Executive Development Programme in Python NLP: Building a Sentiment Dashboard

This course is designed for business executives and professionals looking to leverage Python Natural Language Processing (NLP) for strategic decision-making. Participants will gain skills in building a sentiment analysis dashboard to monitor public opinion on key brand mentions, products, and services, enhancing their ability to respond to market trends and customer feedback in real-time.

By the end of the program, attendees will be able to implement NLP techniques, such as text preprocessing, sentiment classification, and visualization, using Python libraries like NLTK, spaCy, and Matplotlib. They will also learn to integrate these tools into a cohesive dashboard using frameworks like Streamlit or Dash, enabling them to present insights effectively to stakeholders.

1. 1. Introduction to Python for NLP: Learners will be introduced to Python programming basics and essential libraries for NLP. They will gain foundational coding skills and familiarity with Python environments.
2. 2. Text Preprocessing Techniques: This module covers techniques for cleaning and preparing text data, including tokenization, stemming, lemmatization, and stop word removal. Learners will master text preprocessing steps to enhance NLP model accuracy.
3. 3. Sentiment Analysis Fundamentals: Learners will study the basics of sentiment analysis, including polarity and subjectivity, and analyze text to determine positive, negative, or neutral sentiments. Practical skills include using pre-trained models and creating basic sentiment analysis pipelines.
4. 4. Machine Learning Models for Sentiment Analysis: This module delves into building sentiment analysis models using machine learning techniques such as Naive Bayes, SVM, and logistic regression. Learners will gain hands-on experience in model training and evaluation.
5. 5. Deep Learning for Sentiment Analysis: Learners will explore deep learning approaches for sentiment analysis, including the use of RNNs, LSTMs, and Transformers. Skills developed include implementing and fine-tuning deep learning models for NLP tasks.
6. 6. Building a Sentiment Dashboard: In this module, learners will integrate sentiment analysis models into a user-friendly dashboard. They will learn to visualize sentiment data and present insights effectively using tools like Streamlit or Dash.
7. 7. Text Classification and Named Entity Recognition: This module covers advanced NLP tasks such as text classification and Named Entity Recognition (NER). Learners will develop models to categorize text into predefined categories and extract entities from text.
8. 8. Handling Imbalanced Data in NLP: Learners will study techniques to handle imbalanced datasets in NLP, crucial for achieving accurate sentiment analysis. Practical skills include oversampling, undersampling, and using cost-sensitive learning methods.
9. 9. Real-Time Sentiment Analysis: This module focuses on building real-time sentiment analysis systems. Learners will learn to process and analyze streaming text data, such as tweets or social media posts, and implement continuous sentiment monitoring.
10. 10. Advanced Dashboard Features and Deployment: In the final module, learners will enhance their sentiment dashboard with additional features like automated alerts, sentiment trends over time, and deploy the dashboard to a production environment. They will learn best practices for deploying NLP applications.