NumPy Crash Course for Data Scientists
Learn the essentials of NumPy, a cornerstone in data science and machine learning. Master array operations, broadcasting, vectorization, and more.
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Beautiful Soup Crash Course for Data Scientists
Explore the ins and outs of web scraping with Beautiful Soup. This guide covers basics to advanced topics, including parsing, tree navigation, asynchronous scraping, and data management.
Performance Tuning in SQL: Tips and Techniques
Performance tuning in SQL databases is an essential skill for database administrators and developers alike. This article provides a comprehensive guide to optimizing SQL queries and database structures, focusing on best practices, practical techniques, and specific examples.
Building Scalable and Maintainable REST APIs for Data Services
Introduction As applications become more data-driven, RESTful APIs have emerged as a popular way to build interfaces that enable diverse client apps to interact with backend data and services. Well-designed REST APIs power the data backends of web, mobile, IoT, and other applications. They provide a standardized way to expose data and functionality over HTTP…
Database Normalization: A Practical Guide
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.
Understanding Data Sharding
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.
spaCy Crash Course for Data Scientists
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.
An Overview of Data Virtualization
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.
Deploying a Data Engineering Project to Production: A Checklist
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.
Is Feature Engineering a Dying Art?
Manual feature engineering remains an integral skill. A hybrid approach combining automation with human fine-tuning offers the ideal path forward.
PyTorch: A Quick & Dirty Intro
This article provides a hands-on introduction to PyTorch, covering installation, building a simple linear regression model, data preparation, training, evaluation, and further resources.
Docker Crash Course for Data Scientists
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.