Data Science Tutorials

The Poisson distribution models the probability of a certain number of events occurring within a fixed interval. See how it's applied in real-world scenarios like queueing theory and traffic modeling.

Learn three methods to migrate data from Amazon DynamoDB to Redshift, with step-by-step instructions, the pros and cons of each method, and best practices for data integrity and performance optimization.

Learn how to set up Replit Agent and discover how to use it through an example walkthrough and 10 real-world use cases.

A winsorized mean reduces the influence of outliers by capping extreme values at specific percentiles, preserving the overall structure of the dataset. Read further to learn how to calculate the winsorized mean using Python for hands-on practice.

Learn how the AUC-ROC curve assesses binary classification models, focusing on performance across thresholds, particularly in imbalanced datasets. Use Python’s libraries to compute AUC values and compare classifiers in one workflow.

Learn how to set up AWS Glue, create a crawler, catalog your data, and run jobs to convert CSV files into Parquet format, optimizing your ETL processes.

Learn the key components of the ARIMA model, how to build and optimize it for accurate forecasts in Python, and explore its applications across industries.

Reflection Llama-3.1 70B, trained with Reflection-Tuning, claims to surpass GPT-4o and Claude 3.5 Sonnet but has faced reproducibility and verification issues so far.

Learn how to effectively use VBA in Excel to automate tasks, create macros, and enhance your data processing skills with practical examples and best practices.

Explore how using a SQL alias simplifies both column and table names. Learn why using a SQL alias is key for improving readability and managing complex joins.

Discover how CatBoost simplifies the handling of categorical data with the CatBoostClassifier() function. Understand the key differences between CatBoost vs. XGBoost to make informed choices in your machine learning projects.

Learn to build a RAG application with Llama 3.1 8B using Ollama and Langchain by setting up the environment, processing documents, creating embeddings, and integrating a retriever.