Developed exercises for unstructured data processing

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Developed exercises for unstructured data processing

  • Day: 2023-05-02
  • Time: 20:25 to 20:35
  • Project: Teaching
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Python, Unstructured Data, Data Processing, Image Processing, Text Analysis

Description

Session Goal

The session aimed to develop and outline exercises for processing unstructured data using Python, focusing on both text and image data.

Key Activities

  • Reviewed fundamentals of data processing, including data manipulation, visualization, and storage techniques.
  • Developed exercises for loading and processing unstructured data types such as text files, images, and audio files using Python libraries like Pillow, Librosa, and requests.
  • Created practical exercises for image classification using pre-trained convolutional neural networks.
  • Designed exercises for text processing, including tokenization, sentiment analysis, and feature extraction using libraries such as NLTK, spaCy, and TextBlob.

Achievements

  • Successfully outlined a comprehensive set of exercises covering various techniques for handling unstructured data.
  • Integrated multiple Python libraries to demonstrate diverse data processing methods.

Pending Tasks

  • Further refinement of exercises to include more advanced techniques and real-world data scenarios.

Evidence

  • source_file=2023-05-02.sessions.jsonl, line_number=0, event_count=0, session_id=b31bce1ad791fb72dc6e31d845c574103df690a6b2a0fac533700c3cb576b21e
  • event_ids: []

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