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“krangl is a Kotlin library for Kotlin data wrangling. By implementing a grammar of Kotlin data manipulation using a modern Kotlin functional-style Kotlin API, it allows to Kotlin filter, Kotlin transform, Kotlin aggregate and Kotlin reshape Kotlin tabular data”
“krangl is heavily inspired by the amazing dplyr for R. krangl is written in Kotlin, excels in Kotlin, but emphasizes as well on good java-interop. It is mimicking the API of dplyr, while carefully adding more typed constructs where possible.” Fair Use Source: https://github.com/holgerbrandl/krangl
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.
Kotlin: Kotlin Fundamentals, Kotlin Inventor - Kotlin Language Designer: Andrey Breslav and JetBrains on July 22, 2011, Kotlin 1.0 on February 15, 2016; JVM, Kotlin on JVM, Kotlin RFCs, Kotlin Scripting, Kotlin Keywords, Kotlin Built-In Data Types, Kotlin Data Structures - Kotlin Algorithms, Kotlin Syntax, Kotlin OOP - Kotlin Design Patterns - Kotlin Best Practices, Kotlin Installation, Kotlin Containerization, Kotlin Configuration, Kotlin Compiler, Kotlin Transpiler (Kotlin/JS - Kotlin.js, kotlin.multiplatform), Kotlin Multiplatform, Kotlin REPL (Kotlin Interpreter), Kotlin IDEs (JetBrains IntelliJ, Android Studio), Kotlin development tools, Kotlin Linter, JetBrains, Kotlin Testing, Kotlin on Android, Kotlin on Windows, Kotlin on macOS, Kotlin on Linux, KTor, Kotlin DevOps - Kotlin SRE - Kotlin Scripting (kscript), Kotlin Data Science - Kotlin DataOps, Kotlin Machine Learning, Kotlin Deep Learning, Functional Kotlin, Kotlin Concurrency - Kotlin Parallel Programming - Async Kotlin, Kotlin History, Kotlin Bibliography, Manning Kotlin Series, Kotlin Glossary, Kotlin Topics, Kotlin Courses, Kotlin Security - Kotlin DevSecOps, Kotlin Standard Library, Kotlin Libraries, Kotlin Frameworks, Kotlin Research, Kotlin GitHub, Written in Kotlin, Kotlin Popularity, Kotlin Awesome List, Kotlin Versions. (navbar_kotlin)
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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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