• Programming Training

    business_appsInterSource offers live instructor-led courses on all important programming technologies, including C#, C/C#, PHP and Visual Basic. We can also arrange training on many less-known but highly useful languages. We can teach courses on a variety of additional topics; please request an offer if you need a course which is not indicated on the site.

    These live classes are offered both on client sites, at our Geneva training center, and via a Web interface.

  • About Programming

    Within software engineering, programming (the implementation) is regarded as one phase in a software development process, normally following closely on the heels of the requirements gathering phase.

    Computer programming (often shortened to programming or coding) is the process of writing, testing, debugging/troubleshooting, and maintaining the source code of computer programs. This source code is written in a programming language. The code may be a modification of an existing source or something completely new. The purpose of programming is to create a program that exhibits a certain desired behavior (customization). The process of writing source code often requires expertise in many different subjects, including knowledge of the application domain, specialized algorithms and formal logic.

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  • Course Details Programming

    Classes are offered at client sites, at our Geneva training center, and via a live web conference. For detailed course outlines and scheduled classes, please see below.

    To book training, navigate to the course you need, then:

    • For scheduled online classes, register from the choices indicated.
    • If you need an alternative date, time or location, or if you want a live classroom course, click on “request an offer for this course,” to complete the form.

Data Analytics with R Training

Course duration

  • 3 days

Course Benefits

  • Learn the basics of R and Rstudio. 
  • Import and manipulate tabular data with R.
  • Conduct exploratory analysis.
  • Generate rich graphics with GGPlot2.
  • Test for group differences using inferential methods.
  • Build statistical regression models using R.

Course Outline

  1. Course Overview 
    1. Why R? Advantages and disadvantages
    2. Downloading and installing
    3. How to find documentation
  2. Introduction 
    1. Using the R console/RStudio
    2. Getting help
    3. Learning about the environment
    4. Writing and executing scripts
    5. Introduction to vectorized calculations
    6. Introduction to data frames
    7. Installing packages
    8. Working directory
    9. Saving your work
  3. Variable types and data structures 
    1. Variables and assignment
    2. Data types
    3. Numeric, character, boolean, and factors
    4. Data structures
    5. Vectors, matrices, arrays, dataframes, lists
    6. Indexing, subsetting
    7. Assigning new values
    8. Viewing data and summaries
    9. Naming conventions
    10. Objects
  4. Manipulating Data with R
    1. Getting data into the R environment and understanding dataframes 
    2. Built-in data
    3. Overview of dataframes
    4. Reading data from structured text files
    5. Reading data using ODBC
    6. Dataframe manipulation with dplyr 
    7. Renaming columns
    8. Adding new columns
    9. Managing data types
    10. Binning data (continuous to categorical)
    11. Combining categorical values
    12. Transforming variables
    13. Handling missing data
    14. Long to wide and back
    15. Merging datasets together
    16. Stacking datasets together (concatenation)
    17. Handling dates in R 
    18. Date and date-time classes in R
    19. Formatting dates for modeling
  5. Exploratory data analysis (descriptive statistics) including base graphics 
    1. Continuous data
    2. Distributions
    3. Quantiles, mean
    4. Bi-modal distributions
    5. Histograms, box-plots
    6. Categorical data
    7. Tables
    8. Barplots
    9. Group by calculations with dplyr
    10. Split-apply-combine
    11. Long to wide and back, tidy data structures
  6. Advanced graphics in R: using GGPlot 
    1. Understanding the grammar of graphics
    2. Quick plots (qplot function)
    3. Building graphics by pieces (ggplot function)
    4. Understanding geoms (geometries)
    5. Linking chart elements to variable values
    6. Controlling legends and axes
    7. Exporting graphics
  7. Testing for Group differences
    1. Traditional Inferential Statistics, A/B testing
    2. Null hypothesis testing and p-values
    3. Comparing Groups
    4. P-Values, summary statistics, sufficient statistics, inferential targets
    5. T-Tests (equal and unequal variances)
    6. ANOVA
    7. Chi-Square Tests
    8. Correlation
  8. Modeling with R
    1. Frequentist Approaches to multivariable Statistics:
    2. Linear Regression
    3. Multivariate linear regression
    4. Capturing Non-linear Relationships
    5. Comparing Model Fits
    6. Scoring new data

Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this R Programming class:

  • Basic programming background.
Since its founding in 1995, InterSource has been providing high quality and highly customized training solutions to clients worldwide. With over 500 course titles constantly updated and numerous course customization and creation possibilities, we have the capability to meet your I.T. training needs.
Instructor-led courses are offered via a live Web connection, at client sites throughout Europe, and at our Geneva Training Center.