Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python
genuine image
Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python
Create and deploy Streamlit web applications from scratch in Python
Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python
Numéro d'article: 39044117

Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python

Numéro d'article: 39044117

XOF 21575

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

*All items will import from États-Unis

En stock
États-Unis Importé depuis la boutique USA

QTY:

Commandez maintenant et recevez votre commande aux alentours du Mardi, Juillet 14
Transaction sécuriséeTransaction sécurisée

Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

Nos meilleurs partenaires logistiques
  • fedex
  • dhl
Create and deploy Streamlit web applications from scratch in Python
Garantie U-Care :
Aucun
Sélectionnez un forfait
fast shipping

Livraison
rapide

free return

Retour
gratuit*

Emballage sécurisé

Emballage sécurisé

Produits 100 % originaux

Produits 100 % originaux

pci-dss

Conformité PCI DSS

iso certified

Certifié ISO 27001

paypal payment
visa payment
mastercard payment
Note: Step Down Voltage Transformer required for using electronics products of États-Unis store (110-120). Recommended power converters Acheter maintenant.

Ce qui se démarque

User-Friendly Framework
Streamlit simplifies the creation of interactive web apps, making it accessible for data scientists with minimal web development experience, thus enhancing productivity and ease of deployment.
Rapid Prototyping
This product facilitates quick iteration of data apps, enabling users to visualize and share insights faster than traditional methods, which accelerates the data science workflow.
Comprehensive Documentation
Getting Started with Streamlit offers extensive guides and examples, empowering users to fully leverage Streamlit’s capabilities while minimizing the learning curve, making it ideal for both beginners and experienced developers.

Détails du produit

Get started with Streamlit for data science. Learn how to create and deploy web applications from scratch in Python. Shop now at Ubuy Benin KW.
Item Weight1.2 lbs (540 grams)

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners looking to learn how to build web applications using Python for data visualization.

  • Data Analysts

    Perfect for professionals who want to present data insights interactively without deep web development knowledge.

  • Educators and Trainers

    Useful for instructors aiming to create engaging, interactive teaching materials that visualize complex data concepts.

Not Suitable For
  • Advanced Developers

    Not suitable for experienced developers seeking in-depth technical insights or advanced customization options in web development.

  • Non-Technical Users

    Users with no programming knowledge may struggle with understanding Python and web application development concepts.

  • Large Scale Applications

    Not intended for building complex, enterprise-level applications requiring extensive features beyond simple data visualization.

DESCRIPTION DU PRODUIT

Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is Streamlit and why is it used in data science?

    répondre: Streamlit is an open-source app framework specifically designed for machine learning and data science projects. It allows users to create interactive web applications using only Python, making it accessible for developers and data scientists who may not have extensive web development experience. Streamlit transforms scripts into shareable web apps with minimal effort, allowing for real-time data visualization. For instance, a data scientist can display interactive dashboards that auto-update based on changing datasets, enhancing stakeholder engagement and decision-making.
  • question: How can I install Streamlit for my Python projects?

    répondre: To install Streamlit, you can use pip, the Python package manager. Simply open your command line and execute 'pip install streamlit'. Ensure you have Python installed on your machine, as Streamlit requires it to operate. After installation, you can start a new project by creating a Python file and running 'streamlit run [your_file_name].py'. This is particularly useful for launching quick prototypes or visualizations without needing a comprehensive web development setup.
  • question: What are the main features of Streamlit?

    répondre: Streamlit boasts several key features, including easy integration with popular data science libraries like Pandas and NumPy, automatic front-end generation, and interactive widgets, such as sliders and buttons. These features empower users to create dynamic and responsive applications that can evolve based on user input. For example, you can create a machine learning model training app where users adjust parameters and instantly see the impacts on model performance in real-time.
  • question: Can I deploy my Streamlit applications?

    répondre: Yes, Streamlit applications can be deployed in several environments, including Streamlit Sharing, AWS, and Heroku. Streamlit Sharing is a user-friendly option for rapidly deploying applications without extensive infrastructure management. Once deployed, teams can collaboratively access the app, making it an ideal choice for ongoing projects and presentations. For example, a data team can share their analytics app with stakeholders, allowing them to explore insights directly from their web browsers.
  • question: Is Streamlit compatible with other data visualization libraries?

    répondre: Absolutely! Streamlit works seamlessly with various data visualization libraries, including Matplotlib, Seaborn, Plotly, and Altair. You can combine these libraries to enhance your application’s visual appeal and functionality. For instance, you may use Plotly for interactive graphs and Matplotlib for static images, which can both be displayed in one app to cater to different analysis needs, adding depth to your data storytelling.
  • question: What types of projects are ideal for Streamlit?

    répondre: Streamlit is perfect for a wide range of projects, particularly those involving data visualization, machine learning model deployment, and data exploration. It's particularly useful for creating dashboards, data analytics applications, or even simple prototypes to test concepts. For example, a financial analyst might use Streamlit to develop a real-time stock market analysis tool that updates as new data comes in, allowing stakeholders to make informed decisions quickly.
  • question: Does Streamlit require a high level of programming expertise?

    répondre: No, Streamlit is designed to be user-friendly and does not require extensive programming skills. Even those with basic Python knowledge can utilize Streamlit effectively. The clear syntax and straightforward API allow newcomers to develop web applications without needing to delve into front-end web technologies like HTML or CSS. For example, a beginner can create a simple data exploration app using just Python knowledge, making it an excellent learning tool.
  • question: How does Streamlit handle data privacy?

    répondre: Streamlit is designed to run locally initially, meaning your data remains on your machine until you decide to deploy it. When sharing applications, you have full control over which data is included. Streamlit also allows you to configure how user input is handled, ensuring that sensitive information can be managed securely. For instance, many organizations can develop internal tools using Streamlit without exposing critical data to unauthorized users.
  • question: What are some best practices when using Streamlit?

    répondre: Best practices for using Streamlit include keeping your code clean and modular, utilizing caching to boost performance, and deploying only necessary data and visualizations. Additionally, leveraging Streamlit's capability for layout customization can improve user experience significantly. For example, segmenting complex applications into tabs or sections can help users navigate data more effectively, ensuring clarity and engagement while exploring the app.
  • question: Where can I buy Getting Started with Streamlit for Data Science in Benin?

    répondre: You can purchase 'Getting Started with Streamlit for Data Science: Create and Deploy Streamlit Web Applications from Scratch in Python' from Ubuy in Benin. Ubuy provides a convenient platform to obtain this book, enabling you to kick-start your journey into building interactive applications with Streamlit and enhancing your data science skills.

Expert Systems Editorial Review

Aucune critique éditoriale trouvée

Avis et évaluations clients

5.0
1 évaluations des clients
  • 5 étoile
    100%
  • 4 étoile
    0%
  • 3 étoile
    0%
  • 2 étoile
    0%
  • 1 étoile
    0%

Donnez votre avis sur ce produit

Partagez votre avis avec d'autres clients

Platform Trust & Buyer Confidence

trustpilot logo
4.3/5 9,000 + reviews
Read reviews
MT
Mohd
Verified buyer

“The product received very good packaging & safe…Thank You”

16 June 2026 · via Trustpilot
SJ
Shawati
Verified buyer

“Accurate delivery timing given”

16 June 2026 · via Trustpilot
YB
Youcef
Verified buyer

“Not madly expensive like I thought, and much quicker than promised.”

15 June 2026 · via Trustpilot
LM
Leila
Verified buyer

“Never dealt with Ubuy before, but everything worked out great. Seamless cross border purchasing and shipping. Thanks!”

6/7/2026 · via Trustpilot
KA
Kwame
Verified buyer

“The process was smooth, with clear communication and timelines. This was my 1st purchase and I am really impressed. I will definitely be coming back.”

12 June 2026 · via Trustpilot
Paiement sécurisé Global Delivery Easy Returns Genuine Products

Historique des prix du produit

Informations importantes

  • Limitations : Pour les produits expédiés à l'international, veuillez noter que toute garantie du fabricant peut ne pas être valide ; les options de service du fabricant peuvent ne pas être disponibles ; les manuels, instructions et avertissements de sécurité des produits peuvent ne pas être dans les langues du pays de destination ; les produits (et les matériaux qui les accompagnent) peuvent ne pas être conçus conformément aux normes, spécifications et exigences d'étiquetage du pays de destination ; et les produits peuvent ne pas être conformes à la tension et aux autres normes électriques du pays de destination (nécessitant l'utilisation d'un adaptateur ou d'un convertisseur le cas échéant). Il incombe au destinataire de s'assurer que le produit peut être importé légalement dans le pays de destination. En cas de commande auprès d'Ubuy ou de ses filiales, le destinataire est l'importateur officiel et doit se conformer à toutes les lois et réglementations du pays de destination.
  • Tous les produits listés sur Ubuy ne sont pas à vendre, Ubuy étant un moteur de recherche mondial. Les produits sont soumis aux réglementations en matière d'exportation et de commerce.

XOF 21575

Commandez maintenant et recevez votre commande aux alentours du Mardi, Juillet 14

QTY:

Transaction sécuriséeTransaction sécurisée

PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.

Nos meilleurs partenaires logistiques
  • fedex
  • dhl
Livraison transfrontalière la plus rapide

Caractéristiques et avantages

  • Learn to build web apps quickly using Streamlit in Python
  • Explore methods for manipulating and visualizing data with Streamlit
  • Discover techniques for deploying machine learning models
  • Beautify and customize your Streamlit apps using components, themes, and sidebar
  • Implement the best practices for prototyping data science work with Streamlit

Ubuy Assurance

Experience worry-free shopping with 100% original products, PCI DSS-compliant payment security, ISO 27001-certified data protection, the fastest cross-border delivery, free returns *, and secure packaging on every order.