Chapter Overview#

Chapter 2: Data Manipulation#

This chapter covers the essential skills for manipulating geoscientific data in machine learning. It covers a wide range of topics, from data definition, modalities, formats (including cloud optimized), best standard for preparing AI-ready data sets. The chapter also covers basic skills for data manpilulation, such as stastical assessments, data transforms (Fourier, wavelets), creating synthetic signals, feature engineering, and the foundamental concept of data dimensions.

Topics Covered#

  1. Data Definition and Modalities in Geoscience

    • Understanding different types of data in geoscience

    • Data modalities and their characteristics

    • Common data formats relevant to geoscience, and computational consideration for cloud storage

  2. Basic Skills for Manipulating Tabular Data

    • Introduction to Pandas

      • DataFrames and Series

      • Datetime objects

      • Basic operations: filtering, grouping, and aggregating

    • Handling missing data and data cleaning

    • Building data pipelines

  3. Manipulating Array Data

    • Introduction to NumPy

      • Arrays and their operations

      • Indexing, slicing, and reshaping arrays

    • Working with multi-dimensional arrays

  4. Statistical Distributions and Field Transforms

    • Understanding statistical distributions

    • Field transforms

      • Fourier Transform

      • Wavelet Transform

    • Creating synthetic noise for data augmentation

  5. Dimensionality Reduction and Feature Engineering

    • Techniques for dimensionality reduction

      • Principal Component Analysis (PCA)

      • t-Distributed Stochastic Neighbor Embedding (t-SNE)

    • Feature engineering

      • Creating new features from existing data

      • Selecting the most relevant features

  6. Best Practices for AI-Ready Curated GeoDatasets

    • Ensuring data quality and consistency

    • Techniques for data normalization and standardization

    • Strategies for splitting data into training, validation, and test sets

Learning Outcomes#

By the end of this chapter, you will:

  • Gain a solid understanding of different data types, modalities, and formats relevant to geosciences.

  • Develop basic skills for manipulating tabular and array data using Pandas, NumPy, and PyTorch.

  • Learn how to apply statistical distributions and field transforms to your data.

  • Master techniques for dimensionality reduction and feature engineering.

  • Understand best practices for preparing AI-ready and ML-ready curated datasets.

Assignments#

  • Final Assignment: The final assignment for this chapter is to build an AI-ready dataset for your final project. This will involve applying the skills and techniques learned throughout the chapter to curate a high-quality dataset that can be used for machine learning or AI applications.