📅 2023-05-02 — Session: Developed exercises for unstructured data processing
🕒 20:25–20:35
🏷️ Labels: Python, Unstructured Data, Data Processing, Image Processing, Text Analysis
📂 Project: Teaching
⭐ Priority: MEDIUM
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.