Data Science Advance | 2. Data Wrangling and Feature Engineering by Abraham | Learn Smarter
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2. Data Wrangling and Feature Engineering

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Sections

  • 2

    Data Wrangling And Feature Engineering

    Data wrangling and feature engineering are essential processes in data science that involve cleaning, transforming, and organizing raw data for analysis and improving model accuracy.

    Learning Practice

  • 2.1

    Understanding Data Wrangling

    Data wrangling is the process of cleaning and transforming raw data into a usable format for analysis.

    Learning Practice

  • 2.1.1

    What Is Data Wrangling?

    Data wrangling is the process of cleaning and transforming raw data into a format suitable for analysis.

    Learning Practice

  • 2.1.2

    Importance Of Data Wrangling

    Data wrangling is crucial for ensuring high-quality data, leading to improved model accuracy and reliability.

    Learning Practice

  • 2.1.3

    Common Data Wrangling Steps

    This section outlines the essential steps of data wrangling, focusing on how to clean, transform, and organize raw data for analysis.

    Learning Practice

  • 2.2

    Handling Missing Values

    This section discusses the types of missing values in data and techniques to handle them.

    Learning Practice

  • 2.2.1

    Types Of Missingness

    This section discusses the types of missing data in datasets, specifically MCAR, MAR, and MNAR, and their implications for data analysis.

    Learning Practice

  • 2.2.2

    Techniques To Handle Missing Data

    This section covers various techniques for addressing missing data, including deletion, imputation, and predictive models.

    Learning Practice

  • 2.3

    Data Transformation Techniques

    This section covers various techniques for transforming and preparing data to enhance its usability for analysis and modeling.

    Learning Practice

  • 2.3.1

    Normalization And Standardization

    Normalization and standardization are critical data transformation techniques used to scale numerical data, ensuring better performance in machine learning models.

    Learning Practice

  • 2.3.2

    Log Transformation

    Log transformation is a technique used to compress skewed data, making it more suitable for analysis.

    Learning Practice

  • 2.3.3

    Binning

    Binning is the process of converting numeric data into categorical bins to simplify data analysis.

    Learning Practice

  • 2.3.4

    One-Hot Encoding

    One-hot encoding is a technique used to convert categorical variables into a binary format, making them suitable for machine learning models.

    Learning Practice

  • 2.3.5

    Label Encoding

    Label encoding is a technique used to convert categorical variables into numerical format, facilitating the application of machine learning algorithms.

    Learning Practice

  • 2.4

    Feature Engineering

    Feature engineering involves the creation and modification of variables to improve model outcomes in data science.

    Learning Practice

  • 2.4.1

    What Is Feature Engineering?

    Feature engineering is the process of creating or modifying variables to enhance the performance and interpretability of machine learning models.

    Learning Practice

  • 2.4.2

    Why Is It Important?

    Feature engineering is essential in improving model accuracy and aiding algorithms to detect better patterns.

    Learning Practice

  • 2.5

    Types Of Feature Engineering Techniques

    This section explores various feature engineering techniques, focusing on extraction, transformation, selection, and construction.

    Learning Practice

  • 2.5.1

    Feature Extraction

    Feature extraction is the process of deriving new features from raw data to enhance machine learning models.

    Learning Practice

  • 2.5.2

    Feature Transformation

    Feature transformation involves altering the distribution of features to enhance model performance.

    Learning Practice

  • 2.5.3

    Feature Selection

    Feature selection is the process of identifying and selecting the most relevant features from a dataset to improve model performance.

    Learning Practice

  • 2.5.4

    Feature Construction

    Feature construction is the process of creating new, meaningful features from existing data to enhance model performance.

    Learning Practice

  • 2.6

    Dealing With Outliers

    This section discusses how to detect and treat outliers in datasets, which is crucial for ensuring robust analysis.

    Learning Practice

  • 2.6.1

    Detection Techniques

    Detection techniques help identify outliers in datasets.

    Learning Practice

  • .2.6.2

    Treatment Options

    The section on Treatment Options discusses methods for addressing outliers in data.

    Learning Practice

  • 2.7

    Data Pipelines

    Data pipelines automate the processes of data wrangling and feature engineering to enhance reproducibility and scalability.

    Learning Practice

  • 2.8

    Tools And Libraries For Data Wrangling And Feature Engineering

    This section covers essential tools and libraries used for data wrangling and feature engineering, highlighting their purposes in data manipulation and machine learning workflows.

    Learning Practice

  • 2.3

    Summary

    Data wrangling and feature engineering are essential steps in data science for preparing and optimizing data for analysis.

    Learning Practice

References

ADS ch2.pdf

Class Notes

Memorization

Revision Tests