Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 1: FOUNDATIONS (Days 1-10)

Day 7-8: Data Wrangling and Processing Fundamentals

These two days focus on the critical data wrangling and processing skills needed to handle the diverse information sources your PAAS will monitor. You'll learn to transform raw data from APIs into structured formats that can be analyzed and stored efficiently. You'll explore techniques for handling different text formats, extracting key information from documents, and preparing data for semantic search and summarization. You'll develop robust processing pipelines that maintain data provenance while performing necessary transformations. You'll also create methods for enriching data with additional context to improve the quality of your system's insights.

  • Morning (3h): Learn data processing techniques

    • Structured vs. unstructured data: Understand the key differences between working with structured data (JSON, XML, CSV) versus unstructured text (articles, papers, forum posts), and develop strategies for both. Learn techniques for converting between formats and extracting structured information from unstructured sources using regex, parsers, and NLP techniques.
    • Text extraction and cleaning: Master methods for extracting text from various document formats (PDF, HTML, DOCX) that you'll encounter when processing research papers and articles. Develop a comprehensive text cleaning pipeline to handle common issues like removing boilerplate content, normalizing whitespace, and fixing encoding problems.
    • Information retrieval basics: Study fundamental IR concepts including TF-IDF, BM25, and semantic search approaches that underpin modern information retrieval systems. Learn how these techniques can be applied to filter and rank content based on relevance to specific topics or queries that will drive your intelligence gathering.

  • Afternoon (3h): Practice data transformation

    • Build text processing pipelines: Create modular processing pipelines that can extract, clean, and normalize text from various sources while preserving metadata about the original content. Implement these pipelines using tools like Python's NLTK or spaCy, focusing on efficiency and accuracy in text transformation.
    • Extract metadata from documents: Develop functions to extract key metadata from academic papers, code repositories, and news articles such as authors, dates, keywords, and citation information. Create parsers for standard formats like BibTeX and integrate with existing libraries for PDF metadata extraction.
    • Implement data normalization techniques: Create standardized data structures for storing processed information from different sources, ensuring consistency in date formats, entity names, and categorical information. Develop entity resolution techniques to link mentions of the same person, organization, or concept across different sources.