Introduction

R is a powerful programming language and environment for statistical computing and graphics. It is widely used in data analysis, statistical modeling, and generating insightful visualizations, making it a perfect tool for Business Intelligence (BI). This course will focus on utilizing R to manipulate, analyze, and visualize business data, helping professionals extract valuable insights and enhance decision-making processes. Participants will gain practical knowledge in integrating R with BI workflows, including using R in combination with BI tools like Power BI or Tableau.

Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of R programming for BI and data analysis.
• Import, clean, and manipulate business data using R’s data manipulation libraries (e.g., dplyr, tidyr).
• Perform advanced statistical analysis and predictive modeling using R.
• Create dynamic visualizations to communicate business insights effectively.
• Integrate R with BI tools like Power BI and Tableau for automated reporting and dashboards.
• Develop custom business intelligence models using R for forecasting, trend analysis, and optimization.
• Use R to automate repetitive BI tasks such as data preparation, reporting, and analysis.
• Apply machine learning techniques within R to solve complex business problems.

Who Should Attend?

This course is ideal for:

• Data analysts and business intelligence professionals who want to enhance their R programming skills
• Business analysts looking to incorporate advanced data analysis techniques into their BI workflows
• Data scientists who want to expand their toolset by integrating R with BI systems
• IT professionals who support BI platforms and want to learn how R can enhance business reporting
• Anyone interested in learning how to use R for data analysis, visualization, and decision support in BI systems

Course Outline

Day 1: Introduction to R and Business Intelligence

• Overview of R in Business Intelligence: Why R is a powerful tool for BI and data analysis
• R Basics: Variables, data types, operators, functions, and control structures
• R Environment Setup: Installing R and RStudio, configuring your workspace
• Importing Data: Loading business data from different sources (CSV, Excel, databases) into R
• Basic Data Manipulation in R: Using dplyr for filtering, selecting, and transforming data
• Data Structures in R: Vectors, data frames, and lists—essential for BI tasks
• Exploratory Data Analysis (EDA): Summarizing business data using descriptive statistics
• Introduction to Business Intelligence: How R fits into the BI lifecycle (data collection, analysis, reporting)
• Case Study: Analyzing Sales Data with R for Initial Insights
• Hands-on Session: Using R to Import and Manipulate Business Data

Day 2: Advanced Data Manipulation and Cleaning with R

• Advanced Data Wrangling: Merging, reshaping, and joining datasets using dplyr, tidyr
• Handling Missing Data: Techniques for dealing with missing values, outliers, and data imputation
• Data Transformation: Applying complex transformations such as aggregations, sorting, and pivoting
• Date and Time Functions: Working with date-time objects for time-based BI reports
• String Manipulation: Using R for cleaning and transforming text data
• Business Use Cases: Segmenting customers, identifying sales patterns, and performing cohort analysis
• Integrating Data from Multiple Sources: Merging data from CSV, Excel, and SQL databases for comprehensive reporting
• Case Study: Cleaning and Transforming Customer Data for Segmentation and Profiling
• Hands-on Session: Advanced Data Wrangling and Cleaning in R for BI

Day 3: Data Visualization for Business Intelligence with R

• Introduction to Data Visualization in R: Why visualization is important for BI and decision-making
• Using ggplot2 for Data Visualization: The fundamentals of creating static and interactive visualizations
• Best Practices for BI Dashboards: Designing effective and insightful dashboards for business users
• Creating Business Visualizations: Visualizing trends, distributions, and outliers in business data
• Interactive Visualizations: Using plotly and shiny for creating dynamic and interactive BI reports
• Advanced Visualization Techniques: Heatmaps, boxplots, time series plots, and scatter plots for business insights
• Visualizing KPIs and Metrics: Designing key performance indicator (KPI) reports and dashboards
• Embedding R Visualizations in BI Tools: Integrating R visualizations with Power BI, Tableau, or custom dashboards
• Case Study: Visualizing Sales Trends and Customer Behavior with R
• Hands-on Session: Building Interactive BI Dashboards and Reports Using R

Day 4: Advanced Analytics and Predictive Modeling in R

• Introduction to Predictive Modeling: How predictive analytics can improve decision-making in BI
• Linear and Logistic Regression: Building regression models for forecasting and classification in business contexts
• Time Series Forecasting: Using R for demand forecasting, inventory optimization, and trend analysis
• Clustering and Segmentation: K-means and hierarchical clustering for customer segmentation
• Classification Models: Decision trees, random forests, and support vector machines (SVM) for customer classification
• Cross-Validation and Model Evaluation: Techniques for assessing model performance in BI applications
• Model Interpretability: Understanding model outputs to make data-driven business decisions
• Case Study: Predicting Customer Churn with Logistic Regression and Decision Trees
• Hands-on Session: Building Predictive Models for Sales Forecasting and Customer Segmentation in R

Day 5: Automating BI Reporting and Integrating R with BI Tools

• Automating Reports with R: Writing R scripts for automated report generation
• Scheduling R Scripts: Setting up automated tasks to refresh BI reports and analyses regularly
• Using R in BI Tools: Connecting R to Power BI and Tableau for dynamic reporting
• Embedding R Scripts in BI Dashboards: Using R as a backend for interactive and customized BI reports
• Advanced R Functions for BI: Creating custom functions and reusable code for reporting automation
• Deploying R Models in Business Environments: Packaging R models for integration into BI workflows
• R Markdown for Report Generation: Creating dynamic and reproducible reports with R Markdown and LaTeX
• Best Practices in Reporting: Designing clear, actionable BI reports that drive decision-making
• Final Project: Building an End-to-End BI Reporting System Using R
• Hands-on Session: Integrating R Visualizations and Models into Power BI/ Tableau Dashboards