Data Preprocessing

1. Brief Introduction

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.

Data Types

Data Processing Methods

Dataset: Test and Training

Deleting an "Extra" Data

Changing the Data Type

2. Processing Quantitative Data

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.

Data Scaling

Data Scaling vs Data Normalization

Removing Outliers

Removing Missing Values

Data Augmentation: Synthetic Data

3. Processing Categorical Data

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

Methods for Encoding the Categorical Data

One-Hot Encoding

Ordinal Encoding

Label Encoding of the Target Variable

Challenge

4. Time Series Data Processing

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.

Train/Test Split & Cross Validation

Challenge

5. Feature Engineering

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.

Technique Idea

Realization

Feature Extraction from Text

Feature Extraction from Images

Feature Extraction from Time Series

Challenge

6. Moving on to Tasks

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

Challenge 1

Challenge 2

Challenge 3

Changing the Data Type

You already know how to change the data type from string to number, for example. But let's take a closer look at this small but important task.

Let's start by changing the data type from string to datetime. Most often, you will need this to work with time series. You can perform this operation using the .to_datetime() method:

To convert a string to a bool - use the .map() method on the column whose values you want to change:

For example, if you have a price column that looks like "$198,800" and you want to turn it into a float - you should create custom transformation functions:

Task

Read the sales_data_types.csv dataset and change the data type in the Active column from str to bool.

Everything was clear?

Section 1. Chapter 5

Course Content

Data Preprocessing

1. Brief Introduction

Data Types Data Processing Methods Dataset: Test and Training Deleting an "Extra" Data Changing the Data Type

2. Processing Quantitative Data

Data Scaling Data Scaling vs Data Normalization Removing Outliers Removing Missing Values Data Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical Data One-Hot Encoding Ordinal Encoding Label Encoding of the Target Variable Challenge

4. Time Series Data Processing

Data Type Conversion Data Cleaning Stationarity Denoising Train/Test Split & Cross Validation Challenge

5. Feature Engineering

Technique Idea Realization Feature Extraction from Text Feature Extraction from Images Feature Extraction from Time Series Challenge

6. Moving on to Tasks

Challenge 1 Challenge 2 Challenge 3

Data Preprocessing

Changing the Data Type

You already know how to change the data type from string to number, for example. But let's take a closer look at this small but important task.

Let's start by changing the data type from string to datetime. Most often, you will need this to work with time series. You can perform this operation using the .to_datetime() method:

To convert a string to a bool - use the .map() method on the column whose values you want to change:

For example, if you have a price column that looks like "$198,800" and you want to turn it into a float - you should create custom transformation functions:

Task

Read the sales_data_types.csv dataset and change the data type in the Active column from str to bool.

Everything was clear?

Section 1. Chapter 5