Here is the list of A-Z of essential data science concepts:
A: Algorithm – A set of rules or instructions for solving a problem or completing a task.
B: Big Data – Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification – A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining – The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning – A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering – The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent – An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing – A statistical method used to make inferences about a population based on sample data.
I: Imputation – The process of replacing missing values in a dataset with estimated values.
J: Joint Probability – The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering – A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression – A statistical model used for binary classification tasks.
M: Machine Learning – A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network – A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection – The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall – Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis – The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis – A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine – A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis – The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning – Machine learning techniques used to identify patterns and relationships in data without labelled outcomes.
V: Validation – The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka – A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost – An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn – A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model – A statistical model used to analyze data with excess zeros, commonly found in count data.
