Wrangling categorical data in R

Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling

Data Wrangling for Big Data: Challenges and Opportunities

Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield a data set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confront...

Towards Automated Relational Data Wrangling

It is well-known in data science that 80% of the work is devoted to preprocessing and only 20% to the actual machine learning or data mining step. This motivates us to explore different ways to (help) automate that preprocessing step. This note focusses on the question whether it is possible to (help) automate the data wrangling process for tabular data in data science.

Data Wrangling: Making data useful again

Data analysis has become an everyday business and advancements of data management routines open up new opportunities. Nevertheless, transforming and assembling newly acquired data into a suitable form remains tedious. It is often stated, that data cleaning is a critical part of the overall process, but also consumes sublime amounts of time and resources. Data Wrangling is not only about transfo...

Introducing Data Science to Undergraduates through Big Data: Answering Questions by Wrangling and Profiling a Yelp Dataset

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist’s work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper we present hands-on exercises to introduce ...