Table of Contents
Julia Data Science
Return to Data Science, Machine Learning - Deep Learning, Julia Machine Learning, DataOps-MLOps-DevOps, Julia Official Glossary, Julia Topics, Julia, Julia DevOps - Julia SRE, Julia DataOps, Julia MLOps
- Snippet from Wikipedia: Data science
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, 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
Julia: Julia Fundamentals, Julia Inventor - Julia Language Designer: Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah in February 14, 2012; Julia DevOps - Julia SRE, Cloud Native Julia - Julia on Kubernetes - Julia on AWS - Julia on Azure - Julia on GCP), Julia Microservices, Julia Containerization (Julia Docker - Julia on Docker Hub), Serverless Julia, Julia Data Science - Julia DataOps - Julia and Databases (Julia ORM), Julia ML - Julia DL, Functional Julia (1. Julia Immutability, 2. Julia Purity - Julia No Side-Effects, 3. Julia First-Class Functions - Julia Higher-Order Functions, Julia Lambdas - Julia Anonymous Functions - Julia Closures, Julia Lazy Evaluation, 4. Julia Recursion), Reactive Julia), Julia Concurrency - Julia Parallel Programming - Async Julia, Julia Networking, Julia Security - Julia DevSecOps - Julia OAuth, Julia Memory Allocation (Julia Heap - Julia Stack - Julia Garbage Collection), Julia CI/CD - Julia Dependency Management - Julia DI - Julia IoC - Julia Build Pipeline, Julia Automation - Julia Scripting, Julia Package Managers, Julia Modules - Julia Packages, Julia Installation (Julia Windows - Chocolatey Julia, Julia macOS - Homebrew Julia, Julia on Linux), Julia Configuration, Julia Observability (Julia Monitoring, Julia Performance - Julia Logging), Julia Language Spec - Julia RFCs - Julia Roadmap, Julia Keywords, Julia Data Structures - Julia Algorithms, Julia Syntax, Julia OOP (1. Julia Encapsulation - 2. Julia Inheritance - 3. Julia Polymorphism - 4. Julia Abstraction), Julia Design Patterns - Julia Best Practices - Julia Style Guide - Clean Julia - Julia BDD, Julia Generics, Julia I/O, Julia Serialization - Julia Deserialization, Julia APIs, Julia REST - Julia JSON - Julia GraphQL, Julia gRPC, Julia Virtualization, Julia Development Tools: Julia SDK, Julia Compiler - Julia Transpiler, Julia Interpreter - Julia REPL, Julia IDEs (JetBrains Julia, Julia Visual Studio Code), Julia Linter, Julia Community - Juliaaceans - Julia User, Julia Standard Library - Julia Libraries - Julia Frameworks, Julia Testing - Julia TDD, Julia History, Julia Research, Julia Topics, Julia Uses - List of Julia Software - Written in Julia - Julia Popularity, Julia Bibliography - Julia Courses, Julia Glossary - Julia Official Glossary, Julia GitHub, Awesome Julia. (navbar_julia)
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)
Cloud Monk is Retired (for now). Buddha with you. © 2005 - 2024 Losang Jinpa or Fair Use. Disclaimers
SYI LU SENG E MU CHYWE YE. NAN. WEI LA YE. WEI LA YE. SA WA HE.