Undergraduate Certificate in Natural Language Processing: Python for Data Cleaning

This course is designed for undergraduate students and professionals with a foundational understanding of programming who wish to specialize in natural language processing (NLP) using Python. Participants will learn essential Python libraries and techniques for data cleaning, preprocessing, and analysis, which are critical for NLP tasks.

Upon completion, students will be able to clean and preprocess textual data effectively, apply NLP techniques to extract meaningful insights, and develop basic NLP applications. The course equips learners with practical skills in Python programming and NLP, preparing them for careers in data science, AI, and related fields.

1. 1. Introduction to Natural Language Processing (NLP): Learners will study the basics of NLP, including text preprocessing, tokenization, and part-of-speech tagging. They will gain foundational skills in understanding and applying these concepts using Python.
2. 2. Data Cleaning for NLP: This module focuses on the challenges of cleaning unstructured text data, covering techniques such as removing noise, handling missing values, and standardizing text formats. Learners will develop practical skills in text cleaning and preprocessing.
3. 3. Python Libraries for Text Processing: Learners will explore popular Python libraries like NLTK and spaCy, learning how to use them for text analysis, tokenization, and other NLP tasks. They will gain proficiency in selecting and applying appropriate tools for different text processing needs.
4. 4. Regular Expressions for Text Manipulation: This module covers the use of regular expressions in cleaning and transforming text data. Learners will understand how to apply regex patterns for advanced text manipulation and gain hands-on experience in text cleaning tasks.
5. 5. Advanced Text Cleaning Techniques: Building on basic techniques, this module delves into more complex methods such as lemmatization, stemming, and stop-word removal. Learners will learn to implement these techniques effectively and understand their impact on data quality.
6. 6. Text Normalization and Standardization: Learners will study methods for normalizing text, including case normalization, handling numbers and dates, and standardizing abbreviations. They will gain skills in preparing text data for further analysis and machine learning tasks.
7. 7. Sentiment Analysis and Opinion Mining: This module introduces learners to sentiment analysis, teaching them how to extract opinions, emotions, and sentiments from text data. They will apply their text cleaning skills to perform sentiment analysis on real-world datasets.
8. 8. Entity Recognition and Extraction: Learners will learn about named entity recognition (NER) and extraction techniques, including person, location, and organization recognition. They will practice using NLP tools to identify and extract entities from text data.
9. 9. Text Classification and Topic Modeling: This module covers text classification and topic modeling techniques, such as using machine learning algorithms to classify text into predefined categories or topics. Learners will apply their text cleaning skills to preprocess text data for these advanced NLP tasks.
10. 10. Practical Project: Full Text Pipeline: In this capstone project, learners will apply all the skills they have learned to design and implement a complete text cleaning pipeline. They will work on a real-world dataset, from data collection to text cleaning, and prepare the data for downstream NLP tasks.