Data Analysis 1a: Foundation of Data Management in R

Term: 
Fall
Credits: 
2.0
Course Description: 

Type: core for PhD in Business Administration, MSc in Business Analytics( full time ), MA in Economic Policy in Global Markets ( full time) 

elective: MA in Global Economic Relations, MSc in Finance  ( full- time)  

Timing: Full time: weekday, Part-time: Saturday

About 80% of data science tasks are composed of managing data, from understanding and altering features of the dataset and variables, to combining various datasets. This course introduces the critical tasks of data wrangling, presentation and understanding of descriptive statistics and basics of visualization. At the same time, the course serves as an introductory class to using R, discussing the use of the R Studio IDE as well as various important packages (data.table, some of the tidyverse libraries, R markdown, ggplot2, Shiny etc). The focus is on learning to read and write data frames; understand and work with objects, columns, vectors, tables, data frame; or define loops and functions; filter, aggregate, summarize and visualize data. Several real life examples will be used to illustrate these libraries in action.

Learning Outcomes: 

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Assessment: 

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Prerequisites: 

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