**Data preprocessing** occurs to prepare a dataset for the corresponding tasks. The preprocessing pipeline can be very different depending on the data you will be working with. 

The main steps are:

- Data cleaning;
- Dimensionality reduction;
- Data conversion;
- Integration of data from different sources into one dataset.


Each of the stages includes many methods, for example, data cleaning includes working with missing data, which can be replaced by generating synthetic data, deleting rows with missing data, or filling them with an average over the entire column, etc.


Most models accept only processed data as input (images converted to matrices, text converted to vectors, etc.), but many modern models based on the Transformer architecture like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre- trained Transformer) models can handle text data without converting it into numerical representations.

Despite this, inside the model, the data is still transformed into a numerical representation because only with such data can mathematical operations be performed.


Creating a machine learning model seems to be your most challenging and essential task. But first, we have to work with data! Learn how to process datasets and fully prepare them for use. Numerical, categorical, and temporal data await you in our course.

Different types of data? How to work with them? If your eyes are wide open, don't worry, let's start with a brief overview of the pandas library and learn how to work with it in the future.

This chapter discusses in detail how to work with quantitative data, what methods it is processed with, how data scaling and normalization differ, and much more.

Is categorical data as simple as you think it is? Find out what is the complexity of processing and working with it.


Time series data processing is the process of handling, analyzing, and preparing data that is presented as a sequence of temporally ordered values. Find out what steps it includes in this section.

Did you know that you can extract even more values from your data and create more informative features? In this section, you will learn how to work with feature engineering.

You have reached the end of this course. Let's test your knowledge! There are 3 tasks for you to solve.

Data Preprocessing

Data Processing Methods

Data Preprocessing

Data Processing Methods