Platforms & Workbenches
9 entriesWhere data science work happens.
01Data Science PlatformsIntegrated platforms for data science and machine learning.
02Data Science IDEsNotebook environments and integrated development tools.
03Python Libraries for Data ScientistsPython libraries for data manipulation and modelling.
04Machine Learning ModelsLogistic regression, random forests, CatBoost and tabular foundation models such as TabPFN-3.5.
05Data MiningTechniques, open-source and commercial software, datasets, books and conferences for discovering patterns in data.
06Text MiningNLP libraries, topic modeling, entity extraction, sentiment, document parsing and text analytics platforms.
07AutoML ToolsTools that automate feature engineering, model selection and tuning.
08MLOps PlatformsExperiment tracking, deployment and lifecycle management.
09Explainable AI (XAI) FrameworksSHAP, LIME and other tools for explaining model behavior.
Data: Ingest, Prepare & Train
12 entriesGetting data ready for models.
10Data Wrangling ToolsCleaning, transforming and preparing raw data.
11Data Sets for AI Model TrainingPublic datasets for training and benchmarking.
12Feature Engineering: Approaches & ToolsTurning raw data into model-ready features: techniques, libraries, feature stores and data quality tools.
13Top Technologies for Data EngineeringThe nine technologies that changed data engineering: Pandas, Parquet, Spark, Airflow, object storage, cloud warehouses, Arrow, Iceberg and DuckDB.
14Feature Store SolutionsStoring, sharing and serving ML features.
15Data QualityData quality tools and practices.
16Time-Series Forecasting ModelsMoving averages, ARIMA, Prophet and other forecasting models.
17Time Series Foundation ModelsTimestral, Chronos, TimesFM and other forecasting foundation models.
18Tabular Foundation ModelsTabPFN, TabICL and TabFM compared with gradient boosting.
19Python Libraries for Time Series Forecastingsktime, Darts, StatsForecast and foundation model wrappers.
20Causal AI ModelsCausal inference, discovery and learning resources.
21Evaluation Metrics for Deep LearningMetrics for classification, regression, detection and generation.
Databases
5 entriesStorage for structured and embedding data.
22NoSQL DatabasesKey-value, graph and other non-relational databases.
23Vector DatabasesDatabases for embeddings and AI search.
24Database SystemsDatabase systems hub.
25Time Series Databases (TSDBs)Databases for storing and querying time series data.
26Graph DatabasesNeo4j, TigerGraph, Memgraph and other graph databases.
Learning & Providers
2 entriesLearn the field or find a provider.
Related Pages
7 entriesNeighbouring directories.
29Machine LearningMachine learning hub.
30Data AnalyticsData analytics directory.
31Data EngineeringData engineering hub.
32Data Governance PlatformsGovernance for data platforms.
33StatisticsFree textbooks and courses, open data, software such as R and statsmodels, societies and journals.
34Predictive AIForecasting and predictive AI resources.
35Big Data ComputingHadoop, Spark, Flink, Kafka and big data platforms.
Frequently Asked Questions
What is data science?
Data science combines statistics, programming and domain knowledge to extract insight and build predictive models from data.
What tools do data scientists use?
Python or R with notebooks, libraries such as pandas and scikit-learn, SQL databases and platforms for training and deploying models.
How is data science different from data engineering?
Data engineers build and maintain the pipelines and storage; data scientists analyse the data and build models. See the Data Engineering hub.
Where do I find datasets?
See Data Sets for AI Model Training for repositories such as UCI, Kaggle and Hugging Face.
Where do I start learning?
Websites to Learn Data Science lists free courses, books and tutorials.