Data Science (DS)

These terms cover a wide range of concepts, processes, and techniques fundamental to the field of data science, from data preparation and exploration to the application of machine learning models for extracting insights and making predictions.

Accuracy - A fundamental metric in data science used to measure the correctness of a model's predictions.

Anomaly Detection - The identification of unusual patterns that do not conform to expected behavior, widely used in data science for fraud detection, network security, and anomaly detection in time series data.

Association - A method in data mining to discover the probability of the co-occurrence of items in a collection.

Automated Machine Learning (AutoML) - The process of automating the tasks of applying machine learning to real-world problems.

Big Data - Refers to extremely large datasets that cannot be analyzed with traditional data processing techniques, a key area of focus in data science.

Classification - A data science technique where models are trained to categorize inputs into predefined classes or groups.

Clustering - An unsupervised learning technique used in data science to group similar data points together based on their characteristics.

Convolutional Neural Network (CNN) - A deep learning algorithm which can take in an input image, assign importance to various aspects/objects in the image and be able to differentiate one from the other.

Cross-Validation - A model validation technique in data science used to assess how the results of a statistical analysis will generalize to an independent dataset.

Data Cleaning - The process of preparing data for analysis by removing or correcting data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted.

Data Exploration - The initial phase in data analysis where data scientists use statistical summaries and visualization techniques to understand the characteristics and relationships within the data.

Data Lake - A storage repository that holds a vast amount of raw data in its native format until needed, often used in data science for big data storage and analysis.

Data Visualization - The graphical representation of data, an essential part of data science for communicating findings and insights effectively.

Deep Learning (DL) - A class of machine learning algorithms that use several layers of neural networks to extract progressively higher level features from the raw input.

Dimensionality Reduction - A process used in data science to reduce the number of input variables in a dataset, simplifying models without losing significant information.

Evaluation Metric - Criteria used in data science to assess the performance of a model or algorithm, such as accuracy, precision, recall, and F1 score.

Feature Engineering - The process of selecting, modifying, or creating new features from raw data, a critical step in improving the performance of data science models.

Feature Learning - Techniques that allow a system to automatically discover the representations needed from raw data for detection or classification.

Image Recognition - The process of identifying and detecting an object or a feature in a digital image or video.

Label - In supervised learning, a label is the target variable that a model is trained to predict, based on the input features.

Machine Learning (ML) - A core component of data science, focusing on developing algorithms that allow computers to learn from and make predictions or decisions based on data.

Regression - A statistical method used in data science for modeling and analyzing the relationships between dependent and independent variables.

Reinforcement Learning (RL) - A type of machine learning technique that enables an algorithm to learn through trial and error using feedback from its own actions and experiences.

Self-Supervised Learning - This technique enables models to learn patterns from unlabeled data, crucial for leveraging large datasets without extensive manual annotation.

Semi-Structured Data - Data that does not reside in a relational database but has some organizational properties that make it easier to analyze, common in data science.

Structured Data - Data that adheres to a pre-defined data model and is easy to analyze. Structured data includes numbers, dates, and strings.

Supervised Learning - A type of machine learning where the model is trained on a labeled dataset, which includes both the input data and the correct output.

Train vs. Test - In data science, this refers to the practice of dividing a dataset into a training set used to train a model, and a test set used to evaluate its performance.

Unlabeled Data - Data that lacks explicit labels, making it suitable for unsupervised learning tasks in data science, such as clustering or dimensionality reduction.

Unstructured Data - Data that does not have a pre-defined data model, often text-heavy and requiring special processing to derive value from it, a common challenge in data science.

Unsupervised Learning - A type of machine learning where the model is trained on data without explicit answers, used in data science for clustering, association, and dimensionality reduction tasks.