Objective

R is a great choice for marketing analysis. It offers unique capabilities for fitting statistical models. It is extensible and is able to process data from many different systems, in a variety of forms, for both small and large datasets. The R ecosystem includes the widest available range of established and emerging statistical methods as well as visualization techniques. Yet the use of R in marketing lags other fields such as statistics, econometrics, psychology and bioinformatics. The objective of the course is to fill this gap. Learn the power of R applied to marketing research.

Target group and level of proficiency

The course is targeted at marketing researchers who haven't had any prior touchpoints with R and wanted to start their journey, or to such who have already encountered R language but don't know how to apply that knowledge into practical use within the scope of the marketing research industry.

R applied to marketing research is the first course designed to help research practitioners take advantage of the power of R and significantly increase efficiency and effectivity in their daily work. 

Within three days you will get up to speed with the R essential knowledge. You will understand the logic of the language and you will be able to start applying the power of R into your daily work, significantly increasing the effectivity and efficiency of many processes.

Program  

Day 1 - Wednesday, 21 March 2018

During the first day, you will get introduced to R. You will learn about R community and its immerse, ready-to-use resources. You will discover how to set up R and run a first basic example to illustrate file input and output possibilities, basic data operations and reporting capabilities.

The learning outcomes after the first day:

• Knowing the history and philosophy of R and the R community
• Being able to make an informed choice about the required R toolset - R version (R versus Microsoft R, RStudio versus R Client, etc.)
• Understanding the basics of the R environment, including how and where to find information
• Being able to read and write data in text format and R format
• Being able to perform basic data operations
• Being able to generate a basic report with graphs

Day 2 - Thursday, 22 March 2018

On the second day, you will learn how to work with more complex datasets, how to summarize them and report in a workplace-friendly manner.

The learning outcomes after the second day:

• Being able to read data from more extensive formats like Excel files, SPSS files and databases
• Being able to create scripts using programming logic (for loops, if conditions, and much more)
• Being able to manipulate and summarize data in tables and figures
• Being able to export your work to basic Office Word and Excel documents

Day 3 - Friday, 23 March 2018

During the third day, you will learn how to conduct several popular and daily used techniques. The focus will be set on R functionalities and packages, not on the theoretical background, that's why the training is targeted towards marketing research practitioners.

The learning outcomes after the third day:

• Being able to find and perform general basic statistical tests in R
• Being able to reduce data complexity using Principal Component Analysis, Factor Analysis and Multidimensional Scaling
• Being able to perform and visualize basic clustering techniques
• Being able to perform a basic conjoint analysis in R

If we have extra time we might touch upon two additional topics:

• Being able to perform structural equation modeling using the lavaan package 
• Being able to perform and interpret hierarchical linear models (aka mixed models) in R

Otherwise, they will be addressed in the follow-up training sessions. 

The registration for the training is available until the end of the day (23:59 pm) on Friday, 9th of March 2018.

About the trainer

Joris Meys obtained in 2002 a masters degree in Biology at the University of Antwerp. After working as a researcher, writer and project manager, he obtained a second masters degree, this time in statistical data analysis. Since 2009 he works as a statistician, R programmer and lecturer at the Ghent University. He developed multiple packages and web interfaces for a wide variety of applications. Over the years he has given numerous workshops R and statistics for both researchers and companies. In 2012 he published the first edition of R for Dummies together with Andrie de Vries. The revamped second edition arrived in 2015.