Database normalization is the process of organizing data in a database to reduce data redundancy and improve data integrity. This practical guide covers the basics of normalization, including the different normal forms such as 1NF, 2NF, and 3NF.
Data sharding is a fundamental technique in modern database management, providing the means to enhance system performance, scalability, and reliability. This article aims to explore the core principles and practices of data sharding, illuminating the pathway to effective data distribution.
This crash course is designed to provide an in-depth guide to spaCy, an open-source Python library built specifically for advanced NLP. Learn to harness this powerful library for your NLP tasks now.
Data virtualization is a software layer that allows applications to access data from various sources without requiring the data to be moved or copied. It connects data consumers with data sources in real-time. The article provides an introduction to data virtualization concepts, benefits, use cases, architectures, and leading products.
This article provides a checklist of steps and considerations when deploying a data engineering project to production, covering infrastructure setup, testing, monitoring and more. Following this checklist will help ensure a smooth deployment and transition to production systems.
Manual feature engineering remains an integral skill. A hybrid approach combining automation with human fine-tuning offers the ideal path forward.
This article provides a hands-on introduction to PyTorch, covering installation, building a simple linear regression model, data preparation, training, evaluation, and further resources.
This Docker crash course for data scientists covers Docker fundamentals like architecture, images, containers, storage, networking. It then explores using Docker for data science workflows including environments, model training/deployment, notebooks. Finally it discusses best practices for optimization, orchestration, security, and monitoring.
Categorical variables must be encoded before use in scikit-learn models. This article covers 3 of the core strategies and best practices for handling categorical features in machine learning with code examples.
This article provides an overview of the main feature selection techniques available in scikit-learn.
