Business Analytics with R for Data-Driven Decision Making

Business Analytics with R explores how R programming turns business data into clear analysis, visual reports, and informed decisions. The course examines data preparation, exploratory analysis, data visualization with ggplot2, and predictive modeling with caret. Work with business-focused datasets to identify patterns, measure performance, communicate findings, and build models that support forecasting and planning.
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business analytics with r is supported by industry partners that provide real-world datasets for practice. Partner companies such as Acme Corp and DataForge contribute 5 proprietary case studies used in the curriculum.

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Curriculum

business analytics with r, zeblearnindia, r programming course, data visualization with ggplot2, predictive modeling caret Course Syllabus Structure

business analytics with r covers end-to-end data workflows using R programming. Modules include dplyr for data wrangling, ggplot2 for advanced visualizations, and caret for building 10 predictive models.

  • Project Brief: Financial Review
  • Import Data into R
  • What are Factors (Refresher)
  • The Factor Variable Trap
  • FVT Example
  • gsub() and sub()
  • Dealing with Missing Data
  • What is an NA?
  • An Elegant Way To Locate Missing Data
  • Data Filters: which() for Non-Missing Data
  • Data Filters: is.na() for Missing Data
  • Removing records with missing data
  • Reseting the dataframe index
  • Replacing Missing Data: Factual Analysis Method
  • Replacing Missing Data: Median Imputation Method  
  • Replacing Missing Data: Deriving Values Method
  • Visualizing results
  • Section Recap
  • Data Preparation

  • Project Brief: Machine Utilization
  • Import Data Into R
  • Handling Date-Times in R
  • R programming: What is a List?
  • Naming components of a list
  • Extracting components lists: [] vs [[]] vs $
  • Adding and deleting components
  • Subsetting a list
  • Creating A Timeseries Plot

  • Import Data into R
  • R programming: What is the Apply family?
  • Using apply()
  • Recreating the apply function with loops (advanced topic)
  • Using lapply()
  • Combining lapply() with []
  • Adding your own functions
  • Using sapply()
  • Nesting apply() functions
  • which.max() and which.min() (advanced topic)

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    SOLD OUT 30 Aug 2026 Weekend SAT - SUN (08 Weeks) 18:00 - 20:00
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    FAQ

    Frequently Asked Questions

    Q: What core R packages are essential for business analytics? A: dplyr, ggplot2, and caret provide data manipulation, visualization, and predictive modeling capabilities. Q: How does business analytics with R improve decision making? A: R enables rapid statistical analysis and interactive dashboards that reveal actionable insights from raw data. Q: Can beginners use business analytics with R without prior programming experience? A: Yes, the syntax is intuitive and tutorials guide users from basic vectors to advanced modeling.

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