Tools for Data Science - Glossary

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  • Apache MLlib: A language that makes machine learning scalable.

  • Apache Spark: A general-purpose cluster-computing framework allowing you to process data using compute clusters.

  • API: Application Programming Interface allows communication between two pieces of software.

  • Caffe: A deep learning algorithm repository built with C++, with Python and Matlab bindings.

  • CDLA: Community Data License Agreement.

  • Classification Models: Are used to predict whether some information or data belongs to a category (or “class”).

  • CLI: Command Line Interface.

  • C++: A general-purpose programming language. It is an extension of the C programming language or C with Classes.

  • Dataset: A structured collection of data.

  • Deeplearning4: Language for deep learning.

  • Deep Learning: A specialized type of machine learning. It refers to a general set of models and techniques that loosely emulate the way the human brain solves a wide range of problems.

  • ELT: Extract, Load, Transform.

  • ETL: Extract, Transform, and Load.

  • FSF: Free Software Foundation.

  • ggplot2: A popular library for data visualization in R.

  • GPU: Graphics Processing Units.

  • Git: De facto standard for code asset management, also known as version management or version control. Around Git emerged several services, GitHub, and GitLab.

  • Hadoop: Application of Java which manages data processing and storage for big data applications running in clustered systems.

  • Java: Object-oriented programming language.

  • Java-ML: Language for machine learning.

  • JVM: Java Virtual Machine.

  • JavaScript: A general-purpose language that extended beyond the browser with the creation of Node.js and other server-side approaches.

  • Julia: A language for high-performance numerical analysis and computational science.

  • Jupyter Notebook: A browser-based application that allows you to create and share documents containing code, equations, visualizations, narrative text links, and more.

  • JupyterLab: A browser-based application that allows you to access multiple Jupyter Notebook files, other code, and data files.

  • Kernel: An execution environment for the different programming languages.

  • Lattice: It is a high-level data visualization library that can handle graphics without customizations.

  • Library: A collection of functions and methods that allow you to perform many actions without writing the code.

  • Leaflet: Used for creating interactive plots.

  • ML: Machine learning uses algorithms – also known as “models” - to identify patterns in the data.

  • Matplotlib: Package for data visualization.

  • Model Training: The process by which the model learns patterns from data.

  • MNIST: Modified National Institute of Standards and Technology.

  • MongoDB: A NoSQL database for big data management that was built with C++.

  • NLP: Natural Language Processing.

  • NLTK: Natural Language Toolkit.

  • NumPy: Libraries are based on arrays and matrices, allowing you to apply mathematical functions to the arrays.

  • OSI: Open-Source Initiative.

  • PaaS: Platform as a service.

  • Pandas: A library that offers data structures and tools for effective data cleaning, manipulation, and analysis.

  • Plotly: Used for web-based data visualizations that can be displayed or saved as individual HTML files.

  • PMML: Predictive Model Markup Language.

  • Python: A high-level, general-purpose programming language. It has a large, standard library that provides tools suited to many different tasks, including Databases, R.

  • Regression Models: Are used to predict a numeric (or “real”) value.

  • Reinforcement Learning: Loosely based on the way human beings and other organisms learn.

  • REST: RE stands for Representational; the S stands for State, and the T stands for Transfer.

  • RStudio: A fully integrated development environment for data scientists.

  • SaaS: Software as a service.

  • Scala: Is a combination of scalable and language. A general-purpose programming language that provides support for functional programming and is a strong static type system.

  • Spyder: Integrates code, documentation, and visualizations, among others, into a single canvas.

  • SQL: Structured Query Language that is non-procedural, used for querying and managing data.

  • Supervised Learning: A learning in which a human provides input data and correct outputs.

  • TensorFlow: Deep Learning library for dataflow that was built with C++.

  • Unsupervised Learning: The data is not labeled by a human. Examples are Clustering models used to divide each record of a dataset into one of a similar group.

  • Watson Studio: Language for data mining.

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