Data Analysis in R

Learn the Ins and Outs
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 130 June to 14 July 2018
Recommended course combinationSession 2: Artificial Intelligence For a Healthy LifeWildlife Crime Analysis: Data-Driven Nature Protection
Session 3: Big Data Management and Analysis in Linux, Operations Research: A Mathematical Way to Optimize Your World 
Co-ordinating lecturersAndrea Bassi
Other lecturersDr. Meike Morren
Form(s) of tuitionInteractive seminar
Form(s) of assessmentProgramming assignments, final examination
ECTS3 credits
Contact hours45
Tuition fee€1000
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. Nonetheless, we will briefly go over these topics again to refresh the memory. Affinity with programming is an advantage in learning R. You should bring a computer on which R (latest version) and R desktop (latest version) is installed. We will do all the exercises in a normal room where you will exclusively work on your own computer. If you have doubts about your eligibility for the course, please let us know. Our courses are multi-disciplinary and therefore are open to participants with a wide variety of backgrounds.

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.

We start with descriptive statistics and visual representation of data, which is the first step for most statistical analyses. We then introduce the linear regression model, a widely used model with two main purposes: modeling relationships among the data and predicting future observations. After that we will extend the linear model to the generalized linear framework, in order to analyse non-normally distributed variables. In the second week we focus on a common problem in statistics: classification. We explore the two main areas of classification (supervised learning and unsupervised learning) with theory and examples.

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 various popular R packages, can write your own functions and can use attractive plots to present your data.

Upon successful completion of the course, students will be able to:


• evaluate the quality of quantitative data sources
• choose the appropriate method for analysis, depending on the data source
• conduct various statistical tests
• analyze data using generalized linear framework
• handle multivariate data and classify them into categories
• have developed their skills in programming

Optional tour of “new” Amsterdam, rounded off with a drink.

A_Bassi

Andrea Bassi holds a MSc in Engineering Mathematics (Polytechnic University of Milan), with a focus on Applied Statistics. After having worked in Italy as a statistical consultant, he started his PhD training in Biostatistics at the VU University Medical Center, on the BIOMARKER project. The goal of this project is to design a Bayesian adaptive clinical trial to decide on the optimal targeted treatment strategy for patients with diffuse large B-cell lymphoma (DLBCL). Furthermore, Andrea collaborates with the VU University as a teaching assistant in the area of biostatistics, for bachelor and master programs. His main research interests are Bayesian statistics, statistical programming and decision theory. 

"Students should apply for Data analysis in R to discover the enormous potential of the open-source programming language R and for acquiring a series of skills and tools to analyze statistical problems of diverse nature."


Readings to be provided at the start of the course. For those want to make a start on R: http://tryr.codeschool.com/ .
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.
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
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