental data visualization (clarity, focus)
• Utilizing native Power BI visuals: Maps, Gauges, Line Charts, and custom visuals
• Implementing dynamic filters, slicers, and drill-through actions for site-specific analysis
• Integrating R visuals (plots generated in R) directly into the Power BI report
• Practical session: Designing a comprehensive, multi-page Power BI dashboard to monitor the compliance status and historical trends of multiple air quality monitoring stations.

Module 10: Environmental Data Governance and Stakeholder Communication

• Data documentation and metadata management standards for environmental projects
• Data security, anonymity, and ethical considerations in public-facing environmental data
• Principles of effective data storytelling: Translating analytical findings into clear policy or operational recommendations
• Using Power BI Service for report sharing, security, and deployment strategies
• Practical session: Creating a narrative summary and key recommendation slides based on the Power BI dashboard findings, focusing on clear communication for non-technical leadership.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588