Microsoft, in collaboration with Quantide, is offering a one-day live course in Milano: a wide overview of R as a data science tool, in a free class, open to everybody.
Info, dates and topics of our five Autumn R Courses: from programming to data manipulation, from statistics to data mining, everything with R.
Move forward from being a R user to become a R developer. Discover from an inner perspective the R working mechanisms and master your R programming skills.
Discover Data Mining with R: find patterns in large data sets using the R tools for Dimensionality Reduction, Clustering, Classification and Prediction.
A live class for beginners: learn the basics of R, and get an overview on methods for data import, data manipulation, data visualization and data analysis.
How to apply one or many functions to one or many variables using dplyr: a practical guide to the use of summarise() and summarise_each()
Organize your data manipulation tasks in a standard way, write clean and efficient code, and build reproducible data management processes, using the most modern R tools: tidyr, dplyr and lubridate.
A week ago, my boss at Quantide, Andrea Spanò, published Ramarro. Ramarro is a free interactive web based book (i.e. a web-book) about advanced R programming. The book would help R users to become proficient R developers. Target of the book are both R users and IT specialists that should deals with R users.
Dear R users,
the May 2014 public training course schedule for Milano (Italy) based courses is as follows:
|Web Applications with R and Shiny||May 15, 2014|
|Reports in R with RStudio||May 16, 2014|
|Basic R Programming||May 22, 2014|
|Data Visualization with R||May 23, 2014|
"Statistical Models with R" Course
March 27 and 28, 2014
This two-day course shows a wide variety of statistical models with R ranging from Linear Models (LM) to Generalized Linear Models (GLM) modelling, in order to provide a broad overview of statistical linear models with R.
The course will follow a step-by-step approach from simplest to more complex models to illustrate the R capabilities on modeling. Some theoretical introductions are given in course materials.
Brief introductions to modeling with Mixed Effects, GAM, Neural Networks and Trees are also provided.