Data Preprocessing


Mohammadreza Fani Sani


Mohammadreza Fani Sani

Wissenschaftlicher Mitarbeiter


+49 241 80 21908



Organisational Information

Important Dates

Kick-off Meeting:

10.10.2018, 10:00 Uhr – 11:30 Uhr

Raum 6329, Seminarraum Lehrstuhl Process and Data Science, Ahornstraße 55, 52074 Aachen

Seminar Details

The learning objective of this pro-seminar is twofold, first to train students to improve their critical thinking through understanding research methodology and instructing them how to write a scientific paper. Also in this pro-seminar, we will employ data preprocessing as a group of algorithms that enables students to become more familiar with data analysis and data science.

This pro-seminar briefly introduces fundamentals of data preprocessing which is one of the main steps in any data science project that involves transforming raw data into an understandable format. Real data is generally noisy, incomplete, inconsistent, and/or lacking certain behaviours. Data preprocessing comprises a series of techniques that resolve issues and their causes in order to produce more accurate and trustable results.

The following major topics are selected as working materials during this course:

Data cleaning/cleansing

  • Noisy Data
  • Missing Values

Data Integration

  • Redundancy and correlation Analysis
  • Data Value Conflict Detection and Resolution

Data Reduction

  • Attribute Subset Selection
  • Clustering

Data Transformation

  • Normalization
  • Discretization
  • Decision Tree


Each participant will work on one of the above topics and summarize it in a short paper (maximum 15 pages), using LaTeX completed with an oral presentation of 30 minutes.

All topics will be introduced in the introductory session at the start of the pro-seminar. The participation is mandatory throughout the course. In the introductory session topics will be assigned to the students and deadline for submitting the presentations will be discussed.

The language of the course is English; therefore, both the presentations and summaries shall be prepared in English.


Basic computer science skills and knowledge.


References for the specific topics will be given in the first session of pro-seminar.