R Programming for Data Science
The objective of this course is to introduce participants to the data analytics with R programming language, which is a widely used and up-coming statistical programming language. In this course, participants will be exposed to data manipulation with R, produce visualization for data exploration, use some common statistical and machine learning methods to do predictive modelling and finally generate reproducible reports.
- Utilize RStudio, understand R documentation and write R scripts.
- Acquire and manipulate data.
- Produce basic statistical summaries of the data.
- Apply statistical and machine learning models for data analysis and predictive modelling.
- Produce visualization using basic graphics functions and experience the ggplot2 packages.
- Produce reports in R Markdown.
- Overview of R in data analytics
- Installing and setting up R programming environment
- R data types and objects (vectors, lists, matrix and data frame)
- Reading and Writing Data
- List, Matrix, Data Frame
- Sub-setting Data
- Control Structures
- Creating and Using Functions
- Scoping Rules, Manipulating Date and Time
- Laboratory exercise – working with date
- Using the R ‘apply’ functions
- Profiling in R
- Concept of Tidy data in R
- Importing data
- Data Preprocessing with R
- Packages: tidyr and dplyr
- Introduction to data.table
- Introduction to base plotting system
- Introduction to ggplot
- Introduction to Machine Learning and Caret Package I
- Introduction to Machine Learning and Caret Package II
- Introduction to Shiny I
- Introduction to Shiny II
Hands-On Case Study
Lab Exercises will be conducted after each module, including
- Working with data frame
- Working with date
- Data processing with R
- Base plots and ggplot
- Caret package
Drop us your entry if you are interested to join this course.
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