Company | Country | Industry | Employees | Revenue |
---|---|---|---|---|
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
|
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.
私たちはSciPyを使用している4,473社のデータを持っています。このキュレーションリストはダウンロード可能で、業界分類、組織の規模、地理的位置、資金調達ラウンド、収益数値などの重要な会社の詳細が含まれています。
会社 | 国 | 業界 | 従業員 | 収益 |
---|---|---|---|---|
Amazon.com Services LLC | アメリカ合衆国 | Retail | 10K | $50M |
Quiet Professionals LLC | アメリカ合衆国 | Defense And Space Manufacturing | 120 | $7M |
Under Armour | アメリカ合衆国 | Retail Apparel And Fashion | 12K | $5.8B |
![]() AssemblyAI | アメリカ合衆国 | Software Development | 113 | |
CEDARS-SINAI | アメリカ合衆国 | Hospitals And Health Care | 14K | $58M |
![]() PriceHubble | スイス | Real Estate | 200 | $11M |
Cedars-Sinai | アメリカ合衆国 | Hospitals And Health Care | 15K | |
Fiori Technology Solutions Inc | アメリカ合衆国 | It Services And It Consulting | 16 | |
![]() Magic Leap | アメリカ合衆国 | Computers And Electronics Manufacturing | 1.3K | $185M |
![]() Vicarious Surgical | アメリカ合衆国 | Medical Equipment Manufacturing | 220 | $3M |
SONEM Solutions GmbH | ドイツ | Appliances, Electrical, And Electronics Manufacturing | 4 | |
Sonalysts, Inc. | アメリカ合衆国 | Defense And Space Manufacturing | 426 | $40M |
全体リストをダウンロードしますか?
サインアップして 4,473 companies のリスト全体をダウンロードしてください
Loading countries...
Loading other techonlogies...
技術使用統計と市場シェア
このデータは、地理、業界、企業規模、収益、技術使用状況、求人情報などでフィルタリングすることにより、あなたのニーズに合わせてカスタマイズできます。データはExcelまたはCSV形式でダウンロードできます。
このデータのアラートを受け取ることができます。興味のある技術を選択すると、その技術を使用している新しい企業がある場合に受信ボックスにアラートが届きます。
彼のデータをExcelファイルにエクスポートでき、それはあなたのCRMにインポートできます。また、そのデータをAPIにエクスポートすることもできます。
SciPyには28の代替案があります。
18,9k
13,9k
12k
11,1k
2,4k
1,8k
1,6k
1,4k
1,2k
663
432
414
317
223
193
140
105
44
38
32
26
17
11
9
7
4
3
2
よくある質問
私たちのデータは、何百万もの企業から収集された求人情報に基づいています。私たちはこれらの求人情報を企業のウェブサイト、求職掲示板、およびその他の採用プラットフォームで監視しています。求人情報を分析することで、企業が使用している技術や内部ツールの利用状況を理解するための信頼性の高い方法を提供します。
私たちはデータを毎日更新して、利用可能な最新の情報にアクセスできるようにしています。この頻繁な更新プロセスにより、私たちのインサイトとインテリジェンスが業界内の最新の展開とトレンドを反映していることを保証します。
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.
TheirStack.com を訪問すると、SciPy を使用している企業の最新リストにアクセスできます。当社のプラットフォームは、さまざまな技術や内部ツールを活用している企業の包括的なデータベースを提供します。
現在、4,473 社が SciPy を使用しているデータを保持しています。
SciPy は "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" を含む様々な業界の多様な組織によって使用されています。SciPy を利用しているすべての業界の包括的なリストについては、TheirStack.com をご覧ください。
SciPy を使用している企業の中には、Amazon.com Services LLC, Quiet Professionals LLC, Under Armour, AssemblyAI, CEDARS-SINAI, PriceHubble, Cedars-Sinai, Fiori Technology Solutions Inc, Magic Leap, Vicarious Surgical などが含まれています。他にも多くの企業があります。SciPy を使用している 4,473 社の完全なリストは TheirStack.com で見つけることができます。
私たちのデータによれば、SciPy は アメリカ合衆国 (1,731 companies), イギリス (369 companies), インド (151 companies), カナダ (142 companies), ドイツ (136 companies), フランス (124 companies), スペイン (83 companies), オランダ (52 companies), スイス (49 companies), ブラジル (44 companies) で最も人気があります。しかし、世界中の企業で使用されています。
TheirStack.comでSciPyを検索することにより、{technology.name}を使用している企業を見つけることができます。 theirstackは、数百万の企業からの求人情報を追跡し、それらが使用している技術や内部ツールを発見します。
SciPyは68か国で使用されています