Data Wrangling

2019-2020

Course Objective

The course is geared toward getting data ready for its end purpose.
After this course, the student should be able to:
1. acquire data from different online and offline sources,
2. understand how to clean and pre-process data,
3. transform data for analytics purposes,
4. perform feature engineering,
5. visualize data.​

Course Content

Data wrangling is the process of gathering data in its raw form and
molding it into a form that is suitable for its end use. This course is
about how to gather the data that is available and produce an output
that is ready to be used. There are a number of common steps in the data
wrangling process that will be discussed: acquiring, cleaning, shaping
and structuring the data, as well as feature engineering and
visualization.​

Teaching Methods

Lectures (6 x 2 hours) and Q&A sessions (3 x 2 hours).

Method of Assessment

The final grade is determined by hand-in assignments (30% of the final
grade), a presentation
and a report (70% of the final grade). Both parts have to be passed with
a grade that is at least 5.5. There are no resit options for this
course.

Literature

Slides

Target Audience

3BA, 3LI, 3CS, 3IMM

Recommended background knowledge

Programming experience in Python.

General Information

Course Code XB_0014
Credits 6 EC
Period P3
Course Level 300
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. S. Bhulai
Examiner prof. dr. S. Bhulai
Teaching Staff prof. dr. S. Bhulai

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: