Brendan Patrick Purdy

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.

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.

Data scientists using Python must be comfortable and proficient in using scikit-learn, which is why Flatiron School’s Data Science Bootcamp emphasizes it throughout its curriculum.

After a discussion on the two primary methods of statistical inference, viz. hypothesis tests and confidence intervals, it is shown that these two methods are actually equivalent.

Activation functions play an important role in neural networks and deep learning algorithms. A common activation function is the hyperbolic tangent function, which is like the trigonometric tangent function, but defined using a hyperbola rather than a circle.