Format: Live instructor-led online training via Zoom / Microsoft Teams

DATA ANALYSIS, MODELING AND SIMULATION USING R TRAINING COURSE

COURSE OVERVIEW

Data Analysis, Modeling and Simulation Using R Training Course is a comprehensive practical programme designed to equip professionals with advanced skills in R programming, statistical data analysis, data visualization, predictive modeling, statistical modeling, simulation techniques, business analytics, quantitative analysis, and evidence-based decision-making. The training introduces participants to the R statistical computing environment and demonstrates how R can be used to import, clean, transform, analyze, model, visualize, and interpret complex datasets. Participants will work with practical datasets and learn how to convert raw information into meaningful analytical insights for business, finance, economics, research, policy, operations, and management.

The course provides extensive coverage of data management, exploratory data analysis (EDA), descriptive statistics, inferential statistics, probability distributions, hypothesis testing, correlation analysis, regression analysis, time-series analysis, predictive analytics, multivariate analysis, and statistical forecasting. Participants will develop practical capabilities in preparing datasets, identifying patterns and relationships, detecting anomalies and outliers, selecting appropriate statistical techniques, validating analytical assumptions, interpreting model outputs, and communicating findings through professional data visualizations and analytical reports.

The programme further develops practical expertise in regression modeling, classification, clustering, Monte Carlo simulation, scenario analysis, sensitivity analysis, forecasting, model validation, simulation design, probability-based decision modeling, and risk analysis using R. Participants will learn how to construct analytical models, evaluate model performance, simulate alternative scenarios, quantify uncertainty, and use statistical evidence to support strategic and operational decisions. Emphasis is placed on reproducible analysis, analytical accuracy, appropriate model selection, and clear interpretation of results.

Through hands-on exercises, real-world datasets, coding demonstrations, analytical assignments, modeling scenarios, simulation exercises, and general case studies, participants will develop a practical R-Based Data Analysis, Modeling and Simulation Framework. The training is particularly valuable for organizations seeking to strengthen data-driven decision-making, predictive analytics, statistical research, quantitative risk analysis, business intelligence, forecasting, and evidence-based planning using one of the leading open-source statistical programming environments.

COURSE OBJECTIVES

By the end of the training, participants will be able to:

1. Navigate the R environment and use R for statistical computing and data analysis.
2. Import, clean, transform, organize, and manage structured and unstructured datasets.
3. Conduct exploratory data analysis using descriptive statistics and data visualization.
4. Apply probability, statistical inference, hypothesis testing, and correlation analysis.
5. Build and interpret regression and predictive statistical models.
6. Conduct time-series analysis, forecasting, and trend analysis using R.
7. Develop Monte Carlo simulations and scenario-based analytical models.
8. Apply model validation, diagnostics, sensitivity analysis, and performance evaluation.
9. Create professional analytical visualizations and communicate statistical findings.
10. Develop practical R-based models and simulations for evidence-based decision-making.

ORGANIZATION BENEFITS

1. Strengthens organizational data analytics capabilities.
2. Improves evidence-based and data-driven decision-making.
3. Enhances statistical modeling and predictive analytics capacity.
4. Improves forecasting, planning, and scenario analysis.
5. Supports quantitative risk assessment and uncertainty analysis.
6. Reduces dependence on manual data-analysis processes.
7. Improves the quality and consistency of analytical reporting.
8. Strengthens business intelligence and research capabilities.
9. Enables cost-effective use of open-source analytical technologies.
10. Builds internal capacity for advanced data analysis, modeling, and simulation.

TARGET PARTICIPANTS

This course is designed for Data Analysts, Business Analysts, Financial Analysts, Economists, Statisticians, Researchers, Monitoring and Evaluation Professionals, Risk Managers, Actuaries, Investment Analysts, Finance Professionals, Operations Managers, Business Intelligence Professionals, Data Scientists, Quantitative Analysts, Policy Analysts, Academicians, Market Researchers, Project Managers, Planning Professionals, IT Professionals, Auditors, Banking Professionals, Government Officials, and managers responsible for data analysis, forecasting, research, modeling, risk assessment, performance measurement, and evidence-based decision-making.

COURSE OUTLINE

MODULE 1: R PROGRAMMING & DATA ANALYSIS FOUNDATIONS

1. Introduction to R, RStudio, packages, scripts, and working environments
2. R objects, variables, vectors, matrices, lists, factors, and data frames
3. Importing and exporting data from CSV, Excel, databases, and other sources
4. Data cleaning, transformation, filtering, sorting, and preparation
5. Data manipulation using modern R packages and analytical workflows
6. Reproducible analysis, documentation, coding practices, and analytical project organization
   General Case Study: A business receives customer, sales, and operational datasets from different sources. Participants use R to import, clean, merge, transform, validate, and prepare the datasets for subsequent statistical analysis.

MODULE 2: EXPLORATORY DATA ANALYSIS & STATISTICAL METHODS

1. Principles and objectives of exploratory data analysis
2. Descriptive statistics, measures of central tendency, and dispersion
3. Frequency distributions, probability concepts, and statistical distributions
4. Data visualization using charts, plots, and analytical graphics
5. Correlation analysis and identification of relationships between variables
6. Hypothesis testing, confidence intervals, statistical significance, and interpretation
   General Case Study: An organization wants to understand factors influencing employee productivity. Participants conduct exploratory data analysis, visualize relationships, calculate descriptive statistics, test hypotheses, and interpret the statistical evidence.

MODULE 3: STATISTICAL MODELING & PREDICTIVE ANALYTICS USING R

1. Principles of statistical modeling and model selection
2. Simple and multiple linear regression analysis
3. Logistic regression and classification models
4. Model assumptions, diagnostics, residual analysis, and multicollinearity
5. Model performance, validation, accuracy, and predictive interpretation
6. Applying predictive analytics to business, finance, economics, and operational decisions
   General Case Study: A financial-services organization wants to predict customer loan repayment outcomes. Participants develop a predictive model, select relevant variables, evaluate model assumptions, assess predictive performance, and interpret the results for decision-making.

MODULE 4: TIME-SERIES ANALYSIS & FORECASTING

1. Introduction to time-series data and forecasting principles
2. Trend, seasonality, cycles, stationarity, and autocorrelation
3. Time-series decomposition and exploratory analysis
4. Forecasting models and prediction intervals
5. Model evaluation, forecasting accuracy, and validation
6. Applications of R forecasting in finance, economics, sales, demand, and operations
   General Case Study: A retail organization needs to forecast monthly product demand. Participants analyze historical sales data, identify trends and seasonality, develop forecasting models, compare forecast accuracy, and produce projections for future planning.

MODULE 5: SIMULATION, MONTE CARLO & SCENARIO MODELING

1. Fundamentals of simulation modeling and uncertainty analysis
2. Probability-based simulation and random-variable generation
3. Monte Carlo simulation using R
4. Scenario analysis, sensitivity analysis, and stress testing
5. Simulation-based risk assessment and decision modeling
6. Interpreting simulation results and communicating uncertainty
   General Case Study: A project management team is uncertain about project completion time and total costs. Participants build a Monte Carlo simulation to model alternative outcomes, estimate probability ranges, identify key risk drivers, and support project contingency planning.

MODULE 6: ADVANCED MODELING, INTEGRATION & ANALYTICAL DECISION-MAKING

1. Advanced statistical modeling and multivariate analytical techniques
2. Clustering, segmentation, and classification applications
3. Model comparison, validation, optimization, and sensitivity testing
4. Integrating data analysis, predictive modeling, and simulation
5. Developing analytical dashboards, reports, and decision-support outputs
6. Developing an R-Based Data Analysis, Modeling and Simulation Framework
   General Case Study: An organization wants to establish an integrated analytics function for strategic planning. Participants combine exploratory analysis, statistical modeling, forecasting, simulation, scenario analysis, and visualization to produce a decision-support framework for management.

GENERAL INFORMATION

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.