Data Science

Data Preparation

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Data Preparation

Lecture type


Course code






Lecturers and associates

Course objectives

For the data analysis to have high quality results, it is necessary to make the preparation of the input data. The aim of the course is to demonstrate basic methods of data preparation that includes methods of cleaning, transforming, introverting, normalizing and aggregating data, time series transformation, work with missing values as well as basic data reduction methods such as feature reduction, sample reduction, and discretization.


Introduction to data preparation. Data cleaning. Work with missing values. Data Transformation. Sample Reduction. Aggregation of data. Transformation of time series. Data Integration. Normalization of data. Data discretization. Feature Reduction. Practice and Future. Exam preparation.

Required reading

Course handbook prepared and printed by Algebra University College

Additional reading

1. Salvador García, Julián Luengo, Francisco Herrera : Data Preprocessing in Data Mining (2016)
2. Appavu Balamurugan S., Arockia Christopher A.B.: Insight into Data Preprocessing: Theory and Practice: Data Mining Perspective (2012)
3. Soumen Chakrabarti, Earl Cox, Eibe Frank, Ralf Hartmut Güting, Jiawei Han, Xia Jiang, Micheline Kamber, Sam S. Lightstone: Data Mining: Know It All (2009)

Minimal learning outcomes

  • Address issues while preparing data.
  • Differentiate working methods with missing values and data transformation methods.
  • Differentiate the basic aggregation functions and methods of time series transformation.
  • Differentiate potential problems in the process of integration, normalization and data discretization and to know their potential solutions.
  • Differentiate the basic methods of reducing features and patterns.
  • List tools and technologies for data preparation in Big Data environments

Preferred learning outcomes

  • Recommend solutions for problems while preparing data.
  • Choose an adequate method for working with missing data and method of data transformation.
  • Select the appropriate aggregation functions and methods of time series transformation.
  • Select an adequate solution for a particular problem in the process of integration, normalization and data discretization.
  • Apply adequate basic methods of reducing features and patterns.
  • Understand the impact of new technologies on the process of data preparation
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