Data Science & AI Code-Alongs

In this session you'll learn how to use DataLab to seamlessly access Google Sheets data and analyze the data with Python. Practice with exploratory data analysis tasks like crunching numbers and drawing visualizations for Google Analytics data.

In this session, Nuno, DataCamp's Director of Engineering, demonstrates how to make use of GitHub Copilot. You'll see how to perform a simple data analysis in conjunction with AI, and learn how to make the most of Copilot's features.

In this session, you'll learn fundamental concepts of data pipelines, like what they are and when to use them, then you'll get hands-on experience building a simple pipeline using Python.

In this session, you'll learn how to use the Weaviate vector database to store these different content types, then perform search queries on them.

In this introductory session, you'll explore a survey on business innovation. The focus will be on principles of analyzing survey data, and you'll write some simple SQL for calculations and simple Python for data visualizations.

In this session, you'll learn how to use ChatGPT to perform a data analysis and assemble the results into a data story.

In this code-along, Andrea Valenzuela, Computing Engineer at CERN, and Josep Ferrer Sanchez, Data Scientist at the Catalan Tourist Board, will walk you through building an AI system that can query your documents & data using LangChain & the OpenAI API.

In this code along, you'll learn how to use the OpenAI API and Python to get started fine-tuning GPT3.5.

In this code-along, you will be introduced to the basics of exploring new datasets. Using data on UK Premiership soccer results, you will use Python to calculate summary statistics and draw visualizations to generate insights.

In this session you'll learn how to get started with Chroma and perform Q&A on some documents using Llama 2, the RAG technique, and LlamaIndex.

In this webinar, you'll learn how to build your own diffuser model from scratch!

In this session, we cover the different evaluations that are useful for reducing hallucination and improving retrieval quality of LLMs.