Table of Contents
Rust Data Science
Return to Data Science, Machine Learning - Deep Learning, Rust Machine Learning, DataOps-MLOps-DevOps, Rust Official Glossary, Rust Topics, Rust, Rust DevOps - Rust SRE, Rust DataOps, Rust MLOps
- Snippet from Wikipedia: Data science
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.
Research It More
Fair Use Sources
Rust: Rust Fundamentals, Rust Inventor: Rust Language Designer: Graydon Hoare on July 7, 2010; Cloud Native Rust https://CloudRust.rs, Rust Wasm - Rust WebAssembly https://WebAssembly.rs, Rust in the Cloud https://CloudRust.io, Rust RFCs https://github.com/rust-lang/rfcs, Rust Scripting, Rust Keywords, Rust Built-In Data Types, Rust Data Structures - Rust Algorithms, Rust Syntax, Rust OOP - Rust Design Patterns https://DesignPatterns.rs https://rust-unofficial.github.io/patterns/rust-design-patterns.pdf, Rust Package Manager (cargo-crates.io - Rust Crates - Rust Cargo), Rust Virtualization, Rust Interpreter, Rust REPL, Rust IDEs (JetBrains RustRover, IntelliJ - CLion with JetBrains Rust Plugins, Visual Studio Code), Rust Development Tools, Rust Linter, Rustaceans https://Rustaceans.rs Rust Users - Rust Programmers, List of Rust Software, Rust Popularity, Rust Compiler (rustc), Rust Transpiler, Rust DevOps - Rust SRE, Rust Data Science - Rust DataOps, Rust Machine Learning, Rust Deep Learning, Functional Rust, Rust Concurrency - Rust Parallel Programming - Async Rust, Rust Standard Library, Rust Testing, Rust Libraries, Rust Frameworks, Rust History, Rust Bibliography, Manning Rust Series, Rust Glossary - Rust Official Glossary, Rust Topics, Rust Courses, Rust Research, Rust GitHub, Written in Rust, Rust Awesome List. (navbar_rust - see also navbar_rust_domains)
Data Science: Fundamentals of Data Science, DataOps, Big Data, Data Science IDEs (Jupyter Notebook, JetBrains DataGrip, Google Colab, JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio), Data Science Tools (SQL, Apache Arrow, Pandas, NumPy, Dask, Spark, Kafka); Data Science Programming Languages (Python Data Science, NumPy Data Science, R Data Science, Java Data Science, C++ Data Science, MATLAB Data Science, Scala Data Science, Julia Data Science, Excel Data Science (Excel is the most popular "programming language") - Google Sheets, SAS Data Science, C# Data Science, Golang Data Science, JavaScript Data Science, Kotlin Data Science, Ruby Data Science, Rust Data Science, Swift Data Science, TypeScript Data Science, Bash Data Science); Databases, Data, Augmentation, Analysis, Analytics, Archaeology, Cleansing, Collection, Compression, Corruption, Curation, Degradation, Editing (EmEditor), Data engineering, ETL/ ELT ( Extract- Transform- Load), Farming, Format management, Fusion, Integration, Integrity, Lake, Library, Loss, Management, Migration, Mining, Pre-processing, Preservation, Protection (privacy), Recovery, Reduction, Retention, Quality, Science, Scraping, Scrubbing, Security, Stewardship, Storage, Validation, Warehouse, Wrangling/munging. ML-DL - MLOps. Data science history, Data Science Bibliography, Manning Data Science Series, Data science Glossary, Data science topics, Data science courses, Data science libraries, Data science frameworks, Data science GitHub, Data Science Awesome list. (navbar_datascience - see also navbar_python, navbar_numpy, navbar_data_engineering and navbar_database)
© 1994 - 2024 Cloud Monk Losang Jinpa or Fair Use. Disclaimers
SYI LU SENG E MU CHYWE YE. NAN. WEI LA YE. WEI LA YE. SA WA HE.