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course content

Course Content

Data Preprocessing

1. Brief Introduction

Data TypesData Processing MethodsDataset: Test and TrainingDeleting an "Extra" DataChanging the Data Type

2. Processing Quantitative Data

Data ScalingData Scaling vs Data NormalizationRemoving OutliersRemoving Missing ValuesData Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical DataOne-Hot EncodingOrdinal EncodingLabel Encoding of the Target VariableChallenge

4. Time Series Data Processing

Data Type ConversionData CleaningStationarityDenoisingTrain/Test Split & Cross ValidationChallenge

5. Feature Engineering

Technique IdeaRealizationFeature Extraction from TextFeature Extraction from ImagesFeature Extraction from Time SeriesChallenge

6. Moving on to Tasks

Challenge 1Challenge 2Challenge 3

Data Preprocessing

Data TypesData Types

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:

As you understand, each dataset can contain many different data types, for example, numeric (integers, floating point numbers), strings (str), and datetime. To find out what data type a column has, you can call the .dtypes property:

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

Once you've completed this task, click the button below the code to check your solution.

Everything was clear?

Section 1. Chapter 1
toggle bottom row
course content

Course Content

Data Preprocessing

1. Brief Introduction

Data TypesData Processing MethodsDataset: Test and TrainingDeleting an "Extra" DataChanging the Data Type

2. Processing Quantitative Data

Data ScalingData Scaling vs Data NormalizationRemoving OutliersRemoving Missing ValuesData Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical DataOne-Hot EncodingOrdinal EncodingLabel Encoding of the Target VariableChallenge

4. Time Series Data Processing

Data Type ConversionData CleaningStationarityDenoisingTrain/Test Split & Cross ValidationChallenge

5. Feature Engineering

Technique IdeaRealizationFeature Extraction from TextFeature Extraction from ImagesFeature Extraction from Time SeriesChallenge

6. Moving on to Tasks

Challenge 1Challenge 2Challenge 3

Data Preprocessing

Data TypesData Types

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:

As you understand, each dataset can contain many different data types, for example, numeric (integers, floating point numbers), strings (str), and datetime. To find out what data type a column has, you can call the .dtypes property:

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

Once you've completed this task, click the button below the code to check your solution.

Everything was clear?

Section 1. Chapter 1
toggle bottom row
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