Data Science (CloudMonk.io)
Data Science
Return to Big Data, Data Science Platforms, Data Science Bibliography
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines aspects of machine learning, statistics, data analysis, and computer science to analyze and interpret complex data, solve problems, and make informed decisions. Data scientists apply data science techniques to collect, clean, and process data, build predictive models, and communicate findings to stakeholders. This field plays a crucial role in various industries by enabling the development of data-driven products, optimizing business processes, and improving decision-making.
External sites
* https://duckduckgo.com/?q=Data+Science
* wp>Data science
* g>Data science
* https://www.gartner.com/doc/3606026/magic-quadrant-data-science-platforms
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 Plus Plus 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 Sharp 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 (computing) | Data, Data augmentation | Augmentation, Data analysis | Analysis, Data analytics | Analytics, Data archaeology | Archaeology, Data cleansing | Cleansing, Data collection | Collection, Data compression | Compression, Data corruption | Corruption, Data curation | Curation, Data degradation | Degradation, Data editing | Editing (EmEditor), Data engineering, Extract, transform, load | ETL/Extract, load, transform | ELT (Data extraction | Extract-Data transformation | Transform-Data loading | Load), Data farming | Farming, Data format management | Format management, Data fusion | Fusion, Data integration | Integration, Data integrity | Integrity, Data lake | Lake, Data library | Library, Data loss | Loss, Data management | Management, Data migration | Migration, Data mining | Mining, Data pre-processing | Pre-processing, Data preservation | Preservation, Information privacy | Protection (privacy), Data recovery | Recovery, Data reduction | Reduction, Data retention | Retention, Data quality | Quality, Data science | Science, Data scraping | Scraping, Data scrubbing | Scrubbing, Data security | Security, Data steward | Stewardship, Data storage | Storage, Data validation | Validation, Data warehouse | Warehouse, Data wrangling | 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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