DAI 501 – Data Science and AI Applications

A set of vocabulary flashcards based on key concepts in Data Science and AI applications, designed to aid in exam preparation.

Data Science

The primary goal is to turn data into actionable insights and solve complex problems.

Data Munging (Data Wrangling)

The process of cleaning, transforming, and structuring raw data.

Bayes’ Theorem

A way of calculating the probability of an event based on prior knowledge of conditions related to the event.

Expected Value

The mean of all possible values of a random variable, calculated as the sum of the values multiplied by their probabilities.

Correlation

Measures the strength and direction of a relationship between two variables.

Clustering

An unsupervised learning method used to group data points based on their characteristics.

Regression

A type of machine learning used to predict numerical outcomes based on input features.

Pandas DataFrame

A two-dimensional labeled data structure with columns of potentially different types.

NumPy Array

A grid of values, all of the same type, indexed by a tuple of non-negative integers.

Machine Learning Goal

To enable computers to learn without being explicitly programmed.

Anomaly Detection

The identification of rare items or events in a dataset that differ significantly from the majority of the data.

Dropping Rows with Missing Values

The method 'df.dropna()' is used to remove any rows containing missing values.