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Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
4,473
Unternehmen
Wir haben Daten zu 4,473 Unternehmen, die SciPy verwenden. Unsere SciPy Kundenliste steht zum Download bereit und ist mit wichtigen Unternehmensspezifika angereichert, darunter Branchenklassifikation, Organisationsgröße, geografische Lage, Finanzierungsrunden und Umsatzzahlen, unter anderem.
Unternehmen | Land | Branche | Mitarbeiter | Umsatz |
---|---|---|---|---|
Amazon.com Services LLC | Vereinigte Staaten | Retail | 10K | $50M |
Quiet Professionals LLC | Vereinigte Staaten | Defense And Space Manufacturing | 120 | $7M |
Under Armour | Vereinigte Staaten | Retail Apparel And Fashion | 12K | $5.8B |
![]() AssemblyAI | Vereinigte Staaten | Software Development | 113 | |
CEDARS-SINAI | Vereinigte Staaten | Hospitals And Health Care | 14K | $58M |
![]() PriceHubble | Schweiz | Real Estate | 200 | $11M |
Cedars-Sinai | Vereinigte Staaten | Hospitals And Health Care | 15K | |
Fiori Technology Solutions Inc | Vereinigte Staaten | It Services And It Consulting | 16 | |
![]() Magic Leap | Vereinigte Staaten | Computers And Electronics Manufacturing | 1.3K | $185M |
![]() Vicarious Surgical | Vereinigte Staaten | Medical Equipment Manufacturing | 220 | $3M |
SONEM Solutions GmbH | Deutschland | Appliances, Electrical, And Electronics Manufacturing | 4 | |
Sonalysts, Inc. | Vereinigte Staaten | Defense And Space Manufacturing | 426 | $40M |
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SciPy wird in 68 Ländern verwendet
Häufig gestellte Fragen
Unsere Daten stammen aus Stellenanzeigen, die von Millionen von Unternehmen gesammelt wurden. Wir überwachen diese Anzeigen auf Firmenwebseiten, Jobbörsen und anderen Rekrutierungsplattformen. Die Analyse von Stellenanzeigen bietet eine zuverlässige Methode, um die von Unternehmen verwendeten Technologien zu verstehen, einschließlich der Nutzung interner Tools.
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SciPy is a comprehensive open-source library for scientific computing in Python. It provides a wide range of functionalities for mathematical operations, numerical routines, optimization, linear algebra, integration, interpolation, and more. SciPy is built on top of NumPy, another popular Python library for numerical computing, and together they form a powerful ecosystem for scientific computing and data analysis.
Within the category of Data Science Tools, SciPy plays a crucial role in enabling data scientists, researchers, and engineers to perform complex scientific computations efficiently. Its extensive collection of modules and functions makes it a go-to choice for tasks ranging from simple computations to advanced scientific experiments and simulations. By leveraging SciPy, professionals can streamline their workflows, enhance productivity, and tackle intricate data analysis challenges with ease.
Initially founded in 2001 by Travis Oliphant, SciPy emerged as a response to the need for a robust scientific computing toolset in the Python programming language. Over the years, it has evolved into a cornerstone of the Python ecosystem, attracting a vast community of users and contributors dedicated to advancing scientific computing capabilities. With a strong emphasis on performance, usability, and extensibility, SciPy continues to be a pivotal tool for realizing innovative research projects and data-driven insights.
In terms of current market share within the Data Science Tools category, SciPy holds a significant position due to its widespread adoption and reputation among data science and research communities. As the demand for sophisticated data analysis tools continues to rise, SciPy is expected to maintain its growth trajectory and solidify its presence in the market. With ongoing developments, improvements, and expansions, SciPy is poised to further enhance its capabilities and appeal to a broader audience of data enthusiasts and professionals.
SciPy is a powerful library for scientific computing in Python, widely used by companies to leverage its versatile capabilities in data science projects. With a wide range of functions and tools, SciPy offers a comprehensive environment for data analysis, visualization, and manipulation.
1. Extensive Functionality: SciPy provides a vast array of functions for numerical optimization, linear algebra, integration, interpolation, and more. Unlike other similar technologies, SciPy offers a seamless integration with other popular Python libraries like NumPy and Pandas, making it a preferred choice for data scientists and engineers.
2. Open Source and Community Support: Being an open-source library, SciPy benefits from continuous development and contributions from a vibrant community of developers. This ensures regular updates, bug fixes, and new features, which are crucial for staying ahead in the rapidly evolving field of data science.
3. Performance and Scalability: SciPy is built on top of optimized libraries such as BLAS and LAPACK, enabling high-performance computing for large datasets. Its efficient algorithms and data structures make it a reliable choice for handling complex computational tasks with speed and scalability.
4. Visualization Capabilities: SciPy seamlessly integrates with popular data visualization libraries like Matplotlib and Seaborn, allowing users to create stunning visualizations of their analysis results. This visual appeal not only enhances data interpretation but also aids in presenting findings effectively to stakeholders.
In summary, SciPy stands out in the realm of Data Science Tools for its extensive functionality, community support, performance, scalability, and visualization capabilities, making it an indispensable asset for companies aiming to excel in data-driven decision-making.
SciPy is a popular open-source library in the Python ecosystem that provides efficient numerical routines for scientific computing. Many well-known companies across various industries leverage SciPy to enhance their data analysis and machine learning capabilities. Here are a few case studies highlighting how companies utilize SciPy in their workflow:
1. SpaceX: SpaceX, the aerospace manufacturer and space transportation company founded by Elon Musk, extensively uses SciPy for trajectory optimization and simulation in their rocket launches. By leveraging SciPy's mathematical optimization and integration features, SpaceX engineers can calculate optimal flight paths, minimize fuel consumption, and ensure precise landing of reusable rockets. SpaceX started incorporating SciPy into their computational tools since 2014, leading to improved efficiency and accuracy in their space missions.
2. Netflix: Netflix, the global streaming platform, utilizes SciPy for building recommendation algorithms and analyzing user data to personalize content recommendations. SciPy's capabilities in linear algebra, optimization, and statistics play a crucial role in enhancing Netflix's recommendation engine, ultimately improving user engagement and retention. Netflix integrated SciPy into their data science pipeline back in 2012, enabling them to deliver personalized viewing suggestions to millions of subscribers worldwide accurately.
3. Spotify: Spotify, the popular music streaming service, harnesses SciPy for music recommendation systems and audio signal processing tasks. By leveraging SciPy's signal processing and machine learning functionalities, Spotify can extract valuable insights from audio data, such as genre classification, mood detection, and song similarity analysis. Spotify adopted SciPy as a core component of their data analysis toolkit in 2015, empowering music recommendation algorithms that cater to individual user preferences effectively.
These case studies showcase how prominent companies like SpaceX, Netflix, and Spotify leverage SciPy's capabilities in numerical computing, optimization, and data analysis to drive innovation and enhance user experiences in their respective industries. By incorporating SciPy into their workflows, these companies demonstrate the versatile applications of this powerful data science tool in solving complex real-world challenges.
Sie können eine aktuelle Liste von Unternehmen, die SciPy verwenden, auf TheirStack.com einsehen. Unsere Plattform bietet eine umfassende Datenbank von Unternehmen, die verschiedene Technologien und interne Tools nutzen.
Bis jetzt haben wir Daten von 4,473 Unternehmen, die SciPy verwenden.
SciPy wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Retail", "Defense And Space Manufacturing", "Retail Apparel And Fashion", "Software Development", "Hospitals And Health Care", "Real Estate", "Hospitals And Health Care", "It Services And It Consulting", "Computers And Electronics Manufacturing", "Medical Equipment Manufacturing", verwendet. Für eine umfassende Liste aller Branchen, die SciPy nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die SciPy verwenden, umfassen Amazon.com Services LLC, Quiet Professionals LLC, Under Armour, AssemblyAI, CEDARS-SINAI, PriceHubble, Cedars-Sinai, Fiori Technology Solutions Inc, Magic Leap, Vicarious Surgical und viele mehr. Sie können eine vollständige Liste von 4,473 Unternehmen, die SciPy nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist SciPy am beliebtesten in Vereinigte Staaten (1,731 companies), Vereinigtes Königreich (369 companies), Indien (151 companies), Kanada (142 companies), Deutschland (136 companies), Frankreich (124 companies), Spanien (83 companies), Niederlande (52 companies), Schweiz (49 companies), Brasilien (44 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die SciPy verwenden, finden, indem Sie auf TheirStack.com danach suchen. Wir verfolgen Stellenanzeigen von Millionen von Unternehmen und nutzen sie, um herauszufinden, welche Technologien und internen Tools sie verwenden.