Data Science Tutorials

Discover how to create simple to complex graphics with Python's turtle module. Learn essential commands and build interactive projects that bring your ideas to life. Explore fractals, animations, and creative designs while mastering Python basics.

Customize the GPT-4o Mini model to classify posts from Reddit into "stressful" and "non-stressful" labels.

Explore the types of structural equation models. Learn how to make theoretical assumptions, build a hypothesized model, evaluate model fit, and interpret the results in structural equation modeling.

Learn how to install Jenkins on Windows, set up and run Jenkins agents and pipelines, and build MLOps projects with Jenkins pipelines from model training to model serving.

Python dlt is a great choice for data engineers who want to move data fast and confidently. It integrates with almost any source and destination and lets you write your ELT/ETL pipelines quickly. This tutorial shows you how to control every step of the pipeline and work with local files, cloud, databases, and REST APIs.

Learn to use Neo4j graph databases with Python: set up AuraDB, write Cypher queries, and ingest data. Master graph database concepts and optimize queries in this comprehensive guide.

Use color_palette() for clear categorical separation, cubehelix_palette() for gradual sequential data, and diverging_palette() for clear divergence from a midpoint.

Learn how to create, customize, and interpret Gantt charts in Power BI to track project timelines and tasks. Follow our detailed instructions and look at real examples.

Learn how to implement DBSCAN, understand its key parameters, and discover when to leverage its unique strengths in your data science projects.

Learn how to access Llama 3.2 lightweight and vision models on Kaggle, fine-tune the model on a custom dataset using free GPUs, merge and export the model to the Hugging Face Hub, and convert the fine-tuned model to GGUF format so it can be used locally with the Jan application.

Learn simple linear regression. Master the model equation, understand key assumptions and diagnostics, and learn how to interpret the results effectively.

Learn the Adagrad optimization technique, including its key benefits, limitations, implementation in PyTorch, and use cases for optimizing machine learning models.