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Ruby Data Science
Return to Data Science, Machine Learning - Deep Learning, Ruby Machine Learning, DataOps-MLOps-DevOps, Ruby Official Glossary, Ruby Topics, Ruby, Ruby DevOps - Ruby SRE, Ruby DataOps, Ruby MLOps
- Snippet from Wikipedia: Data science
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms, coding (like Python, SQL, and R), and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data. A data scientist is a professional who creates programming code and combines it with statistical knowledge to summarize data.
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Ruby: Ruby Fundamentals, Ruby Inventor - Ruby Language Designer: Yukihiro Matsumoto in 1995; Ruby scripting, Rails, RubyGems, Ruby keywords, Ruby Built-In Data Types, Ruby data structures - Ruby algorithms, Ruby syntax, Ruby OOP - Ruby design patterns, Ruby for Chef, Ruby for Puppet, Ruby on Linux, Ruby on macOS, Ruby on Windows, Ruby installation, Ruby containerization, Ruby configuration, Ruby compiler - Ruby interpreter (Matz's Ruby Interpreter or Ruby MRI, also called CRuby), Ruby IDEs (RubyMine), Ruby development tools, Ruby DevOps - Ruby SRE, Ruby data science - Ruby DataOps, Ruby machine learning, Ruby deep learning, Functional Ruby, Ruby concurrency, Ruby history, Ruby bibliography, Ruby Glossary - Glossaire de Ruby - French, Ruby topics, Ruby courses, Ruby Standard Library, Ruby libraries, Ruby frameworks (Ruby on Rails), Ruby research, Ruby GitHub, Written in Ruby, Ruby popularity, Ruby Awesome list, Ruby Versions. (navbar_ruby)
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)
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