Data Analysis in R

Learn the Ins and Outs

Course full: no longer possible to apply

With the increasing use of alternative software packages like R in data analysis, now is the time to learn their ins and outs. The large number of active programmers creating R packages makes this an up-to-date programme providing a huge range of statistical analyses. Researchers also use R to write functions for analysing data, or to create professional plots.
Course levelAdvanced Bachelor/Master, open to PhD staff and professionals
Session 214 July to 28 July 2018
Recommended course combination
Session 1: Programming in Python
Session 3: Big Data Management and Analysis in Linux
Co-ordinating lecturersDr Meike Morren
Other lecturersAndrea Bassi
Form(s) of tuitionInteractive seminar
Form(s) of assessmentProgramming assignments, final examination
ECTS3 credits
Contact hours45
Tuition fee€1000
Students or professionals with an interest in quantitative data analysis using R. We will use examples from Economics, Social Sciences and Biostatistics. No programming experience is required. PhD students wishing to refresh their knowledge are also welcome. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to students with a wide variety of backgrounds.
A completed undergraduate course in statistics and an acquaintance with basic linear algebra, the fundamentals of hypothesis testing, linear regression analysis and statistical tests such as the t-test.
This course focuses upon understanding statistical models and analysing the results whilst learning to work with R. As well as introducing the software to newcomers, it presents basic and more advanced statistics using an overarching framework of the generalized linear model. We start with descriptive statistics and simple regression, before moving on to multiple regression.
Many problems in data analysis are related to dimension reduction, from data mining problems such as classification to analyzing survey answers. You will learn how to reduce data dimensions using principal component analysis and how to analyse multi-item scales using confirmatory factor analysis. Additionally, you learn how to treat missing data in various models. Lastly we will introduce how to create and adjust plots in R. Every day consists of short lectures with examples, and exercises in which you apply what you have learned right away. Each week you are supposed to make an assignment which is graded. The focus in the exercises and assignment is the coding in R and how to apply and to interpret generalized linear regression models.
By the end of the two weeks you are acquainted with numerous basic functions available in R can write your own functions and can use attractive plots to present your data.

At the end of this course you can:

  • Evaluate the quality of quantitative data sources.
  • Choose the appropriate method for an analysis, depending upon the data source.
  • Conduct various statistical tests.
  • Analyse data using generalized linear framework.
  • Decide when and how to use dimension reduction.
  • Conduct and interpret factor analysis.
  • Enjoy your developed programming skills.
Optional extracurricular bicycle tour of “new” Amsterdam, rounded off with a drink.
Readings to be provided at the start of the course. For those want to make a start on R: http://tryr.codeschool.com/.
Bernard"I think this is an important course to take because in the 21st century Data is power. You see companies such as google and Facebook that do a lot of data collection and to make use of that data you have to analyze it, so this is a good course to introduce those techniques. It's interesting because the Dutch have historically been aware of the importance of analyzing data. During the Golden Era, one of the reasons the Dutch East India company had such strong cartographers (map makers), and what they did was they had these cartographers travel and explore. Collecting data for future traders so that they could know the best spots to trade and that's how they used data during that time to advance their business. The importance of data has only grown today, whether you're running a technological company or a marketing company and therefore I found the course very practical." -Bernard Wong

M_Morren

Meike Morren has been an Assistant Professor of Marketing at VU University Amsterdam since 2012. She was trained as a sociologist and researcher at University of Amsterdam (UvA) and obtained her master degree by completing the research master in Social Sciences in 2006. In 2011, She defended her PhD in Methods and Statistics at Tilburg University treating a mixed methods study on the quality of survey questions. Since graduation, she focuses on green behavior and data quality in surveys in general. She has been published in Journal of Environmental Psychology, Sociological Methodology, Methodology, Cross Cultural Research and Field Methods. She teachs courses at bachelor and master level in statistics and methodology. The main topics are inferential statistics, survey methods, sampling methods, cluster analysis, factor analysis and regression analysis. While in most courses she uses SPSS syntax, in some courses she uses R.

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