R is an integrated suite of software components designed for professional statistical computation, data analysis and data visualization.

R comprises:

1. The R programming language, designed specifically for working with statistical analysis and data analysis;
2. The R environment, designed for effective handling and storage of large data sets; and
3. A massive on-line repository of R packages which cover advanced statistical modeling, machine learning, data visualization and data manipulation tools, as well as packages that support analyses in specific domains such bio-statistics, financial modeling, population studies and computational physics.
This webinar is designed for data scientists and business professional who want to use R in their professional environments and need to learn how to use R effectively in the shortest possible time by developing a solid conceptual understanding of both how R works and how to produce effective results in the most efficient manner.

Four main topic areas covered (briefly are):

1. The R environment. How to customize the R environment and use the various help and assistance tools to support working with R and simplify analysis workflows.
2. Basic R programming. How to write simple, efficient and effective R scripts to automate the data analysis workflow.
3. R Data manipulation. How to use the R data structures, such as data frames, to access, store, arrange and organize data sets.
4. R Data analysis. How to use the core R facilities for statistical analysis and graphics for data visualization.

About your Instructor:

Rod Davison has been providing customized training and executive development in the high technology field for over 25 years.  His expertise spans the cloud, artificial intelligence / machine learning, agile and project management fields.  He has developed extensive content in a variety of subjects, including for a wide array of online courses. Rod’s recent experience includes delivering various courses on:

  • Advanced Software Design and Development
  • Artificial Intelligence, Cognition, non-linear systems, behavioral economics
  • Research in bioengineering, biological computing
  • Project Management and Project Forensics
  • Corporate training (including Azure, DevOps, AWS…)
  • Development of structured educational methodologies
  • Content developer for courses in a variety of subject and technical areas
  • Market research and social dynamics
  • Data management, quality, design, modeling, analysis and engineering
  • Advanced programming in emerging languages: OO, functional and structured.
  • Software testing and quality
  • Development methodologies (including Agile and Design Thinking)

 

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