Overview
The hands-on R Programming for data scientists and analysts training course is a five day course that familiarizes participants with the commonly encountered scenarios in a data scientist and data analyst’s work. The course also helps participants discover effective solutions to the challenges faced while performing data analysis. The course also covers the data science theory as well as the AI grouping theory. The participants would also learn about using R with AI libraries such as Madlib.
What You'll Learn
- The R environment
- R and Mathematics
- Working with Vectors
- Working with dates and times
- R and Data Science
- R and Madlib
- R and Hadoop
- Data visualization tools and examples
Curriculum
- Common problems with Excel
- The R environment
- Hello, R
- Simple Math with R
- Working with Vectors
- Functions
- Comments and code structure
- Using packages
- Vector properties
- Creating, combinging and iterating
- Passing and returning vectors in functions
- Logical vectors
- Text manipulation
- Factors
- Working with dates
- Date formats and formatting
- Time manipulation and operations
- Adding a second dimension
- Indices and named rows and columns in a matrix
- Matrix calculations
- n-Dimensional arrays
- Data frames
- Lists
- AI grouping theory
- K-means
- Linear regression
- Logistic regression
- Elastic net
- Importing and exporting static data (CSV, Excel)
- Using libraries with CRAN
- K-means with Madlib
- Regression with Madlib
- Other libraries
- Powerful data through visualization: Communicating the message
- Techniques in data visualization
- Data visualization tools
- Examples
- Overview of Hadoop
- Overview of distributed databases
- Overview of Pig
- Overview of Mahout
- Exploiting Hadoop cluster with R
- Hadoop, Mahout and R
- Rule systems in the enterprise
- Enterprise service busses
- Drools
- Using R with drools
Who should attend
The course is highly recommended for –
- Data scientists
- Data analysts
- Business analysts
- Big Data professionals
- Aspiring machine learning professionals
Prerequisites
Participants need to have experience working in data scientist/analyst roles and a thorough understanding of the concepts and techniques involved in data analysis.
