Blog | AI and Data Science Articles

Python is the most popular programming language for data science. In this guide, we explain how DataCamp can help upskill your teams in Python with our engaging and flexible educational solutions.

Learn how dbt-utils enhances dbt with pre-built macros, key features, and practical use cases. Streamline your data transformations with our step-by-step guide.

The article provides a comprehensive overview of the characteristics, types and methods to analyze nominal variables.

Data lineage is the systematic tracking and documentation of data's origins, transformations, and movements within a system or across systems.

Learn how to achieve data accuracy, consistency, and governance with master data management best practices.

We take a grounded look at the applications of AI in the banking industry: credit risk modeling, fraud detection, customer churn, and customer service bots. Discussing the challenges faced in these areas reveals why the progress of AI in this industry may be slowed.

Explore how text embeddings work, their evolution, key applications, and top models, providing essential insights for both aspiring & junior data practitioners.

Explore the key differences between two popular business intelligence tools: Looker Studio and Power BI. Determine which one is best fit for your needs.

Explore the key differences between Power BI and Alteryx in user interface, integration, cost, and learning curve to determine the best fit for your needs.

The Artificial Intelligence (AI) development surge has created a notable increase in computing demands, driving the necessity for robust hardware solutions. Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) have emerged as pivotal technologies in addressing these demands.

Codestral is Mistral AI's first open-weight generative AI model designed for code generation tasks, automating code completion, generation, and testing across multiple languages.

Reinforcement learning from AI feedback (RLAIF) leverages AI models to provide feedback during LLM training, enhancing performance and scalability.