Data Science With Genai Analytics: Python + Hugging Face

From Raw Data to AI-Powered Insights: Building Modern Analytics Pipelines with Python and Transformers

What you'll learn

Describe the core concepts of Generative AI and its role in enhancing data science workflows, including the generation of synthetic data.

Analyze unstructured text data using Hugging Face Transformers to perform sentiment analysis and extract key insights.

Design and implement a Python pipeline for data prep, sentiment analysis, and LLM summarization to generate and validate analytical reports programmatically.

Evaluate ethical considerations and best practices for responsible implementation of Generative AI in data science projects.

Requirements

Python programming fundamentals

Basic use of Jupyter notebooks or similar

Familiarity with AI language tools (e.g., understanding of what an LLM does)

Basic understanding of data structures and manipulation (pandas)

Description

Every day, businesses generate mountains of unstructured text data—from customer reviews to support tickets—and most of it goes underused. What if you could turn all that raw text into structured insights and automated reports in minutes, using Generative AI?Welcome to Generative AI for Data Science: Build Smart Analytics with GenAI! I’m Prof. Reza (Dr. Reza Moradinezhad), an AI scientist and educator with over a decade of experience in machine learning, computer science, and human computer interaction—having collaborated with teams at MIT Media Lab, CMU, and Harvard. In this course, I’ll guide you through a hands-on, project-based course where you’ll build an end-to-end Generative AI data pipeline using Python, Hugging Face Transformers, and real-world datasets.You’ll learn how to analyze movie reviews, generate synthetic data, extract insights with LLMs, and automate analytical reporting—step by step, with practical code examples and guided walkthroughs. Unlike typical GenAI courses that stay theoretical, this one is all about real application.We’ll work with the Rotten Tomatoes dataset, build in Jupyter Notebooks, and apply pre-trained models for sentiment analysis, text summarization, and report generation.Every step you learn is transferable to your own data science projects, across domains. By the end, you won’t just understand how to use GenAI—you’ll have built a fully functional analytics tool powered by it. Whether you're a data scientist, AI engineer, or a curious analyst, you’ll leave with the skills and confidence to turn any text-heavy dataset into automated insights. Let’s dive in—and start transforming how we do data science, with Generative AI at the core.1 Main Outcome:By the end of this course, you will be able to design and implement a Python-based Generative AI pipeline for analyzing unstructured text data, generating insights, and creating automated analytical reports.TOOLS USEDIn this course, all the tools work in concert to create a comprehensive generative AI workflow for:You'll leverage Python and its powerful Pandas library as your foundational environment for data handling, from initial loading and exploration of review data to the creation of simple, illustrative synthetic tabular data that enriches your analytical context. Hugging Face Transformers and Datasets will serve as your direct gateway to cutting-edge AI, providing seamless access to pre-trained models for automated sentiment analysis, text summarization, and key insight extraction, while also efficiently loading the large Rotten Tomatoes dataset. All these components will come together within Jupyter Notebooks, providing an interactive and flexible environment where you'll build, visualize, and refine your end-to-end Generative AI analytics pipeline step by step.Python + Pandas – Core tools for data loading, exploration, manipulation, and generating simple synthetic tabular data (e.g., simulated movie attributes or tiers).Hugging Face Transformers + Datasets – Provides access to pre-trained models for sentiment analysis, summarization, and keyword extraction, as well as efficient loading of the Rotten Tomatoes dataset.Jupyter Notebooks – The interactive coding environment used to integrate all tools, visualize intermediate results, and build the complete GenAI analytics pipeline.

Overview

Section 1: Foundations & Kickstarting Your E-commerce GenAI Project

Lecture 1 What you're going to get from this course

Lecture 2 What is Generative AI for Data Science?

Lecture 3 The Power of Synthetic Data & Why Hugging Face?

Lecture 4 Setting Up & Loading Movie Review Data

Lecture 5 Automated Sentiment Analysis for Review Data

Section 2: Text Analysis for Your Movie Review Project: Sentiment & Key Insights

Lecture 6 Visualizing Sentiment and Preparing for Generative AI

Lecture 7 Preparing Data for Your Report

Lecture 8 Constructing Your AI-Powered Product Report (Core Content)

Lecture 9 Outputting & Presenting Your E-commerce Analytics Report

Section 3: Building & Reviewing Your Concise Movie Review Analytics Report

Lecture 10 Project Blueprint Review: Your Streamlined Pipeline

Lecture 11 Optimizing & Iterating on Your GenAI Pipeline (Part 1)

Lecture 12 Responsible AI & Final Remarks

Lecture 13 Optimizing & Iterating on Your GenAI Pipeline (Part 2)

Lecture 14 Conclusion and Closing Statements

Python developers and data scientists looking to integrate Generative AI into real-world analytics workflows.,Data analysts who want to automate insight extraction and generate AI-powered summaries from unstructured text.,ML engineers and AI enthusiasts exploring hands-on applications of LLMs and Hugging Face Transformers.,Students, researchers, and product professionals seeking practical skills in GenAI-powered reporting and decision support.