29,258 technical terms and definitions
Data Science in Medicine & Biomedical Analysis: Statistical Learning and Clinical Applications 1. Epidemiology and Statistical Analysis Epidemiology studies disease distribution…
data serialization formats, data serialization, serialization formats
Data Serialization Formats Introduction & Motivation Data Serialization Formats addresses a central problem in programming process control and format-overhead-size prediction: how…
documentation
Data sheets for datasets is the dataset documentation framework that records origin, composition, collection process, and ethical constraints - it provides provenance and context…
distributed training
Data shuffling at scale is the large-distributed randomization of sample order to prevent correlation bias during training - it must balance statistical randomness quality…
Data Smells Automated Antipattern Static Analysis Introduction & Motivation Data Smells Automated Antipattern Static Analysis addresses a central problem in (subject) assurance…
first-party second-party third-party data, batch vs streaming data, schema drift, data lineage
A data source is any origin point from which data enters an analysis or system — a production database, a third-party API, a sensor feed, a file upload, a web scrape, a survey…
Data Standardization and Normalization Introduction & Motivation Data Standardization and Normalization addresses a central problem in (subject) assurance and (target) management…
narrative arc setup conflict resolution, audience-aware communication, statistical significance vs narrative clarity
Data storytelling is the practice of communicating an analysis's findings through narrative structure — framing a conclusion around a clear point, supported by the most relevant…
arrays, strings, linked lists, stacks, queues, hash tables, trees, graphs, binary search tree
A data structure is a particular way of arranging the addresses and pointers from the previous topic so that specific operations can be done efficiently, and most of…
arrays vs linked lists, hash table explained, stacks and queues, intro trees and graphs
A data structure is simply a particular way of arranging the addresses from the previous topic so that a specific set of operations — adding an item, finding one, removing one…
legal
Data subject rights are the legal rights granted to individuals under GDPR (and similar regulations) regarding the personal data that organizations collect and process about them.…
nlp
Data-to-text is the NLP task of generating natural language descriptions from structured data — automatically converting tables, databases, knowledge bases, and other structured…
ETL vs ELT, pivoting, aggregation, dbt data build tool, idempotent transformation
Data transformation is the process of converting data from one format, structure, or representation into another so it fits the shape a destination system, model, or analysis…
quantitative vs categorical data, discrete vs continuous data, nominal vs ordinal data, type conversion, one-hot encoding
A data type defines what kind of value a variable, field, or piece of data can hold and what operations are valid on it — integer, floating-point number, string, boolean, date…
Data Type and Format Consistency Introduction & Motivation Data Type and Format Consistency addresses a central problem in (subject) assurance and (target) management: how…
type range format referential uniqueness validation, shift-left validation, Great Expectations, data quality monitoring
Data validation is the process of checking that data conforms to expected rules — correct type, valid range, required fields present, referential consistency — before it's…
Data Completeness Validation Introduction & Motivation Data Completeness Validation addresses a central problem in (subject) assurance and (target) management: how to Improve…
Data Validation Rules and Constraints Introduction & Motivation Data Validation Rules and Constraints addresses a central problem in (subject) assurance and (target) management…
Data Valuation for Active Learning Introduction & Motivation Data Valuation for Active Learning addresses a central problem in (subject) learning and efficient (target)…
dataset versioning, data version control, dvc, lakefs, data lineage versioning
Data versioning definition and system boundary. Data versioning records the identity, content, metadata, parentage, and lifecycle of datasets so a team can reproduce what existed…
data versioning and reproducibility, data versioning reproducibility, data versioning, versioning reproducibility
Data Versioning and Reproducibility Introduction & Motivation Data Versioning and Reproducibility addresses a central problem in ml-ops process control…
Cleveland and McGill perceptual hierarchy, Edward Tufte data-ink ratio chartjunk, misleading charts truncated axis
Data visualization is the representation of data through visual elements — position, length, color, shape — specifically because the human visual system can detect patterns…
cloud data warehouse, enterprise data warehouse, analytical database, olap warehouse
Data warehouse definition and system boundary. A data warehouse is a centralized analytical database that publishes structured, governed, historically consistent data for complex…
data warehouse olap, data warehouse, warehouse olap
Data Warehouse OLAP Introduction & Motivation Data Warehouse OLAP addresses a central problem in systems process control and query-latency-optimization prediction: how to Predict…
data2vec General Self-Supervised Learning Across Modalities Introduction & Motivation data2vec General Self-Supervised Learning Across Modalities addresses a central problem…
DBMS, ACID properties, relational database, NoSQL, CAP theorem, database indexing, normalization
A database is an organized, persistent collection of data managed by software — a database management system (DBMS) — that handles storage, retrieval, concurrent access…
database backup recovery plan, database backup recovery, backup recovery plan, database backup, backup recovery, recovery plan
Database Backup Recovery Plan Introduction & Motivation Database Backup Recovery Plan addresses a central problem in cloud process control and backup-strategy-full-incremental…
database connection pooling, database connection, connection pooling
Database Connection Pooling Introduction & Motivation Database Connection Pooling addresses a central problem in ml-inference process control and connection-acquisition-latency…
database migration strategy, database migration, migration strategy
Database Migration Strategy Introduction & Motivation Database Migration Strategy addresses a central problem in ml-ops process control and migration-time-hours prediction: how…
database migration strategy, database migration, migration strategy
Database Migration Strategy Introduction & Motivation Database Migration Strategy addresses a central problem in cloud process control and migration-downtime-zero-downtime…
tool use
Database querying is structured retrieval of information from databases using generated query operations - The model constructs queries against schemas retrieves records…
database replication consistency, database replication, replication consistency
Database Replication Consistency Introduction & Motivation Database Replication Consistency addresses a central problem in ml-inference process control…
database replication consistency, database replication, replication consistency
Database Replication Consistency Introduction & Motivation Database Replication Consistency addresses a central problem in cloud process control…
database sharding strategy, database sharding, sharding strategy
Database Sharding Strategy Introduction & Motivation Database Sharding Strategy addresses a central problem in cloud process control and shard-key-distribution-balance prediction…
Relational Databases, Transactions, Concurrency, and B+ Trees A database management system turns shared persistent storage into a dependable abstraction. Applications issue…
data modeling, relational tables, SQL, primary key, index, transaction, ACID
A database is the organized, persistent storage sitting behind the HTTP response from the previous topic, and this very article is a real row inside one. Data modeling is…
lakehouse, mlflow
Databricks is the unified data intelligence platform founded by the creators of Apache Spark that combines data engineering, data warehousing, and machine learning — pioneering…
architecture, deep, learning, processing
Dataflow is an execution model in which a computation is expressed as a graph of operations, and each operation runs as soon as its input data is available, rather than in a fixed…
computing, paradigm, architecture, execution
Dataflow Computing Paradigm is an execution model where computation is driven by data availability rather than program counter sequencing, enabling massive parallelism through…
dataflow accelerator architecture design, dataflow accelerator architecture, accelerator architecture design, dataflow accelerator, accelerator architecture, architecture design
Dataflow Accelerator Architecture Design Introduction & Motivation Dataflow Accelerator Architecture Design addresses a central problem in ml-hardware process control…
dataflow analysis in compilers, dataflow analysis compilers, dataflow analysis, analysis compilers
Dataflow Analysis in Compilers Introduction & Motivation Dataflow Analysis in Compilers addresses a central problem in compiler-optimization process control and analysis-precision…
dataflow programming, dataflow graph, stream processing, dataflow execution
Dataflow architecture definition and engineering boundary. executes an operation when its required data tokens are available rather than advancing primarily through a single…
spatial computing hardware, coarse grain reconfigurable, cgra dataflow, dataflow processor design
Dataflow Architecture Computing is the processor design paradigm where instructions execute as soon as their input operands are available (data-driven execution) rather than…
dataflow computing and architectures, dataflow computing architectures, dataflow computing, computing architectures
Dataflow Computing and Architectures Introduction & Motivation Dataflow Computing and Architectures addresses a central problem in dataflow-computing process control…
wave computing, spatial architecture computing, coarse grain reconfigurable array cgra, stream dataflow architecture
Dataflow Processor Architecture: Spatial Computing via Coarse-Grained Reconfigurable Arrays — compute elements directly mapped to hardware nodes with data-driven execution model…
corpus, training data
Training Data for LLMs Pretraining Datasets Large language models are pretrained on massive text corpora—often trillions of tokens from diverse sources. Common Pretraining Sources…
data quality
Dataset Bias refers to systematic errors or skews in training data that cause models to learn unintended, misleading patterns — the model captures the bias in the data rather than…
Dataset Cartography and Data Maps for Training Dynamics Introduction & Motivation Dataset Cartography and Data Maps for Training Dynamics addresses a central problem…
distributed training
Dataset sharding is the partitioning of training data into non-overlapping subsets assigned across distributed workers - it ensures balanced workload distribution, minimizes…
mlops
Dataset versioning is the practice of creating immutable, traceable dataset snapshots for every training and evaluation run - it ensures model results can be reproduced even when…