R Programming for Data Science Training Course

Course Overview

The R Programming for Data Science Training Course is a comprehensive professional development program designed to equip participants with the programming knowledge, statistical computing techniques, and practical analytical skills required to perform data science, statistical analysis, predictive modeling, data visualization, machine learning, and research analytics using the R programming language. As governments, universities, healthcare organizations, financial institutions, humanitarian agencies, research institutions, and private enterprises increasingly rely on data-driven decision-making, Artificial Intelligence (AI), business intelligence, predictive analytics, and evidence-based policy development, R has become one of the world's leading platforms for statistical computing and advanced data science. This course provides participants with practical expertise in R programming, data manipulation, statistical modeling, machine learning, visualization, reproducible research, dashboard development, and automated analytical reporting while emphasizing internationally recognized data science methodologies and programming best practices.

The training combines theoretical instruction with extensive hands-on practical sessions covering RStudio, R programming fundamentals, data structures, tidyverse, dplyr, tidyr, readr, stringr, ggplot2, plotly, Shiny dashboards, data cleaning, exploratory data analysis, descriptive statistics, inferential statistics, regression analysis, time series forecasting, multivariate statistics, machine learning using caret and randomForest, text analytics, geospatial analytics, SQL integration, API connectivity, and research reporting. Participants will gain practical experience importing, cleaning, transforming, analyzing, visualizing, modeling, and interpreting complex datasets while developing reusable R scripts and analytical workflows.

Participants will also explore emerging technologies including Artificial Intelligence (AI), Machine Learning, Deep Learning integration, cloud-based analytics, Big Data Analytics, research reproducibility, open science, geospatial data science, Internet of Things (IoT) analytics, business intelligence, statistical simulation, interactive reporting, ethical data science, cybersecurity for research data, and computational research methodologies. Emphasis is placed on statistical accuracy, reproducibility, data governance, programming standards, documentation, quality assurance, research ethics, project management, and continuous learning to support modern digital transformation and advanced analytics initiatives.

Throughout the course, participants will engage in programming laboratories, statistical analysis workshops, machine learning exercises, dashboard development projects, collaborative analytical assignments, visualization activities, and real-world interdisciplinary case studies. By the end of the training, participants will possess the competencies required to develop advanced analytical solutions using R, perform sophisticated statistical analyses, build predictive models, automate research workflows, communicate insights through interactive visualizations, and support evidence-based decision-making across diverse sectors.

Course Objectives

1.     Understand the fundamentals of R programming and statistical computing.

2.     Develop R programs for research, statistical analysis, and data science applications.

3.     Perform data cleaning, transformation, and management using tidyverse packages.

4.     Conduct descriptive, inferential, and multivariate statistical analyses.

5.     Develop professional data visualizations using ggplot2, Plotly, and Shiny dashboards.

6.     Apply machine learning techniques using R for predictive analytics.

7.     Integrate databases, APIs, and external data sources into analytical workflows.

8.     Automate research reporting and reproducible analytical processes.

9.     Apply ethical data management, research governance, and programming best practices.

10.  Utilize R to support scientific research, policy analysis, business intelligence, and organizational decision-making.

Organizational Benefits

1.     Strengthens organizational research and statistical analysis capacity.

2.     Enhances evidence-based planning and strategic decision-making.

3.     Improves data quality through standardized analytical workflows.

4.     Supports predictive analytics and business intelligence initiatives.

5.     Reduces manual reporting through automation and reproducible research.

6.     Strengthens organizational innovation using Artificial Intelligence and Machine Learning.

7.     Improves visualization and communication of analytical findings.

8.     Builds internal expertise in modern statistical computing.

9.     Supports digital transformation through advanced analytics technologies.

10.  Improves organizational performance through data-driven insights.

Target Participants

This course is designed for researchers, statisticians, data scientists, monitoring and evaluation specialists, economists, public health professionals, university lecturers, postgraduate students, financial analysts, policy analysts, government officers, NGO professionals, software developers, business intelligence analysts, healthcare researchers, consultants, engineers, scientists, project managers, academic researchers, and professionals responsible for research, statistical analysis, business intelligence, or evidence-based decision-making.

Course Outline

Module 1: Introduction to R Programming

·       Installing R and RStudio

·       R syntax and programming fundamentals

·       Variables and data structures

·       Functions and packages

·       Script development

·       Case Study: Building a basic R program for research data analysis

Module 2: Data Import and Data Management

·       Importing Excel and CSV data

·       Database connectivity

·       Data cleaning

·       Data transformation

·       Data validation

·       Case Study: Preparing national survey datasets for statistical analysis

Module 3: Data Manipulation with tidyverse

·       dplyr

·       tidyr

·       readr

·       stringr

·       Data wrangling

·       Case Study: Organizing organizational performance datasets for reporting

Module 4: Statistical Analysis with R

·       Descriptive statistics

·       Inferential statistics

·       Hypothesis testing

·       Correlation analysis

·       Regression analysis

·       Case Study: Evaluating development project outcomes using statistical methods

Module 5: Data Visualization

·       ggplot2

·       Plotly

·       Interactive graphics

·       Scientific visualization

·       Dashboard design

·       Case Study: Creating executive dashboards for organizational performance monitoring

Module 6: Machine Learning with R

·       Classification models

·       Regression models

·       Clustering

·       Model evaluation

·       Predictive analytics

·       Case Study: Predicting customer behavior using machine learning models

Module 7: Time Series and Forecasting

·       Time series analysis

·       Forecasting models

·       Trend analysis

·       Seasonal decomposition

·       Forecast accuracy

·       Case Study: Forecasting organizational performance indicators

Module 8: Text Mining and Geospatial Analytics

·       Text analytics

·       Natural language processing

·       Spatial data analysis

·       GIS integration

·       Mapping with R

·       Case Study: Analyzing qualitative survey responses and geographic trends

Module 9: Interactive Reporting with Shiny

·       Shiny fundamentals

·       Dashboard development

·       User interfaces

·       Interactive visualization

·       Web application deployment

·       Case Study: Developing an interactive executive analytics portal

Module 10: Artificial Intelligence and Advanced Analytics

·       Artificial Intelligence concepts

·       Deep Learning integration

·       Advanced predictive analytics

·       Cloud analytics

·       Automated decision support

·       Case Study: Applying AI-assisted analytics to organizational research

Module 11: Research Reproducibility and Data Governance

·       Reproducible research

·       Version control

·       Research documentation

·       Data governance

·       Ethical analytics

·       Case Study: Developing reproducible analytical workflows for collaborative research

Module 12: Data Science Project Management

·       Project planning

·       Analytical workflow management

·       Quality assurance

·       Performance optimization

·       Continuous improvement

·       Case Study: Managing an end-to-end R-based data science project

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, freight 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 training@fdc-k.org or call +254712260031.

14.  Website: Visit www.fdc-k.org for more information.