Data Science with R (DataSciR)

News

06.07.2020: The definite schedule for the final presentations is fixed now, please see the table below.
UPDATE 09.07: project website URLs added.

	Date	Time	Title
1	10.07.20	13:00-13:30	Weather Data Analysis: Using Machine Learning Models and Trend Analysis Methods
2	10.07.20	13:30-14:00	Help in Yelp: Decision Support System For New Business Ventures
3	10.07.20	14:00-14:30	Asteroid diameter prediction using NASA jet propulsion lab dataset
4	10.07.20	14:30-15:00	COVID-19 Prediction using Explainable Machine Learning
5	14.07.20	10:00-10:30	Market Research for Supermarket Customer Loyality Improvement (password-protected)
6	15.07.20	10:00-10:30	Multi-label Classification of News Articles on Coronavirus
7	15.07.20	10:30-11:00	Analysis and Predictions of the Impact of COVID-19

Administrative Information

Course day/time: Fri., 13:15-16:45 (building 22A, room 105)
Instructor: Uli Niemann
Course type: seminar
ECTS credits: 6
Audience: all FIN Master degree programs
Course language: english
Registration: see section Application & registration
Prerequisites: see section Prerequisites
Technical requirements: bring your own laptop with R and RStudio installed on it
Grading: based on several deliverables in the context of a semester-long data science project → see Project page

Application & registration

The course is limited to max. 30 students. Please apply for DataSciR as follows:

Register yourself until 03.~~10.~~04.2020 on the LSF
Complete the application form until 03.~~10.~~04.2020.

You will be notified on 05.~~12.~~04.2020 on course admission.

After admission, please complete this registration form from the examination office and hand it to Uli until 20.05.2020.

Course description

Ten years ago, who would have thought, that R, the self-proclaimed “environment for statistical computing and graphics”, would become one of the most popular programming languages for data scientists?

The impressive growth of R is not a coincidence. As free & open-source alternative to expensive & proprietary software like SPSS, Matlab and Excel, R’s strengths have alway been its capabilities for statistical data analysis as well as its functionalities to create powerful, aesthetically appealing graphics and charts.

While R attracted a rather exclusively academic audience in the 90’s & 00’s, the R community since has grown not only by sheer number but also in diversity, as people from different industries and backgrounds discover R’ usefulness for a wide range of applications. As of February 2020, more than 15,000 (!) packages have been published to CRAN, ca. half of them since 2015.

Especially in the last decade, the functionality and versatility of R has gained momentum. The team around Hadley Wickham from RStudio, the public benefit coorporation behind the eponymous IDE specifically made for R, has dedicated to developing R packages with a focus on increasing productivity and reproducibility of the workflows of data scientists, including the highly popular

“tidyverse” packages like dplyr and tidyr for data manipulation,
ggplot2 for data visualization,
rmarkdown and knitr for reproducible & automated reporting,
shiny for interactive web applications, and
tidymodels for inferential and predictive modeling.

In Data Science with R (DataSciR), you will learn fundamentals of R and how to use the above mentioned packages.

Further, you will work on a semester-long graded data science project using R.

Tentative Syllabus

Prerequisites

There are no mandatory prerequisites for DataSciR. However, you are expected to have a profound knowledge of fundamental data mining techniques, such as classification, regression and clustering. Hence, it is recommended that you have heard at least one of the following lectures (or comparable):

Also, you should have a basic programing and statistics knowledge. For example, you will learn the most important vector types and classes in R, but you will not learn what a vector or a class is in general. Accordingly, you should know what the terms mean, standard deviation, probability, hypothesis test, p-value, etc. mean.

Technical Requirements

It is recommended to bring your laptop to each course meeting. Class meetings are a mix of lecture and short coding exercises. You will get the most out of the meetings if you have a laptop and can work on these exercises. Hence, you should set-up your laptop until the end of the first week as described in the Software section.

Software

By the end of the first week, you should have installed the following software on your own laptop:

R (>=3.6.0)
RStudio (>=1.2)
only Windows: Rtools

Also, please check whether you can successfully install packages. To do so, click on the Packages tab in the bottom-right pane in RStudio. Then, click on the Install button and specify an arbitrary package, e.g. dplyr. Finally, click on Install. Alternatively, you can install a package from the console with install.packages("dplyr"). If everything is set up correctly, no error messages should be displayed when you load the installed package with library(dplyr).

List of packages used on slides

We will use the following packages. Install them in one go using the console in RStudio with:

# Install packages from CRAN
cran_pkgs <- c("remotes",
               "tidyverse",
               "tidymodels",
               "gapminder",
               "patchwork",
               "showtext",
               "ggthemes",
               "ggrepel",
               "socviz",
               "ggiraph",
               "ggforce",
               "janitor",
               "rpart",
               "rpart.plot",
               "kknn",
               "rmarkdown",
               "knitr",
               "rticles",
               "xaringan",
               "Hmisc",
               "kableExtra",
               "maps",
               "mapproj",
               "concaveman",
               "AmesHousing")
install.packages(cran_pkgs)

# Install development packages from GitHub
github_pcks <- c("rstudio/gt",
                 "gadenbuie/countdown")
remotes::install_github(github_pcks)

# To generate PDF output from R Markdown documents, you need to install LaTeX. 
# If you have not installed LaTeX before, consider to install the lightweight 
# LaTeX distribution [tinytex](https://yihui.name/tinytex/) which automatically 
# installs missing LaTeX packages when rendering R Markdown documents.
install.packages(tinytex)
tinytex::install_tinytex()

Data Science with `R` (DataSciR)