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Java Data Science
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- 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.
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Java: Java Fundamentals, Java Inventor - Java Language Designer: James Gosling of Sun Microsystems, Java Docs, JDK, JVM, JRE, Java Keywords, JDK 17 API Specification, java.base, Java Built-In Data Types, Java Data Structures - Java Algorithms, Java Syntax, Java OOP - Java Design Patterns, Java Installation, Java Containerization, Java Configuration, Java Compiler, Java Transpiler, Java IDEs (IntelliJ - Eclipse - NetBeans), Java Development Tools, Java Linter, JetBrains, Java Testing (JUnit, Hamcrest, Mockito), Java on Android, Java on Windows, Java on macOS, Java on Linux, Java DevOps - Java SRE, Java Data Science - Java DataOps, Java Machine Learning, Java Deep Learning, Functional Java, Java Concurrency, Java History,
Java Bibliography (Effective Java, Head First Java, Java - A Beginner's Guide by Herbert Schildt, Java Concurrency in Practice, Clean Code by Robert C. Martin, Java - The Complete Reference by Herbert Schildt, Java Performance by Scott Oaks, Thinking in Java, Java - How to Program by Paul Deitel, Modern Java in Action, Java Generics and Collections by Maurice Naftalin, Spring in Action, Java Network Programming by Elliotte Rusty Harold, Functional Programming in Java by Pierre-Yves Saumont, Well-Grounded Java Developer, Second Edition, Java Module System by Nicolai Parlog
), Manning Java Series, Java Glossary, Java Topics, Java Courses, Java Security - Java DevSecOps, Java Standard Library, Java Libraries, Java Frameworks, Java Research, Java GitHub, Written in Java, Java Popularity, Java Awesome List, Java Versions. (navbar_java and navbar_java_detailed - see also navbar_jvm, navbar_java_concurrency, navbar_java_standard_library, navbar_java_libraries, navbar_java_navbars)
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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