Module 1: Introduction to Climate Change and the Need for Climate Finance

Module 2: Understanding Carbon Markets: Compliance Mechanisms

Module 3: Understanding Carbon Markets: Voluntary Mechanisms

Module 4: International Climate Finance Architecture

Module 5: Project Development and Financing for Climate Action

Module 6: Accessing Climate Finance: Proposal Writing and Requirements

Module 7: Carbon Market Project Development and Implementation

Module 8: Article 6 of the Paris Agreement and New Market Mechanisms

Module 9: Risk Management and Governance in Climate Finance and Carbon Markets

Module 10: The Role of the Private Sector and Future Trends in Climate Finance and Carbon Markets

Here's What You'll Learn:

Module 1: Introduction to Sustainable Agriculture and Its Principles

Module 2: Soil Health and Fertility Management for Sustainability

Module 3: Sustainable Water Use and Irrigation

Module 4: Integrated Pest and Disease Management (IPM) in Sustainable Systems

Module 5: Crop Diversification and Rotation

Module 6: Agroforestry and Permaculture Design

Module 7: Organic Farming Principles and Practices

Module 8: Sustainable Livestock Integration

Module 9: Farm Energy Efficiency and Renewable Resources

Module 10: Economic Viability and Certification in Sustainable Agriculture

Here's What You'll Learn:

• Introduction to Kobo Toolbox: Overview of mobile data collection, platform setup, basic form creation.
• Form Design: Effective design techniques, skip logic, validation rules, cascading selects.
• Data Management: Deployment, synchronization, export, and permissions management.
• Advanced Features: Multimedia questions, geopoints, external data, and advanced logic.
• Analysis & Visualization: Data cleaning, exporting, creating charts, and using external tools.
• Security & Ethics: Data privacy, security strategies, ethical considerations, compliance.
• Integration with External Tools: APIs, Google Sheets, GIS software, automation.
• Collaborative Projects: Team data collection and real-time monitoring.
• Project Management: Large-scale project handling, budgeting, stakeholder reporting.

Module 1: Introduction to Results-Based Monitoring and Evaluation

Module 2: Designing RBM&E Systems

Module 3: Data Collection Techniques

Module 4: Data Management and Storage

Module 5: Data Analysis and Interpretation

Module 6: Impact Evaluation

Module 7: Integrating Learning into Projects

Module 8: Using RBM&E Findings

Module 9: Addressing RBM&E Challenges

Module 10: Reporting and Communicating RBM&E Results

Here's What You'll Learn:

Module 1: Foundations of Environmental Science and Geospatial Technologies

Module 2: Remote Sensing Data Acquisition and Pre-processing for Environmental Applications

Module 3: GIS Data Models and Management for Environmental Data

Module 4: Land Cover and Land Use Mapping and Change Detection

Module 5: Water Resources Monitoring and Management

Module 6: Biodiversity and Ecosystem Health Assessment

Module 7: Environmental Pollution and Hazard Mapping

Module 8: Climate Change Impact Assessment and Adaptation

Module 9: Geospatial Modeling for Environmental Prediction

Module 10: Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA)

Module 11: Geospatial Analysis for Conservation Planning

Module 12: Soil and Land Degradation Monitoring

Module 13: Urban Environmental Analysis

Module 14: Integrating Field Data and Citizen Science

Module 15: Environmental Policy, Governance, and Reporting

Module 16: Communication and Visualization of Environmental Data

Module 17: Advanced Topics and Emerging Trends in Environmental Geospatial Science

Module 18: Capstone Project: Integrated Environmental Application

Here’s what you’ll learn:

Module 1: Advanced Logical Functions

Module 2: Dynamic Array Formulas

Module 3: Advanced Text Functions

Module 4: Date and Time Functions

Module 5: Lookup and Reference Functions

Module 6: Financial Functions

Module 7: Statistical Functions

Module 8: Data Validation and Auditing

Module 9: Power Query for Data Transformation

Module 10: PivotTables and PivotCharts

Module 11: Advanced Conditional Formatting

Module 12: Macros and VBA Basics

Module 13: Advanced VBA Programming

Module 14: Data Analysis ToolPak

Module 15: Power Pivot Advanced Data Modeling

Module 16: Advanced Charting Techniques

Module 17: Scenario Management and Goal Seek

Module 18: Advanced Data Import and Export

Here’s What You’ll Learn:

Module 1: The Financial Environment of Startups and SMEs

Module 2: Financial Planning and Budgeting

Module 3: Sources of Finance and Funding

Module 4: Financial Statements and Analysis

Module 5: Cash Flow Management

Module 6: Pricing Strategy and Revenue Management

Module 7: Risk Management and Internal Controls

Module 8: Valuation and Investor Relations

Module 9: Legal and Regulatory Compliance

Module 10: Capstone Project: Developing a Financial Plan

Here's What You'll Learn:

Module 1: Introduction to GIS and Public Health in Humanitarian Contexts

Module 2: Geospatial Data Collection and Management for Health Emergencies

Module 3: Core QGIS Functionalities for Public Health Analysis

Module 4: Spatial Epidemiology and Advanced Disease Mapping

Module 5: Environmental Health Risk Assessment and Mapping

Module 6: Healthcare Access, Resource Allocation, and Service Delivery Optimization

Module 7: Population Mapping, Vulnerability, and Displacement Analysis

Module 8: Introduction to Epi Info for Epidemiological Data Management

Module 9: Advanced Epi Info Data Analysis and Survey Design

Module 10: Integrating Epi Info and Other Health Data with QGIS

Module 11: Geocoding and Address Matching for Public Health Data

Module 12: Network Analysis for Health Logistics and Access

Module 13: Time Series and Spatio-Temporal Analysis for Outbreaks

Module 14: Automated Mapping and Reporting with QGIS

Module 15: Introduction to Remote Sensing for Public Health

Module 16: Data Quality, Ethics, and Privacy in Humanitarian GIS

Module 17: Web-Based GIS for Public Health Information Sharing

Module 18: Applied Project: End-to-End GIS for Public Health in Crisis

Course Outline

Here's What You'll Learn:

• Module 1: Understanding Stress
• Module 2: Identifying Your Stressors
• Module 3: Mind-Body Connection and Relaxation Techniques
• Module 4: Time Management and Productivity
• Module 5: Emotional Regulation and Resilience
• Module 6: Communication and Boundaries
• Module 7: Work-Life Integration
• Module 8: Building a Supportive Network
• Module 9: Nutrition, Exercise, and Sleep
• Module 10: Creating a Personal Stress Management Plan

Here's What You'll Learn:

Module 1: Introduction to MHPSS in Humanitarian Settings

Module 2: The IASC MHPSS Framework

Module 3: Basic Psychosocial Support Skills

Module 4: Needs Assessment and Programme Design

Module 5: Psychosocial Support Interventions

Module 6: Mental Health Interventions

Module 7: Case Management and Referral Pathways

Module 8: Community-Based MHPSS

Module 9: Self-Care and Staff Well-being

Module 10: Practical Application and Case Study

Here's What You'll Learn:

Module 1: Foundations and the Research Lifecycle

Module 2: Crafting Compelling Introductions and Literature Reviews

Module 3: Methodological Rigor and Reporting Standards

Module 4: Presenting Results and Discussion/Conclusion

Module 5: Effective Academic Style and Language

Module 6: Ethics, Integrity, and Authorship

Module 7: Selecting the Right Journal and Targeting Audience

Module 8: Manuscript Submission and the Peer Review Process

Module 9: Revising and Responding to Reviewer Comments

Module 10: Maximizing Visibility and Impact

Module 1: Introduction to Oil and Gas Project Management

Module 2: Project Planning and Scheduling

Module 3: Risk Management in Oil and Gas Projects

Module 4: Stakeholder Engagement and Communication

Module 5: Quality Management and Assurance

Module 6: Health, Safety, and Environment (HSE) Management

Module 7: Procurement and Contract Management

Module 8: Project Execution and Control

Module 9: Environmental Considerations in Oil and Gas Projects

Module 10: Project Closure and Lessons Learned

Here's What You'll Learn:

Module 1: Introduction to Aquaculture

Module 2: Site Selection and Pond Construction

Module 3: Fish Species Selection

Module 4: Water Quality Management

Module 5: Fish Nutrition and Feeding

Module 6: Fish Health Management

Module 7: Harvesting and Post-Harvest Handling

Module 8: Farm Management and Economics

Module 9: Sustainable Aquaculture Practices

Module 10: Current Trends and Innovations in Aquaculture in Kenya

Here's What You'll Learn:

Module 1: Understanding Climate Change and the Energy Landscape

Module 2: Solar Photovoltaic (PV) Technology

Module 3: Wind Energy Systems

Module 4: Hydropower and Geothermal Energy

Module 5: Bioenergy and Waste-to-Energy Technologies

Module 6: Energy Storage Solutions

Module 7: Grid Integration and Smart Grids

Module 8: Energy Efficiency and Conservation

Module 9: Climate Change Mitigation Policies and Strategies

Module 10: Project Development and Financing for Renewable Energy

Here's What You'll Learn:

Module 1: Introduction to Genomics and Bioinformatics

Module 2: R Programming Fundamentals for Bioinformatics

Module 3: Working with Genomic Data Formats in R

Module 4: Quality Control and Pre-processing of Genomic Data

Module 5: Differential Expression Analysis (RNA-Seq)

Module 6: Introduction to Variant Calling and Annotation

Module 7: Population Genetics and GWAS (Conceptual)

Module 8: Introduction to Epigenomic Data Analysis

Module 9: Pathway and Network Analysis

Module 10: Introduction to Data Visualization with Tableau

Module 11: Connecting Genomic Data to Tableau

Module 12: Building Basic Visualizations in Tableau

Module 13: Advanced Visualizations for Genomic Data in Tableau

Module 14: Creating Interactive Dashboards in Tableau

Module 15: Statistical Plotting and Interpretation in R

Module 16: Best Practices for Reproducible Genomic Analysis

Module 17: Case Studies in Genomic Data Analysis

Module 18: Project Work and Presentation

Here's What You'll Learn:

Module 1: Foundations of Outbreak Data and R Environment Setup

Module 2: Data Import, Cleaning, and Tidy Data Principles in R

Module 3: Descriptive Epidemiology and Measures in R

Module 4: Advanced Visualization for Outbreaks using ggplot2

Module 5: Introduction to Spatial Epidemiology and Mapping in R

Module 6: Time Series Analysis and Epidemic Curves using R

Module 7: Advanced Epidemiological Modeling with R

Module 8: Introduction to Power BI for Data Connection

Module 9: Data Transformation and M-Query in Power BI

Module 10: Data Modeling and Relationships in Power BI

Module 11: DAX Fundamentals for Public Health Metrics

Module 12: Creating Interactive Outbreak Dashboards in Power BI

Module 13: Geospatial Mapping and Visualization in Power BI

Module 14: Integrating R Visuals into Power BI

Module 15: Forecasting and Predictive Analytics in R

Module 16: Ethics, Data Security, and Confidentiality

Module 17: Automating Refresh and Dashboard Publication

Module 18: Final Capstone Project: End-to-End Analytics

Here's What You'll Learn:

Module 1: Business Combinations and Consolidated Financial Statements (IFRS 3 & IFRS 10)

Module 2: Financial Instruments - Recognition, Measurement, and Derecognition (IFRS 9)

Module 3: Financial Instruments - Presentation and Disclosures (IAS 32 & IFRS 7)

Module 4: Employee Benefits (IAS 19)

Module 5: Income Taxes (IAS 12)

Module 6: Provisions, Contingent Liabilities, and Contingent Assets (IAS 37)

Module 7: Leases (IFRS 16)

Module 8: Revenue from Contracts with Customers (IFRS 15)

Module 9: Investment Property (IAS 40) and Property, Plant and Equipment (IAS 16)

Module 10: Presentation of Financial Statements (IAS 1) and Accounting Policies, Changes in Accounting Estimates and Errors (IAS 8)

Here's What You'll Learn:

• Introduction to ODK: Basics of mobile data collection and ODK ecosystem setup.

Form Design: Create user-friendly, effective forms using XLSForm.

• Advanced Features: Implement skip logic, cascading selects, multimedia questions, and repeat groups.
• Deployment and Management: Use ODK Collect for deployment and manage collected data effectively.
• Data Analysis: Visualize and interpret data using ODK tools and external software.
• Security & Ethics: Ensure data privacy and ethical data collection practices.
• Integration: Use APIs and tools like GIS software and Google Sheets for real-time data integration.
• Collaborative Projects: Manage team projects with shared permissions and real-time monitoring.
• Project Management: Handle large-scale projects with strategic planning and reporting.

Here's What You'll Learn:

Module 1: Introduction to Proposal Writing in the Humanitarian and Development Sectors

Module 2: Needs Assessment and Problem Analysis

Module 3: Theory of Change and Logframe Development

Module 4: Designing Project Activities and Implementation Plans

Module 5: Budgeting and Resource Mobilization

Module 6: Writing the Technical Narrative (The Core Proposal)

Module 7: Monitoring, Evaluation, Accountability, and Learning (MEAL)

Module 8: Understanding Donor Requirements and Compliance

Module 9: Partnership Building and Stakeholder Analysis

Module 10: Review, Submission, and Follow-up Strategies

Here's What You'll Learn:

Module 1: Fundamentals of Climate Change and Risk

Module 2: Principles of Climate Risk Assessment

Module 3: Data Collection and Analysis for Vulnerability

Module 4: Assessing Exposure and Sensitivity

Module 5: Evaluating Adaptive Capacity

Module 6: Integrated Climate Risk and Vulnerability Mapping

Module 7: Sectoral Climate Risk Assessment

Module 8: Communicating Climate Risk and Engaging Stakeholders

Module 9: Mainstreaming Climate Risk into Planning

Module 10: Tools and Technologies for Climate Risk Analysis

Here's What You'll Learn:

Module 1: Foundations of Real-Time M&E

Module 2: Designing a Real-Time M&E Framework

Module 3: Developing Real-Time Key Performance Indicators (KPIs)

Module 4: Data Collection Tools & Digital Platforms for Real-Time M&E

Module 5: Mobile Data Collection and GIS Integration

Module 6: Data Visualization and Dashboard Design

Module 7: Data Analysis for Real-Time Decision Making

Module 8: Utilizing M&E Findings for Adaptive Management

Module 9: Theory of Change and Logic Models in a Dynamic Environment

Module 10: Real-Time Evaluation and Rapid Assessment Methods

Module 11: Feedback Loops and Community Engagement

Module 12: Managing Real-Time M&E Data for Security and Ethics

Module 13: Leading and Cultivating a Data-Driven Culture

Module 14: Cost-Effectiveness and Resource Tracking

Module 15: Integrating M&E with Strategic Planning

Module 16: Forecasting and Predictive Analytics

Module 17: Change Management for M&E System Implementation

Module 18: Capstone: Developing a Real-Time M&E System

Here's What You'll Learn:

Module 1: Introduction to Agricultural Water Resources and Hydrology

Module 2: Soil-Plant-Water Relationships

Module 3: Irrigation Water Quality and Its Management

Module 4: Surface Irrigation Systems Design and Management

Module 5: Sprinkler Irrigation Systems Design and Management

Module 6: Drip and Micro-Irrigation Systems Design and Management

Module 7: Irrigation Scheduling and Water Use Efficiency

Module 8: Drainage and Salinity Management in Irrigated Lands

Module 9: Water Harvesting and Conservation Techniques

Module 10: Economic and Environmental Aspects of Irrigation and Water Management

Here's What You'll Learn:

Module 1: Introduction to Mixed-Methods Philosophy and Rationale

Module 2: Core Mixed-Methods Designs (Typology)

Module 3: Convergent Parallel Design (Triangulation)

Module 4: Explanatory Sequential Design (QUAN  QUAL)

Module 5: Exploratory Sequential Design (QUAL  QUAN)

Module 6: Sampling in Mixed-Methods Research

Module 7: Data Integration and Mixing Strategies

Module 8: Validity and Rigor in Mixed-Methods

Module 9: Analysis of Mixed-Methods Data

Module 10: Writing and Disseminating Mixed-Methods Findings

Here's What You'll Learn:

Module 1: Introduction to Stata and Data Management

Module 2: Simple Linear Regression Model

Module 3: Multiple Linear Regression

Module 4: Hypothesis Testing and Confidence Intervals

Module 5: Violations of Ordinary Least Squares (OLS) Assumptions

Module 6: Panel Data Analysis: Fixed Effects Models

Module 7: Panel Data Analysis: Random Effects Models

Module 8: Instrumental Variables Estimation

Module 9: Introduction to Time Series Analysis

Module 10: Limited Dependent Variable Models

Here's What You'll Learn:

Module 1: Foundations of Data Mining and Python Setup

Module 2: Data Preprocessing and Feature Engineering

Module 3: Introduction to Classification (Decision Trees & k-NN)

Module 4: Advanced Classification Techniques (Ensemble Methods)

Module 5: Regression for Predictive Modeling

Module 6: Clustering Analysis (Unsupervised Learning)

Module 7: Association Rule Mining and Market Basket Analysis

Module 8: Text Mining and Sentiment Analysis

Module 9: Dimensionality Reduction and Evaluation Metrics

Module 10: Deployment and Ethical Considerations in Data Mining

Here's What You'll Learn:

Module 1: Foundations of Time Management

Module 2: Prioritization and Goal Setting

Module 3: Planning and Scheduling Techniques

Module 4: Overcoming Procrastination and Distractions

Module 5: Email and Digital Workflow Management

Module 6: Time Management for Meetings and Projects

Module 7: Delegation and Saying 'No'

Module 8: Managing Energy, Not Just Time

Module 9: Work-Life Integration and Boundaries

Module 10: Creating a Personal Time Management System

Here's What You'll Learn:

Module 1: Introduction to Research and the Research Process

Module 2: Formulating Research Questions and Hypotheses

Module 3: Quantitative Research Designs

Module 4: Qualitative Research Designs

Module 5: Mixed Methods Research

Module 6: Sampling Techniques

Module 7: Data Collection Methods

Module 8: Basics of Data Analysis (Quantitative)

Module 9: Basics of Data Analysis (Qualitative)

Module 10: Research Ethics and Writing Research Proposals/Reports

Here's What You'll Learn:

Module 1: Introduction to Clinical Research and Study Designs

Module 2: Ethical Considerations and Regulatory Frameworks in Clinical Research

Module 3: Principles of Clinical Trial Design

Module 4: Data Management in Clinical Research Using Stata

Module 5: Descriptive Statistics and Graphical Methods for Clinical Data in Stata

Module 6: Hypothesis Testing for Comparing Groups in Clinical Research Using Stata

Module 7: Linear Regression Analysis for Clinical Data Using Stata

Module 8: Logistic Regression Analysis for Clinical Data Using Stata

Module 9: Survival Analysis for Time-to-Event Data Using Stata

Module 10: Analysis of Correlated Data in Clinical Research Using Stata

Module 11: Handling Missing Data in Clinical Research Using Stata

Module 12: Interpreting Statistical Output and Drawing Conclusions in Clinical Research

Module 13: Meta-Analysis: Principles and Application in Stata

Module 14: Statistical Programming in Stata for Clinical Research: Introduction to Do-Files

Module 15: Advanced Regression Techniques in Clinical Research Using Stata

Module 16: Causal Inference in Clinical Research: Introduction to Potential Outcomes Framework

Module 17: Analysis of Clustered and Multilevel Data in Clinical Research Using Stata

Module 18: Reporting Statistical Results in Clinical Research Manuscripts and Presentations

Here's What You'll Learn:

Module 1: Foundations of High-Performing Teams

Module 2: Effective Team Communication

Module 3: Building Trust and Psychological Safety

Module 4: Conflict Resolution and Negotiation

Module 5: Defining Roles and Responsibilities

Module 6: Collaborative Problem-Solving and Decision-Making

Module 7: Leveraging Diversity for Innovation

Module 8: Giving and Receiving Constructive Feedback

Module 9: Building a Collaborative Culture

Module 10: Creating a Teamwork Action Plan

Here's What You'll Learn:

Module 1: Fundamentals of Food Safety and Quality

Module 2: Good Manufacturing Practices (GMP) and Good Hygiene Practices (GHP)

Module 3: Hazard Analysis and Critical Control Points (HACCP) System

Module 4: Food Safety Management Systems (FSMS) and ISO 22000

Module 5: Food Microbiology and Contaminants

Module 6: Food Allergens and Labeling Requirements

Module 7: Traceability and Recall Procedures

Module 8: Food Safety Auditing and Regulatory Compliance

Module 9: Quality Assurance Tools and Techniques

Module 10: Developing and Implementing a Food Safety and Quality Plan

Here's What You'll Learn:

Module 1: Introduction to Agricultural Marketing and Value Chains

Module 2: Market Analysis and Demand Assessment

Module 3: Post-Harvest Management and Quality Control

Module 4: Value Addition and Product Development

Module 5: Market Channels and Distribution Strategies

Module 6: Pricing Strategies and Market Information Systems

Module 7: Contract Farming and Producer Organizations

Module 8: Digital Marketing and E-commerce in Agriculture

Module 9: Food Safety Standards and Certifications

Module 10: Financing Agricultural Value Chains and Risk Management

Here's What You'll Learn:

Module 1: Introduction to Nutrition and Food Security

Module 2: Understanding the Nutrition-Sensitive Agriculture Framework

Module 3: Diversified Food Production for Improved Nutrition

Module 4: Post-Harvest Management and Food Safety

Module 5: Water, Sanitation, and Hygiene (WASH) in Nutrition-Sensitive Agriculture

Module 6: Gender Equity and Women's Empowerment in Agriculture

Module 7: Social and Behavior Change Communication for Nutrition

Module 8: Policy and Governance for Nutrition-Sensitive Agriculture

Module 9: Monitoring, Evaluation, and Learning in NSA Programs

Module 10: Designing and Implementing Nutrition-Sensitive Agriculture Interventions

Here's What You'll Learn:

Module 1: Foundational Data Concepts and Stata Interface

Module 2: Advanced Quantitative Data Manipulation in Stata

Module 3: Descriptive Statistics and Data Visualization in Stata

Module 4: Inferential Statistical Analysis using Stata

Module 5: Introduction to Qualitative Data Analysis and NVivo

Module 6: Preparing and Importing Qualitative Data into NVivo

Module 7: Systematic Coding and Node Management in NVivo

Module 8: Exploring Themes and Patterns with NVivo Queries

Module 9: Integrating Quantitative and Qualitative Findings (Mixed Methods)

Module 10: Reporting, Dissemination, and Ethical Data Practices

Here's What You'll Learn:

Module 1: Foundations of Data Protection and the 3-2-1 Rule

Module 2: Backup Strategies and Implementation Methods

Module 3: Storage Technologies for Backup and Recovery

Module 4: Disaster Recovery Planning (DRP) and Business Continuity

Module 5: Data Recovery Procedures and Testing

Module 6: Data Archiving, Retention, and Compliance

Module 7: Cloud-Based Backup and Recovery Solutions

Module 8: Security and Encryption in Backup Processes

Module 9: Backup System Management and Monitoring

Module 10: Advanced Recovery Scenarios and Automation

Here's What You'll Learn:

Module 1: Foundations of Market Research and the Consumer Landscape

Module 2: Secondary Data and Market Sizing

Module 3: Qualitative Research Methods

Module 4: Fundamentals of Quantitative Research Design

Module 5: Descriptive Statistics and Data Visualization for Insights

Module 6: Hypothesis Testing and Statistical Inference in Market Research

Module 7: Segmentation, Targeting, and Positioning (STP) Analysis

Module 8: Regression and Predictive Modeling

Module 9: Advanced Measurement and Scaling Techniques

Module 10: Reporting, Storytelling, and Actionable Insights

Here's What You'll Learn:

Module 1: Introduction to Post-Harvest Management and Food Processing

Module 2: Principles of Post-Harvest Handling and Storage

Module 3: Food Preservation Technologies

Module 4: Food Quality Control and Safety

Module 5: Processing of Grains and Cereals

Module 6: Processing of Fruits and Vegetables

Module 7: Processing of Dairy and Meat Products

Module 8: Packaging and Labeling of Food Products

Module 9: Food Waste Reduction and Value Addition

Module 10: Food Business Development and Regulations

Here's What You'll Learn:

Module 1: Foundations of Culture and Communication

Module 2: Cultural Dimensions and Models

Module 3: Verbal Communication Across Cultures

Module 4: Non-Verbal Communication and Cultural Cues

Module 5: Navigating Cultural Differences in Business and Workplaces

Module 6: Building Intercultural Relationships and Trust

Module 7: Managing Conflict in Cross-Cultural Settings

Module 8: Cross-Cultural Negotiation and Decision-Making

Module 9: Adapting Communication Styles for Global Success

Module 10: Developing Intercultural Competence and Continuous Learning

Here's What You'll Learn:

 Module 1: Financial Data Analytics in FP&A

 Module 2: SQL Fundamentals: Retrieval and Filtering

 Module 3: SQL Aggregation and Grouping

 Module 4: Joining Financial Datasets

 Module 5: Advanced SQL Set Operations and Subqueries

 Module 6: Common Table Expressions (CTEs) for Multi-Step Logic

 Module 7: Window Functions for Financial Metrics

 Module 8: SQL for Data Cleansing and Transformation

 Module 9: Direct Database Connection and Excel Integration

 Module 10: Mastering Power Query for Data Transformation (ETL)

 Module 11: Advanced Power Query M Language and Custom Functions

 Module 12: Introduction to Data Modeling with Power Pivot

 Module 13: Dynamic Array Formulas for Modern Analysis

 Module 14: Advanced Lookup and Conditional Functions

 Module 15: Scenario and Sensitivity Analysis Tools

 Module 16: Time Value of Money (TVM) and Valuation Functions

 Module 17: Interactive Dashboard Design and Visualization

 Module 18: Automation, VBA, and Strategic Data Storytelling

Here's What You'll Learn:

Module 1: Foundations of Negotiation

Module 2: The Preparation Phase

Module 3: Communication and Psychology in Negotiation

Module 4: Dealing with Difficult Situations

Module 5: Advanced Strategies and Tactics

Module 6: Building Relationships and Trust

Module 7: Cross-Cultural Negotiation

Module 8: Negotiating in Teams

Module 9: Ethics and Legal Aspects of Negotiation

Module 10: Creating a Personal Negotiation Plan

Here's What You'll Learn:

Module 1: Foundational Data Ecosystems in Oil and Gas

Module 2: Python Fundamentals for Data Ingestion and Wrangling

Module 3: Specialized Data Handling (Time-Series, Well Logs, Seismic)

Module 4: Data Quality, Cleaning, and Feature Engineering with Pandas

Module 5: Automated Reporting and Scripting with Python

Module 6: PowerBI Architecture and Advanced Data Modeling

Module 7: Mastering Data Analysis Expressions (DAX)

Module 8: Geospatial and Visual Analytics for Petroleum Data

Module 9: Designing Interactive and Actionable Business Dashboards

Module 10: Deployment, Security, and Governance

Here's What You'll Learn:

Module 1: Foundations of Fraud Risk and Power BI Setup

Module 2: Data Modeling and ETL for Fraud Scenarios

Module 3: Core DAX for Financial Risk and Anomaly Metrics

Module 4: Visualization of Fraud Patterns and Red Flags

Module 5: Analytical Techniques for Transactional Fraud Detection

Module 6: Vendor, Procurement, and Accounts Payable Fraud Analysis

Module 7: Employee and Expense Report Fraud Analytics

Module 8: Advanced Analytics and AI Visuals for Predictive Fraud

Module 9: Reporting Governance and Secure Dashboard Deployment

Module 10: Integrated Fraud Detection Dashboard Capstone

Here's What You'll Learn:

Module 1: Understanding Climate Change Impacts and Adaptation

Module 2: Vulnerability and Risk Assessment

Module 3: Ecosystem-Based Adaptation (EbA)

Module 4: Infrastructure and Technology-Based Adaptation

Module 5: Policy, Governance, and Legal Frameworks for Adaptation

Module 6: Climate-Resilient Agriculture and Food Security

Module 7: Water Resources Management in a Changing Climate

Module 8: Health and Human Settlements Adaptation

Module 9: Climate Finance for Adaptation

Module 10: Developing and Implementing Adaptation Plans

Here's What You'll Learn:

Module 1: Introduction to Evidence Synthesis

Module 2: Formulating the Research Question (PICO/PICOS)

Module 3: Developing a Comprehensive Search Strategy

Module 4: Study Selection and Data Extraction

Module 5: Assessing Risk of Bias in Included Studies

Module 6: Introduction to Meta-Analysis

Module 7: Performing Meta-Analysis (Dichotomous and Continuous Data)

Module 8: Heterogeneity and Sensitivity Analysis

Module 9: Reporting and Disseminating Systematic Reviews and Meta-Analyses

Module 10: Advanced Topics and Software Tools

Here's What You'll Learn:

Module 1: Foundations of Emotional Intelligence

Module 2: Self-Awareness: The First Pillar

Module 3: Self-Regulation: Managing Your Emotions

Module 4: Social Awareness: Empathy and Perspective

Module 5: Relationship Management: The Art of Connection

Module 6: Emotional Intelligence in the Workplace

Module 7: Conflict Resolution and Difficult Conversations

Module 8: Leadership and Emotional Intelligence

Module 9: Building Emotional Resilience

Module 10: Creating Your Personal EQ Action Plan

Here's What You'll Learn:

Module 1: Introduction to Climate Finance and Global Climate Frameworks

Module 2: Key Sources and Instruments of Climate Finance

Module 3: Project Design for Climate Finance

Module 4: Developing Bankable Climate Projects

Module 5: Accessing Multilateral Climate Funds

Module 6: Bilateral and Philanthropic Climate Funding

Module 7: Private Sector Engagement in Climate Finance

Module 8: Monitoring, Reporting, and Verification (MRV) in Climate Projects

Module 9: Climate Finance Readiness and Proposal Writing

Module 10: Case Studies and Emerging Trends in Climate Finance

Here's What You'll Learn:

Module 1: Introduction to Big Data and Hadoop Ecosystem

Module 2: Hadoop Distributed File System (HDFS)

Module 3: Hadoop MapReduce Framework

Module 4: Introduction to Apache Spark

Module 5: Spark Core and Resilient Distributed Datasets (RDDs)

Module 6: Spark SQL and DataFrames

Module 7: Spark Structured Streaming

Module 8: Spark Machine Learning Library (MLlib)

Module 9: Graph Analytics with Spark GraphX

Module 10: Real-world Big Data Architectures

Module 11: Data Ingestion with Apache Sqoop and Flume

Module 12: Messaging and Streaming with Apache Kafka

Module 13: Data Warehousing with Hive and Impala

Module 14: NoSQL Databases in the Big Data Landscape

Module 15: Big Data Security and Governance

Module 16: Cluster Management with YARN

Module 17: Performance Tuning and Optimization

Module 18: End-to-End Big Data Analytics Project

Here's What You'll Learn:

Module 1: Foundations of Marketing Analytics

Module 2: Introduction to R for Data Analysis

Module 3: Data Preparation and Visualization in R

Module 4: Descriptive Marketing Analytics

Module 5: Customer Segmentation and Clustering (R)

Module 6: Predictive Modeling for Marketing (R)

Module 7: Introduction to Tableau for Visualization

Module 8: Connecting Data and Building Dashboards in Tableau

Module 9: Marketing Mix Modeling (R)

Module 10: Web and Social Media Analytics (R)

Module 11: A/B Testing and Experimentation Analysis (R)

Module 12: Customer Lifetime Value (CLV) Analysis (R)

Module 13: Visualizing Campaign Performance in Tableau

Module 14: Advanced Dashboarding Techniques in Tableau

Module 15: Reporting and Storytelling with Data

Module 16: Market Basket Analysis and Recommendation Systems (R)

Module 17: Geospatial Marketing Analysis

Module 18: Course Capstone Project and Next Steps

Here's What You'll Learn:

Module 1: Introduction to Longitudinal, Panel, and Time Series Data

Module 2: Stata Fundamentals for Time-Series Data

Module 3: Data Management for Longitudinal and Panel Data

Module 4: Descriptive Analysis of Longitudinal and Panel Data

Module 5: Introduction to Linear Panel Data Models

Module 6: Fixed Effects (FE) Model

Module 7: Random Effects (RE) Model

Module 8: Choosing Between Fixed and Random Effects

Module 9: Dynamic Panel Data Models

Module 10: Introduction to Time Series Analysis

Module 11: Stationarity and Unit Root Tests

Module 12: Autoregressive (AR) and Moving Average (MA) Models

Module 13: Autoregressive Integrated Moving Average (ARIMA) Models

Module 14: Vector Autoregression (VAR) Models

Module 15: Cointegration and Error Correction Models

Module 16: Advanced Topics in Panel Data

Module 17: Advanced Topics in Time Series

Module 18: Reporting and Interpretation of Results

Here's What You'll Learn:

Module 1: Foundations of Epidemiology and Public Health Data

Module 2: Python Environment Setup and Data Handling with Pandas

Module 3: Core Epidemiological Measures and Descriptive Analytics

Module 4: Inferential Epidemiology: Risk, Association, and Hypothesis Testing

Module 5: Data Cleaning and Validation for Epidemiological Datasets

Module 6: Data Visualization for Epidemiology using Matplotlib and Seaborn

Module 7: Introduction to Time-Series Analysis and Epidemic Curves

Module 8: Introduction to Power BI: Connecting and Transforming Health Data

Module 9: Data Modeling and DAX for Public Health Metrics in Power BI

Module 10: Building Interactive Epidemiological Dashboards

Module 11: Regression Modeling in Epidemiology (Linear and Logistic)

Module 12: Survival Analysis Fundamentals and Kaplan-Meier Curves

Module 13: Spatial Epidemiology and Data Mapping with Python

Module 14: Bias, Confounding, and Effect Modification (Statistical Adjustment)

Module 15: Advanced Data Storytelling and Power BI Custom Visuals

Module 16: Public Health Surveillance Systems and Data Pipelines

Module 17: Machine Learning Applications in Disease Prediction

Module 18: Capstone Project: End-to-End Outbreak Investigation Dashboard

Here's What You'll Learn:

Module 1: Understanding Climate Change and Its Impacts

Module 2: The Food Security Framework in a Changing Climate

Module 3: Climate Change Impacts on Agricultural Production

Module 4: Climate Change Impacts on Food Access and Utilization

Module 5: Climate-Smart Agriculture (CSA) Principles and Practices

Module 6: Water Management and Irrigation for Climate Resilience

Module 7: Building Resilient Food Systems and Supply Chains

Module 8: Policy and Governance for Climate-Resilient Food Security

Module 9: Climate Finance and Investment in Food Security Adaptation

Here's What You'll Learn:

• Survey Data Basics: Overview, SPSS introduction, data import, descriptive statistics.
• Data Cleaning: Handling missing data, transformation, validation, data file management.
• Descriptive Statistics & Visualization: Measures of central tendency and dispersion, frequency distributions, data visualization.
• Inferential Statistics: Hypothesis testing, t-tests, ANOVA, Chi-square tests, non-parametric tests.
• Correlation & Regression: Correlation analysis, simple and multiple linear regression, assumptions.
• Advanced Techniques: Factor analysis, cluster analysis, discriminant analysis, logistic regression.
• Data Interpretation & Presentation: Interpreting results, creating tables and figures, writing reports, presentations.
• Survey Weighting: Introduction, applying weights, adjusting for bias, analyzing weighted data.
• Data Quality Assessment: Assessing quality, handling outliers, reliability and validity, ethical considerations.
• Reporting & Dissemination: Writing reports, communicating results, using visuals, ethical reporting.

Module 1: Introduction to Livestock Management and Health

Module 2: Animal Anatomy, Physiology, and Nutrition

Module 3: Livestock Breeding Systems and Genetic Improvement

Module 4: Common Livestock Diseases in Kenya

Module 5: Livestock Disease Prevention and Biosecurity

Module 6: Livestock Feeding and Ration Formulation

Module 7: Reproductive Management in Livestock

Module 8: Housing and Handling of Livestock

Module 9: Livestock Production Economics and Marketing

Module 10: Modern Technologies in Livestock Management

Here's What You'll Learn:

Module 1: Understanding Climate Change and Disaster Risk

Module 2: The Disaster Risk Reduction Framework

Module 3: Vulnerability and Risk Assessment

Module 4: Climate Change Adaptation Strategies

Module 5: Ecosystem-Based Disaster Risk Reduction (Eco-DRR)

Module 6: Early Warning Systems and Communication for DRR

Module 7: Climate-Resilient Infrastructure and Planning

Module 8: Financing Climate Change Adaptation and DRR

Module 9: Governance and Policy for Climate and Disaster Resilience

Module 10: Integrating Climate Change and DRR for Resilient Development

Here's What You'll Learn:

Module 1: Foundations of Interpersonal Skills

Module 2: Active Listening and Empathy

Module 3: Effective Communication Strategies

Module 4: Conflict Resolution and Negotiation

Module 5: Building Rapport and Trust

Module 6: Managing Emotions and Self-Awareness

Module 7: Assertiveness vs. Aggression

Module 8: Giving and Receiving Feedback

Module 9: Collaboration and Teamwork

Module 10: Creating a Personal Interpersonal Development Plan

Here's What You'll Learn:

Module 1: Foundations and Philosophical Paradigms in Qualitative Research

Module 2: Qualitative Research Design, Ethics, and Planning

Module 3: In-Depth Interviewing and Elicitation Techniques

Module 4: Focus Group Discussions (FGDs) and Group Dynamics

Module 5: Ethnography, Observation, and Fieldwork Methods

Module 6: Purposeful Sampling Strategies and Participant Selection

Module 7: Data Transcription, Memoing, and Software Preparation

Module 8: Thematic Analysis (TA) in Practice

Module 9: Advanced Qualitative Analysis Methodologies

Module 10: Ensuring Trustworthiness and Reporting Findings

Here's What You'll Learn:

Module 1: Foundations of Industrial Data and Python Environment Setup

Module 2: Python Fundamentals for Industrial Data Processing (Pandas)

Module 3: Time Series Analysis of Sensor Data (Resampling and Filtering)

Module 4: Advanced Data Preprocessing and Feature Engineering

Module 5: Descriptive and Exploratory Data Analysis in Python

Module 6: Statistical Process Control (SPC) and Anomaly Detection

Module 7: Predictive Maintenance: Classification Models in Python

Module 8: Regression Models for Process Optimization

Module 9: Introduction to Power BI for Data Connection

Module 10: Data Transformation and M-Query for Industrial Data

Module 11: Data Modeling for Manufacturing and IoT Datasets

Module 12: DAX Fundamentals for Calculating Operational Metrics

Module 13: Creating Interactive Performance Dashboards in Power BI

Module 14: Integrating Python Visuals and Scripts into Power BI

Module 15: Root Cause Analysis and Drill-Through Reporting

Module 16: Data Security and Governance in Industrial Analytics

Module 17: Automating Data Pipelines and Dashboard Deployment

Module 18: Final Capstone Project: End-to-End Industrial Analytics

Here's What You'll Learn:

Module 1: Introduction to Permaculture Ethics and Principles

Module 2: Understanding Natural Systems and Ecological Literacy

Module 3: Permaculture Design Process and Tools

Module 4: Water Management and Earthworks

Module 5: Soil Building and Fertility Strategies

Module 6: Guilds, Stacking, and Forest Gardens

Module 7: Integrated Animal Systems in Permaculture

Module 8: Appropriate Technology and Renewable Energy

Module 9: Designing for Social and Economic Permaculture

Module 10: Developing Your Permaculture Design Project

Here's What You'll Learn:

Module 1: Foundations of Academic Writing and Research

Module 2: Structuring Your Academic Paper

Module 3: Critical Reading and Literature Review

Module 4: Citation and Referencing Styles

Module 5: Data Presentation and Visualization

Module 6: Advanced Academic Style and Tone

Module 7: The Research Publication Process

Module 8: Peer Review and Revision Strategies

Module 9: Grant Proposal and Research Funding

Module 10: Dissemination and Professional Development

Here's What You'll Learn:

Module 1: Foundational Research Skills and Topic Selection

Module 2: The Literature Review Strategy and Theoretical Framework

Module 3: Developing the Research Problem, Questions, and Objectives

Module 4: Research Design and Methodology Selection

Module 5: Data Collection Instruments and Pilot Testing

Module 6: Ethical Clearance and Proposal Writing Mechanics

Module 7: Data Analysis Planning and Software Preparation

Module 8: Thesis Structure, Drafting, and Chapter Development

Module 9: Academic Writing, Citation, and Plagiarism Management

Module 10: Thesis Defense Preparation and Submission

Here's What You'll Learn:

Module 1: STATA Programming and Data Management Fundamentals

Module 2: Advanced Data Reshaping and Cleaning Techniques

Module 3: Mastering Linear Regression Diagnostics and Extensions

Module 4: Generalized Linear Models (GLM) and Non-Linear Regression

Module 5: Time Series Analysis and Forecasting in STATA

Module 6: Panel Data Econometrics: Fixed and Random Effects

Module 7: Instrumental Variables (IV) and Treatment Effects Modeling

Module 8: Survival Analysis and Event History Modeling

Module 9: Survey Data Analysis and Weighting Methods

Module 10: Automation, Custom Reports, and Reproducible Research

Here's What You'll Learn:

• Module 1: Introduction to CRM Data Analytics
• Module 2: Data Visualization with PowerBI
• Module 3: Advanced Analytics Techniques
• Module 4: Marketing Performance Measurement
• Module 5: Customer Segmentation and Targeting
• Module 6: Data-Driven Decision Making
• Module 7: CRM Data Integration
• Module 8: PowerBI Best Practices
• Module 9: Case Studies and Real-Life Applications
• Module 10: Future Trends in CRM Data Analytics

Here's What You'll Learn:

Module 1: Introduction to Climate Change and Climate-Smart Agriculture

Module 2: Climate-Smart Crop Production Practices

Module 3: Climate-Smart Livestock Management

Module 4: Sustainable Water Management in Agriculture

Module 5: Soil Health Management for Climate Resilience

Module 6: Agroforestry and Diversified Farming Systems

Module 7: Climate Information Services for Farmers

Module 8: Risk Management and Climate Change Adaptation Strategies

Module 9: Policy and Institutional Frameworks for CSA

Module 10: Economic Benefits and Scaling Up Climate-Smart Agriculture

Here's What You'll Learn:

Module 1: Foundations of Research Project Management

Module 2: Research Project Planning and Scheduling

Module 3: Budgeting and Financial Management

Module 4: Team Leadership and Human Resource Management

Module 5: Data Management and Quality Control

Module 6: Monitoring, Evaluation and Reporting

Module 7: Ethics, Compliance and Risk Management

Module 8: Communication and Stakeholder Engagement

Module 9: Agile and Adaptive Research Approaches

Module 10: Project Closure and Dissemination

Here's What You'll Learn:

Module 1: Introduction to Food Security Concepts

Module 2: The Four Pillars of Food Security

Module 3: Measuring and Monitoring Food Insecurity

Module 4: Drivers of Food Insecurity

Module 5: Food Systems Approach to Food Security

Module 6: Policies and Governance for Food Security

Module 7: Climate Change and Food Security

Module 8: Emergency Food Security and Humanitarian Response

Module 9: Sustainable Food Production Systems

Module 10: Developing Food Security Interventions and Strategies

Here's What You'll Learn:

Module 1: Foundations of Active Listening

Module 2: Non-Verbal Communication

Module 3: Verbal Cues and Effective Questioning

Module 4: Overcoming Barriers to Listening

Module 5: Empathy and Perspective-Taking

Module 6: Listening in Different Contexts

Module 7: Advanced Active Listening Techniques

Module 8: Active Listening for Conflict Resolution

Module 9: Mindfulness and Active Listening

Module 10: Developing a Personal Listening Plan

Here's What You'll Learn:

Module 1: Introduction to Epidemic Preparedness

Module 2: Data Management with R

Module 3: Descriptive Statistics in R

Module 4: Inferential Statistics in R

Module 5: Data Visualization with R

Module 6: Introduction to Tableau

Module 7: Advanced Tableau Visualizations

Module 8: Integrating R and Tableau

Module 9: Time Series Analysis in R

Module 10: Geospatial Analysis in Tableau

Module 11: Machine Learning Basics with R

Module 12: Predictive Modeling in R

Module 13: Advanced Analytics with R

Module 14: Tableau Server and Online

Module 15: Data Storytelling with Tableau

Module 16: Real-world Case Studies

Module 17: Best Practices in Data Analysis

Module 18: Course Wrap-up and Review

Here's What You'll Learn:

Module 1: Foundations of Literature Review and Research Questions

Module 2: Developing Comprehensive Search Strategies

Module 3: Database Selection and Search Execution

Module 4: Reference Management and Screening Tools

Module 5: Critical Appraisal and Risk of Bias Assessment

Module 6: Data Extraction and Coding for Synthesis

Module 7: Narrative Synthesis and Thematic Grouping

Module 8: Introduction to Meta-Analysis Principles

Module 9: Reporting Standards and Dissemination

Module 10: Writing, Reviewing, and Utilizing the Review

Here's What You'll Learn:

Module 1: Principles of Clear and Effective Scientific Writing

Module 2: Structuring Different Types of Scientific Manuscripts

Module 3: Writing Compelling Introductions and Methods

Module 4: Presenting Results Effectively

Module 5: Writing Insightful Discussions and Conclusions

Module 6: Journal Selection and the Submission Process

Module 7: Navigating Peer Review and Responding to Reviewers

Module 8: Ethical Considerations in Scientific Publication

Module 9: Writing Impactful Abstracts and Cover Letters

Module 10: Enhancing Publication Impact and Visibility

Here's What You'll Learn:

Module 1: Understanding Climate Change and Greenhouse Gases

Module 2: Energy Efficiency and Conservation

Module 3: Renewable Energy Technologies

Module 4: Sustainable Transport and Urban Planning

Module 5: Industrial Emissions Reduction

Module 6: Waste Management and Circular Economy

Module 7: Carbon Sequestration and Land Use

Module 8: Policy, Regulations, and Carbon Markets

Module 9: Green Building and Infrastructure

Module 10: Developing and Implementing GHG Reduction Strategies

Here's What You'll Learn:

Module 1: Python Fundamentals for Biomedical Data

Module 2: Data Wrangling with Pandas and NumPy

Module 3: Biostatistics and Inferential Analysis

Module 4: Clinical Data Processing and EHR Analysis

Module 5: Introduction to Genomics and Bioinformatics Data

Module 6: Statistical Modeling for Healthcare Outcomes

Module 7: Machine Learning for Diagnostics and Prognostics

Module 8: Survival Analysis and Time-to-Event Modeling

Module 9: Data Visualization with Matplotlib and Seaborn

Module 10: Geospatial and Epidemiology Data Analysis

Module 11: Power BI Desktop Fundamentals and Data Loading

Module 12: Data Transformation with Power Query (M Language)

Module 13: Designing Robust Data Models in Power BI

Module 14: Introduction to DAX (Data Analysis Expressions)

Module 15: Advanced Power BI Visualizations for Healthcare

Module 16: Creating Interactive Dashboards for Healthcare Performance

Module 17: Integrating Python Scripts and Models into Power BI

Module 18: Ethical AI, Data Governance, and Deployment

Here's What You'll Learn:

Module 1: Introduction to Qualitative Research & NVivo

Module 2: Setting up Your NVivo Project

Module 3: Importing and Organizing Data

Module 4: Initial Coding and Node Management

Module 5: Advanced Coding Techniques

Module 6: Working with Cases and Classifications

Module 7: Queries and Exploring Your Data

Module 8: Visualizing and Presenting Findings

Module 9: Collaborative Projects and Data Security

Module 10: Advanced Features and Mixed Methods

Here's What You'll Learn:

Module 1: Introduction to Epidemiological Data Science

Module 2: Python Fundamentals for Public Health Data

Module 3: Epidemiological Data Acquisition and Cleaning

Module 4: Visualization and Exploratory Data Analysis (EDA)

Module 5: Classic Compartmental Modeling (SIR/SEIR)

Module 6: Time Series Forecasting for Incidence Rates

Module 7: Machine Learning for Outbreak Prediction

Module 8: Geospatial Epidemiology and Spatial Analysis

Module 9: Model Validation, Uncertainty, and Interpretation

Module 10: Communicating Forecasts and Policy Integration

Here's What You'll Learn:

Module 1: Foundations of Quality Management and Variation

Module 2: Fundamentals of Control Charts

Module 3: Control Charts for Variable Data (I-MR, X̄-R)

Module 4: Control Charts for Variable Data (X̄-S and Advanced Techniques)

Module 5: Control Charts for Attribute Data (p and np Charts)

Module 6: Control Charts for Attribute Data (c and u Charts)

Module 7: Process Capability Analysis (Cₚ, Cₚₖ)

Module 8: Advanced Process Monitoring: Cusum and EWMA Charts

Module 9: Pre-Control and Short Run SPC Methods

Module 10: SPC Implementation, Management, and Reporting

Here's What You'll Learn:

Module 1: Introduction to Predictive Modeling with R
Module 2: Linear Regression
Module 3: Logistic Regression
Module 4: Decision Trees
Module 5: Random Forests
Module 6: Support Vector Machines
Module 7: Clustering Techniques
Module 8: Time Series Analysis
Module 9: Ensemble Methods
Module 10: Model Deployment

Here’s what you’ll learn:

Module 1: Introduction to Customer Analytics

Module 2: Data Visualization Basics

Module 3: Advanced Data Analysis Techniques

Module 4: Dashboard Design and Development

Module 5: Customer Segmentation and Profiling

Module 6: Predictive Customer Analytics

Module 7: Marketing Analytics

Module 8: Customer Lifetime Value Analysis

Module 9: Customer Satisfaction and Feedback Analysis

Module 10: Future Trends in Customer Analytics

Here's What You'll Learn:

Module 1: Understanding the Grant Landscape

Module 2: Planning Your Grant Project

Module 3: Crafting a Compelling Narrative

Module 4: Developing a Realistic Budget

Module 5: Designing a Robust Evaluation Plan

Module 6: Writing for Impact and Clarity

Module 7: Addressing Key Proposal Components

Module 8: Proposal Submission and Funder Relations

Module 9: Post-Submission and Grant Management

Module 10: Advanced Grant Writing Strategies

Here's What You'll Learn:

Module 1: Foundations of Psychological Measurement and Scaling

Module 2: Classical Test Theory (CTT) and Reliability

Module 3: Validity in Measurement

Module 4: Item Analysis and Scale Development

Module 5: Introduction to Factor Analysis for Scale Validation

Module 6: Item Response Theory (IRT) Fundamentals

Module 7: Differential Item Functioning (DIF) and Measurement Bias

Module 8: Standard Setting and Score Interpretation

Module 9: Specialized Measurement Models and Techniques

Module 10: Practical Implementation and Psychometric Reporting

Here's What You'll Learn:

Module 1: Introduction to Social Protection and Food Security

Module 2: Targeting and Beneficiary Selection

Module 3: Cash Transfer Programs

Module 4: Food Assistance Programs

Module 5: Social Insurance

Module 6: Monitoring and Evaluation of Social Protection Programs

Module 7: Policy and Legal Frameworks for Social Protection

Module 8: Social Protection in Fragile and Conflict-Affected States

Module 9: Social Protection and Gender

Module 10: Emerging Trends and Innovations in Social Protection

Here's What You'll Learn:

Module 1: Foundations of Effective Communication

Module 2: Mastering Verbal Communication

Module 3: Non-Verbal and Listening Skills

Module 4: Communicating with Confidence and Assertiveness

Module 5: Interpersonal Communication and Emotional Intelligence

Module 6: Business Writing and Digital Communication

Module 7: Delivering Powerful Presentations

Module 8: Conflict Resolution and Difficult Conversations

Module 9: Cross-Cultural Communication

Module 10: Building and Maintaining Professional Relationships

Here's What You'll Learn:

Module 1: Introduction to Medical Data Analysis & R Setup

Module 2: R Fundamentals and Data Import for Healthcare Data

Module 3: Data Wrangling in R using the tidyverse

Module 4: Descriptive Statistics and Data Summarization in Medical Context

Module 5: Basic Biostatistics in R (Hypothesis Testing)

Module 6: Introduction to Regression Models in Medical Research

Module 7: Survival Analysis Fundamentals and Implementation in R

Module 8: Data Visualization for Medical Research using ggplot2

Module 9: Introduction to Power BI for Data Integration

Module 10: Connecting, Importing, and Transforming Medical Data in Power BI

Module 11: Data Modeling and Relationships in Power BI

Module 12: Introduction to DAX for Advanced Calculations

Module 13: Creating Interactive Dashboards in Power BI

Module 14: Visualization Best Practices for Medical Reporting

Module 15: Incorporating R Visuals and Scripts into Power BI

Module 16: Ethics, Data Governance, and Reproducible Research

Module 17: Advanced Power BI: Security and Distribution

Module 18: Capstone Project: End-to-End Medical Analytics Workflow

Module 1: Introduction to Public Sector Accounting and IPSAS

Module 2: Core IPSAS Standards - Presentation of Financial Statements

Module 3: Core IPSAS Standards - Assets

Module 4: Core IPSAS Standards - Liabilities and Equity

Module 5: Core IPSAS Standards - Financial Instruments

Module 6: Specific Public Sector Issues under IPSAS

Module 7: Transition to IPSAS Accrual Accounting

Module 8: Budgeting and Financial Reporting under IPSAS

Module 9: Current Developments and Interpretations of IPSAS

Module 10: Practical Application and Case Studies in the Kenyan Public 

Here's What You'll Learn:

Module 1: Foundations of Multivariate Analysis and Data Screening in SPSS

Module 2: Multiple Linear Regression and Model Building in SPSS

Module 3: Binary Logistic Regression Analysis in SPSS

Module 4: Multivariate Analysis of Variance (MANOVA) in SPSS

Module 5: Discriminant Function Analysis (DFA) using SPSS

Module 6: Exploratory Factor Analysis (EFA) and Dimensionality Reduction in SPSS

Module 7: Cluster Analysis and Market Segmentation in SPSS

Module 8: Introduction to Survival Analysis (Cox Regression) in SPSS

Module 9: Repeated Measures ANOVA and Mixed Models in SPSS

Module 10: Advanced Reporting, Interpretation, and Presentation of Results

Here's What You'll Learn:

Module 1: Fundamentals of Climate Science and Modeling

Module 2: Types and Scales of Climate Models

Module 3: Climate Data Sources and Downscaling Techniques

Module 4: Understanding Climate Scenarios and Emissions Pathways

Module 5: Impact Modeling and Sectoral Applications

Module 6: Uncertainty and Limitations in Climate Projections

Module 7: Communicating Climate Model Outputs

Module 8: Integrating Climate Scenarios into Decision-Making

Module 9: Practical Application of Climate Modeling Tools

Module 10: Developing Custom Climate Scenarios for Planning

Here's What You'll Learn:

Module 1: Foundations of Metadata and Data Governance

Module 2: Metadata Architecture, Models, and Standards

Module 3: Data Cataloging Principles and Tool Selection

Module 4: Automated Metadata Capture and Data Discovery

Module 5: Implementing Data Quality and Data Stewardship

Module 6: Business Glossary, Taxonomy, and Data Definitions

Module 7: Understanding and Mapping Data Lineage

Module 8: Technical Metadata and Integration with Data Sources

Module 9: Operationalizing the Data Catalog and Search

Module 10: Metadata Management Strategy and Roadmap

Here's What You'll Learn:

Module 1: Foundations and Principles of Epidemiology

Module 2: Measures of Disease Occurrence and Association

Module 3: Study Design: Observational Studies (Cross-Sectional and Case-Control)

Module 4: Study Design: Cohort and Intervention Studies (Trials)

Module 5: Addressing Bias, Confounding, and Effect Modification

Module 6: Public Health Surveillance and Outbreak Investigation

Module 7: Introduction to Regression Modeling in Epidemiology

Module 8: Causal Inference and Advanced Analytical Techniques

Module 9: Validity, Reliability, and Survey Instrument Design

Module 10: Ethical Issues, Communication, and Proposal Development

Here's What You'll Learn:

Module 1: Introduction to Food Systems and Nutrition

Module 2: Environmental Sustainability of Food Systems

Module 3: Social Equity and Economic Viability in Food Systems

Module 4: Sustainable Food Production Systems

Module 5: Sustainable Food Processing and Supply Chains

Module 6: Promoting Sustainable and Healthy Diets

Module 7: Policy and Governance for Sustainable Food Systems

Module 8: Climate Change and Food Systems Resilience

Module 9: Measurement and Assessment of Sustainable Food Systems

Module 10: Designing Sustainable Food Systems Interventions

Here's What You'll Learn:

Module 1: Foundational Concepts in Programme Evaluation

Module 2: Logic Models, Theory of Change, and Evaluation Questions

Module 3: Choosing the Right Impact Evaluation Design and Counterfactuals

Module 4: Data Collection, Quality Assurance, and Introduction to Statistical Software

Module 5: Non-Experimental Methods: Propensity Score Matching (PSM)

Module 6: Quasi-Experimental Methods: Difference-in-Differences (DiD)

Module 7: Experimental Methods: Randomized Control Trials (RCTs)

Module 8: Advanced Data Analysis: Regression Analysis and Treatment Heterogeneity

Module 9: Integrating Qualitative Data and Mixed-Methods Evaluation

Module 10: Reporting, Dissemination, and Utilization of Evaluation Findings

Here's What You'll Learn:

Module 1: Understanding Post-Harvest Loss and Food Waste

Module 2: Causes and Hotspots of Post-Harvest Loss

Module 3: Post-Harvest Technologies for Cereals and Grains

Module 4: Post-Harvest Technologies for Fruits, Vegetables, and Perishables

Module 5: Food Processing and Value Addition for Waste Reduction

Module 6: Sustainable Packaging and Cold Chain Management

Module 7: Addressing Food Waste at Retail and Consumer Levels

Module 8: Policy, Governance, and Legal Frameworks for PHL/FW Reduction

Module 9: Measurement, Monitoring, and Reporting of PHL/FW

Module 10: Designing and Implementing PHL/FW Reduction Programs

Here's What You'll Learn:

Module 1: Understanding Climate Change and Its Impacts

Module 2: Principles of Effective Climate Communication

Module 3: Audience Analysis and Segmentation for Climate Action

Module 4: Crafting Compelling Climate Messages

Module 5: Communication Channels and Tools for Climate Awareness

Module 6: Engaging Stakeholders and Building Partnerships

Module 7: Addressing Misinformation and Climate Denial

Module 8: Storytelling and Narrative Building for Climate Change

Module 9: Measuring the Impact of Climate Communication

Module 10: Developing a Climate Communication Strategy

Here's What You'll Learn:

Module 1: Introduction to Early Warning Systems and Food Security

Module 2: Understanding Food Security Information Systems

Module 3: Data Collection and Indicators for Early Warning

Module 4: Climate and Environmental Monitoring for Food Security

Module 5: Market and Economic Analysis for Food Security

Module 6: Conflict, Displacement, and Food Security Linkages

Module 7: Information Analysis and Interpretation for Early Warning

Module 8: Communication and Dissemination of Early Warning Information

Module 9: Linking Early Warning to Early Action

Module 10: Developing and Strengthening Early Warning Systems

Here's What You'll Learn:

Module 1: Introduction to Epidemiological Modeling and R

Module 2: Fundamentals of Deterministic Compartmental Models

Module 3: Implementing SIR/SEIR Models in R

Module 4: Parameter Estimation and Model Calibration

Module 5: Modeling the Impact of Vaccination

Module 6: Advanced Model Structures and Dynamics

Module 7: Stochastic Modeling and Outbreak Simulation

Module 8: Policy Analysis and Economic Evaluation

Module 9: Global Sensitivity and Uncertainty Analysis

Module 10: Case Studies and Advanced Modeling in R

Here's What You'll Learn:

Module 1: Foundations of Data Exploration and the EDA Process

Module 2: Descriptive Statistics for Data Understanding

Module 3: Univariate and Bivariate Data Visualization

Module 4: Data Quality Assessment and Missing Value Imputation

Module 5: Outlier Detection and Treatment

Module 6: Foundations of Feature Engineering

Module 7: Handling Categorical Variables

Module 8: Feature Scaling and Transformation

Module 9: Dimensionality Reduction and Feature Selection

Module 10: Time-Based and Automated Feature Engineering

Here's What You'll Learn:

Module 1: Introduction to Urban Agriculture and Food Security

Module 2: Principles of Urban Permaculture and Ecological Design

Module 3: Urban Farming Techniques and Technologies

Module 4: Soil Health and Nutrient Management in Urban Settings

Module 5: Water Management and Irrigation for Urban Farms

Module 6: Pest and Disease Management in Urban Agriculture

Module 7: Policy, Planning, and Governance for Urban Agriculture

Module 8: Economic Models and Value Chains in Urban Agriculture

Module 9: Community Engagement and Social Aspects of Urban Agriculture

Module 10: Developing an Urban Agriculture Project Plan

Here's What You'll Learn:

Module 1: Foundations of Data Analysis in Excel

Module 2: Data Cleaning, Preparation, and Transformation

Module 3: Descriptive Statistics and Exploratory Data Analysis

Module 4: Data Visualization for Insightful Reporting

Module 5: Advanced Formulas and Logical Functions

Module 6: PivotTables and PivotCharts for Aggregation

Module 7: What-If Analysis and Scenario Planning

Module 8: Regression Analysis and Forecasting

Module 9: Introduction to Power Query and Power Pivot

Module 10: Building an Evidence-based Decision-making Framework

Here's What You'll Learn:

Module 1: Introduction to Stata and M&E Concepts

Module 2: Data Management and Preparation in Stata

Module 3: Descriptive Analysis for M&E Data

Module 4: Monitoring and Indicator Tracking

Module 5: Hypothesis Testing for Impact Evaluation

Module 6: Introduction to Regression Analysis

Module 7: Advanced Regression Models for M&E

Module 8: Qualitative and Mixed-Methods Data Analysis

Module 9: Data Visualization for M&E Reports

Module 10: Project-Based Application: End-to-End M&E Analysis

Here's What You'll Learn:

Module 1: Introduction to Markets and Food Security

Module 2: Value Chain Analysis for Market Access

Module 3: Market Information Systems and Price Analysis

Module 4: Agricultural Marketing Strategies and Best Practices

Module 5: Post-Harvest Handling and Quality Standards for Market Access

Module 6: Financing and Risk Management in Agricultural Trade

Module 7: Trade Policies, Agreements, and Food Security

Module 8: Global Food Trade Dynamics and Trends

Module 9: Overcoming Market Access Barriers

Module 10: Designing Market-Oriented Food Security Interventions

Module 1: Introduction to MEAL for Agricultural Projects

Module 2: Designing MEAL Systems for Agricultural Projects

Module 3: Participatory Data Collection Techniques

Module 4: Data Management and Storage

Module 5: Data Analysis and Interpretation

Module 6: Impact Evaluation in Agricultural Projects

Module 7: Accountability Mechanisms for Agricultural Projects

Module 8: Integrating Learning into Agricultural Projects

Module 9: Using MEAL Findings in Agricultural Projects

Module 10: Reporting and Communicating MEAL Results

Here's What You'll Learn:

Module 1: Foundations of Renewable Energy and Data  

Module 2: Python Environment Setup and Fundamentals  

Module 3: Data Acquisition and Cleaning for Energy Datasets  

Module 4: Exploratory Data Analysis (EDA) of Solar PV Data  

Module 5: Wind Energy Resource Assessment and Data Preparation  

Module 6: Advanced Time Series Analysis for Energy Data  

Module 7: Introduction to Machine Learning for Renewables  

Module 8: Predictive Modeling for Energy Output Forecasting  

Module 9: Power BI Desktop and Data Modeling Fundamentals  

Module 10: Creating Interactive Solar PV Dashboards in Power BI  

Module 11: Advanced DAX and Calculated Measures  

Module 12: Geospatial Analytics of Renewable Assets  

Module 13: Predictive Maintenance Analytics for Wind Turbines  

Module 14: Financial Modeling and Levelized Cost of Energy (LCOE)  

Module 15: Grid Impact Analysis and Load Profile Modeling  

Module 16: Optimization and Scenario Analysis in Renewable Projects  

Module 17: Capstone Project: End-to-End Analytics Workflow  

Module 18: Communicating Insights and Data Storytelling

Here's What You'll Learn:

Module 1: Introduction to Statistics and Inference

Module 2: Probability Distributions and Estimation

Module 3: Fundamentals of Hypothesis Testing

Module 4: One-Sample Hypothesis Tests

Module 5: Two-Sample Hypothesis Tests

Module 6: Analysis of Variance (ANOVA)

Module 7: Non-Parametric Tests

Module 8: Chi-Square Tests and Association

Module 9: Simple Linear Regression

Module 10: Advanced Topics and Model Selection

Here’s What You’ll Learn:

Module 1: Introduction to MLOps and the ML Lifecycle

Module 2: Version Control and Experiment Tracking

Module 3: ML Model Packaging and Containerization

Module 4: CI/CD for Machine Learning

Module 5: Model Deployment Strategies

Module 6: Monitoring and Logging in Production

Module 7: Infrastructure and Orchestration (Kubernetes/Cloud)

Module 8: Model Governance and Regulatory Compliance

Module 9: Data and Model Drift Detection

Module 10: Advanced MLOps Patterns and Future Trends

Here's What You'll Learn:

Module 1: Foundations of Corporate Finance

Module 2: Capital Budgeting Techniques

Module 3: Cost of Capital

Module 4: Financial Statement Analysis

Module 5: Business Valuation: Discounted Cash Flow (DCF) Method

Module 6: Business Valuation: Multiples-Based Valuation

Module 7: Mergers and Acquisitions (M&A)

Module 8: Capital Structure Decisions

Module 9: Dividend Policy

Module 10: Risk Management and Financial Planning

Here's What You'll Learn:

Module 1: Foundations of Research Ethics

Module 2: Informed Consent and Participant Protection

Module 3: Privacy, Confidentiality, and Data Security

Module 4: Data Management and Sharing

Module 5: Authorship and Intellectual Property

Module 6: Peer Review Ethics

Module 7: Research Misconduct

Module 8: Conflict of Interest in Research

Module 9: Responsible Use of Animals in Research

Module 10: Responsible Conduct in Collaborative Research and Emerging Areas

Here's What You'll Learn:

Module 1: Foundations of Survey Research and Total Survey Error

Module 2: Survey Design Process and Questionnaire Development

Module 3: Crafting Effective Survey Questions and Response Formats

Module 4: Sampling Methods and Coverage Error

Module 5: Modes of Data Collection and Administration

Module 6: Measurement Error: Validity and Reliability

Module 7: Pre-testing, Cognitive Interviews, and Pilot Studies

Module 8: Data Processing, Cleaning, and Ethical Considerations

Module 9: Non-response and Weighting Adjustments

Module 10: Survey Data Analysis and Reporting Findings

Here's What You'll Learn:

Module 1: Introduction to R and RStudio

Module 2: Data Wrangling with dplyr and tidyr

Module 3: Exploratory Data Analysis and Visualization

Module 4: Descriptive Statistics and Data Summarization

Module 5: Hypothesis Testing and t-Tests

Module 6: ANOVA and Chi-Squared Tests

Module 7: Linear Regression Analysis

Module 8: Multiple Regression and Model Diagnostics

Module 9: Introduction to Time Series Analysis

Module 10: Building an End-to-End Statistical Project

Here's What You'll Learn:

Module 1: Foundations of Environmental Data and R Setup

Module 2: Data Acquisition and Preprocessing with R

Module 3: Geospatial Analysis of Environmental Data

Module 4: Time Series Modeling for Environmental Monitoring

Module 5: Statistical Analysis and Modeling in R

Module 6: Advanced Machine Learning for Environmental Prediction

Module 7: Introduction to Power BI for Environmental Reporting

Module 8: Data Modeling and Advanced DAX for Metrics

Module 9: Creating Interactive Environmental Dashboards

Module 10: Environmental Data Governance and Stakeholder Communication

Here's What You'll Learn:

Module 1: Introduction to Food Policy and Governance

Module 2: The Policy Cycle and Food Security

Module 3: National Food Security Strategies and Plans

Module 4: Role of Different Actors in Food Governance

Module 5: Legal and Regulatory Frameworks for Food Security

Module 6: Land, Water, and Natural Resource Governance

Module 7: Trade, Markets, and Food Policy

Module 8: Climate Change, Disasters, and Food Governance

Module 9: Monitoring, Evaluation, and Accountability in Food Policy

Module 10: Designing and Advocating for Effective Food Policies

Here’s What You’ll Learn:

Module 1: Introduction to Payroll Management and Kenyan Statutory Deductions

Module 2: Understanding the Legal and Regulatory Framework for Payroll in Kenya

Module 3: Setting Up and Managing Employee Records for Payroll in Kenya

Module 4: Calculating Gross Pay and Allowances in Kenya

Module 5: Calculating and Remitting Pay As You Earn (PAYE) in Kenya

Module 6: Calculating and Remitting National Social Security Fund (NSSF) Contributions in Kenya

Module 7: Calculating and Remitting National Hospital Insurance Fund (NHIF) Contributions in Kenya

Module 8: Other Statutory and Voluntary Deductions in Kenya

Module 9: Payroll Processing, Reporting, and Record Keeping in Kenya

Module 10: Best Practices and Emerging Trends in Payroll Management in Kenya

Here's What You'll Learn:

Module 1: Introduction to Time Series Data and Components

Module 2: Data Preparation and Visualization in SPSS

Module 3: Smoothing and Classical Decomposition Methods

Module 4: Introduction to Exponential Smoothing Models

Module 5: Advanced Exponential Smoothing: Holt-Winters Models

Module 6: Stationarity, Autocorrelation, and Partial Autocorrelation

Module 7: ARIMA Model Identification and Specification

Module 8: ARIMA Model Estimation and Diagnostic Checking

Module 9: Advanced Forecasting Techniques and Model Comparison

Module 10: Integrating Time Series with Regression and Reporting

Here's What You'll Learn:

Module 1: Foundations of Data Visualization and Design Principles

Module 2: Visualizing Univariate and Comparative Data

Module 3: Effective Visualization of Relationships and Distributions

Module 4: Advanced Time-Series and Flow Visualization

Module 5: Geospatial Data and Static Mapping

Module 6: Interactive Visualization and Dashboard Creation

Module 7: Visualization for Statistical Modeling and Machine Learning

Module 8: Principles of Visual Perception and Cognitive Load

Module 9: Communicating Uncertainty and Statistical Inference

Module 10: Publication-Ready Graphics and Ethical Reporting

Module 1: Introduction to ESIA
Module 2: Scoping and Baseline Studies
Module 3: Impact Identification and Assessment
Module 4: Mitigation and Management Plans
Module 5: Stakeholder Engagement and Public Participation
Module 6: Environmental and Social Management Systems (ESMS)
Module 7: Reporting and Documentation
Module 8: Case Studies and Best Practices
Module 9: Sector-Specific ESIAs
Module 10: Advanced Topics in ESIA

Here's What You'll Learn:

Module 1: Introduction to Data Warehousing

Module 2: Dimensional Modeling

Module 3: ETL Concepts and Architecture

Module 4: Data Extraction Techniques

Module 5: Data Transformation Processes

Module 6: Data Loading Strategies

Module 7: ETL Tool Selection and Best Practices

Module 8: Data Quality Management in Data Warehousing

Module 9: Performance Tuning of ETL Processes

Module 10: Data Warehouse Implementation and Business Intelligence

Here's What You'll Learn:

Module 1: Fundamentals of Data and the Data Lifecycle

Module 2: Data Collection Methods and Tools

Module 3: Data Collection Planning and Ethics

Module 4: Introduction to Data Cleaning

Module 5: Handling Missing Data

Module 6: Dealing with Inconsistent and Erroneous Data

Module 7: Data Validation and Quality Assurance

Module 8: Automating Data Cleaning Workflows

Module 9: Introduction to Data Visualization

Module 10: Project-Based Application: End-to-End Data Pipeline

Here's What You'll Learn:

Module 1: Foundations of Petroleum Data and Analytics

Module 2: Python Environment Setup and Core Libraries

Module 3: Data Acquisition and Preparation for E&P Datasets

Module 4: Exploratory Data Analysis (EDA) of Production Trends

Module 5: Well Performance Modeling and Decline Curve Analysis (DCA)

Module 6: Advanced Time Series Forecasting for Oil and Gas Prices

Module 7: Geospatial Analytics for Field and Asset Mapping

Module 8: Integrated Reservoir Data Analytics

Module 9: Drilling and Completion Performance Optimization

Module 10: Machine Learning for Production Prediction (Regression)

Module 11: Machine Learning for Subsurface Classification

Module 12: Power BI Desktop and Data Modeling Essentials

Module 13: Advanced DAX for Oilfield Metrics and KPIs

Module 14: Developing Interactive Production Dashboards

Module 15: Financial Analytics and Economic Limit Modeling

Module 16: Risk and Uncertainty Analysis (Monte Carlo Simulation)

Module 17: Midstream and Downstream Logistics Analytics

Module 18: Capstone Project and Stakeholder Communication

Here’s What You’ll Learn:

Module 1: Foundations of Forensic Accounting

Module 2: Fraud Schemes and Typologies

Module 3: The Fraud Investigation Process

Module 4: Gathering and Analyzing Evidence

Module 5: Interviewing and Interrogation Techniques

Module 6: Digital Forensics and Cybercrime

Module 7: Reporting and Communicating Findings

Module 8: Legal Aspects of Fraud Examination

Module 9: Prevention and Internal Controls

Module 10: Capstone Project: Mock Fraud Investigation

Here’s What You’ll Learn:

Module 1: Foundations of Corporate Finance

Module 2: The Time Value of Money

Module 3: Sources of Long-Term Finance

Module 4: Sources of Short-Term Finance

Module 5: Introduction to Capital Structure

Module 6: Theories of Capital Structure

Module 7: Cost of Capital

Module 8: Working Capital Management

Module 9: Capital Budgeting and Investment Decisions

Module 10: Capstone Project: Developing a Capital Structure Plan

Here's What You'll Learn:

Module 1: SurveyCTO Fundamentals and Server Setup

Module 2: Designing Forms with the Online Form Designer

Module 3: Advanced Form Logic and Validation (XPath Functions)