Data Science

"When I found Flatiron School, I was excited about the opportunity to level up my coding skills and gain a deeper understanding of machine learning and AI."

Predictive modeling in data science involves using statistical algorithms and machine learning techniques to build models that predict future outcomes or behaviors based on historical data. It encompasses various steps, including data preprocessing, feature selection, model training, evaluation, and deployment.

Natural Language Processing (NLP) encompasses a variety of techniques designed to enable computers to understand and process human languages. In this post you’ll learn about NLP applications like text classification and sentiment analysis, plus NLP techniques like tokenization and stemming.

Learning to effectively collaborate with co-workers in a remote work setting is a critical skill to hone. Read on to find out how Flatiron’s AI-geared hackathon is helping the school’s graduates sharpen this skill—plus many others.

Smooth bank integrations and rock-solid security are just two things people want most from an AI personal finance platform. Read on to learn the other six, and find out how Flatiron’s Hackonomics hackathon teams will incorporate all eight into their Money Magnet final projects.

You should only use data science terms such as mean, median, standard deviation, correlation, and hypothesis testing if you are confident in being able to explain them. This post walks readers through explanations of some of the most common data science terms.