✅ Explain the fundamental concepts related to computer skills, digital text processing, and their applications.

✅ Analyze the characteristics of text-processing tools and the relationships between their functions and uses.

✅ Apply loops and conditional statements to perform processing operations on textual data.

✅ Use Python commands to open, read, write, and save text files.

✅ Use core Python libraries to organize and process textual data.

✅ Create a simple Python script to perform search and sort operations within text files.

✅ Analyze extracted textual data to reveal its patterns, components, and relationships.

✅ Apply text-standardization procedures through the processing of characters, spaces, symbols, and punctuation.

✅ Use text-normalization techniques to prepare textual data for analysis.

✅ Apply the basic techniques for converting text into numerical representations.

Module A: Foundations: computer skills and the Python ecosystem (Sessions 1–3)

Review of the computer skills required for digital text processing and introduction to the main Python libraries dedicated to text analysis (NLTK, spaCy, gensim, pandas).

Module B: Applied Python programming (Sessions 4–8)

Mastery of control structures (loops, conditionals), file handling, basic libraries, and writing scripts for search, sorting, and the generation of digital reports (PDF, Excel).

Module C: Extraction and analysis of textual data (Sessions 9–10)

Extraction of text from various digital sources and organization of this data into structures suitable for analysis.

Module D: Preparation and numerical representation of texts (Sessions 11–14)

Standardization and normalization of textual corpora, followed by the transformation of texts into vector representations (bag of words, TF-IDF) for analytical use.

Dr. BOUDJEMLINE Haïder

Professor & Lead Instructor

Expert in Algorithms, Modeling, Programming, and Artificial Intelligence at University Mohamed Lamine Debaghine - Setif 2 University.