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Free Download Udemy - Master Streamlit - Build Interactive Data Apps With Python Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.28 GB | Duration: 9h 15m Go from Python scripts to interactive web apps and dashboards. Learn widgets, layouts, vizualization, APIs & deployment What you'll learn Build interactive data dashboards with Python, using Streamlit, without needing web development expertise Transform static data analyses (Pandas, NumPy) into engaging web applications that can be shared and explored by othersBI Professionals/Report Builders Create custom data-driven tools and visualizations, using Streamlit components, to prototype ideas and explore datasets rapidly Deploy interactive Streamlit apps to the cloud and share them with anyone, using free and easy-to-use hosting platforms Integrate external data sources (CSV, JSON, APIs) into Streamlit apps to create dynamic and up-to-date visualizations Design intuitive user interfaces for data exploration using Streamlit's layout options (columns, tabs, expanders) Customize the appearance of Streamlit apps using themes and custom CSS to match branding or personal preferences Requirements Basic computer literacy. No prior programming experience is required. We'll cover all the necessary Python and Streamlit concepts from the ground up. Description Are you a data scientist, analyst, engineer, or researcher who works with Python? Do you want to share your data insights in a more engaging and interactive way, without having to learn complex web development frameworks? Then this course is for you!Streamlit is a revolutionary open-source Python library that makes it incredibly easy to build beautiful, interactive web applications for data science and machine learning. With Streamlit, you can turn your data scripts into shareable web apps in minutes, using only Python. No need for HTML, CSS, or JavaScript!This comprehensive course will guide you from the very basics of Streamlit to building and deploying sophisticated, interactive data dashboards and tools. You'll learn how to:Get Started: Set up your development environment and create your first Streamlit app.Display Data: Work with text, tables, and a wide variety of charts (line charts, bar charts, area charts, and more) using Streamlit's built-in functions and popular libraries like Matplotlib and Plotly.Add Interactivity: Use Streamlit's powerful widgets (buttons, sliders, selectboxes, text inputs, etc.) to create dynamic applications that respond to user input.Control Layout: Organize your apps with columns, tabs, expanders, and containers for a clean and intuitive user interface.Work with Data: Load data from CSV files, JSON files, and even external APIs.Persist State: Store user preferences and data across sessions using cookies.Deploy Your Apps: Share your creations with the world using Streamlit Sharing and other cloud deployment options.Go Beyond the Basics: Learn how to extend the capabilities of Streamlit by building custom components using React, opening up endless possibilities for creating unique and powerful data applications.This course emphasizes hands-on learning, with numerous examples, practical exercises, and skill challenges to reinforce the concepts. By the end, you'll be able to confidently build and deploy your own interactive data apps with Streamlit, transforming the way you work with and communicate data. Whether you're a seasoned data professional or just starting your journey, this course will empower you to create compelling data-driven web applications with ease. And if you're new to Python, don't fret! There is a full-length introduction to Python included as an Appendix which is included to get anyone up and running writing pythonic code in no time.See you inside! Anyone interested in turning Python scripts into interactive web applications,Individuals with some Python experience who want to rapidly prototype data-driven applications and dashboards,Data professionals (analysts, scientists, engineers) who want to share their work interactively and create internal tools, without complex web development,Researchers, academics, and educators who want to create interactive visualizations and simulations to explain concepts or share findings effectively Homepage: https://www.udemy.com/course/master-streamlit-build-interactive-data-apps-with-python/ [b]AusFile[/b] https://ausfile.com/8u23ab30qi2n/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part1.rar.html https://ausfile.com/31pybijenvpu/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part2.rar.html https://ausfile.com/ffq8m40p8g0d/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part3.rar.html https://ausfile.com/m1k1pwikc1si/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part4.rar.html Rapidgator https://rg.to/file/dae56d61daee2d0e914fb2723e93a77a/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part1.rar.html https://rg.to/file/7a8234a14b83293f5e44c57c68273967/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part2.rar.html https://rg.to/file/aa6d37865c1aa9449588548c777f7aef/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part3.rar.html https://rg.to/file/b2ea8826835f76bae516dc56d0b78216/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part4.rar.html Fikper Free Download https://fikper.com/ScHeaDO9is/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part1.rar.html https://fikper.com/wkPOBton4H/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part2.rar.html https://fikper.com/ZgsYe0qOSx/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part3.rar.html https://fikper.com/jcWlPHFJ0h/nyqub.Master.Streamlit.Build.Interactive.Data.Apps.With.Python.part4.rar.html No Password - Links are Interchangeable

Free Download Streamlit Deployer son app de Machine Learning sur le web Last updated 9/2024 Created by Pierre-louis Danieau MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: French + subtitle | Duration: 25 Lectures ( 4h 38m ) | Size: 1.74 GB Créez rapidement une superbe application web et déployez votre modèle d'IA dans le monde entier avec Python ! What you'll learn Savoir utiliser Streamlit Développer et déployer son application Data afin de partager ses modèles de Machine Learning sur le web Scrapper de la Data en temps réel grâce à une API (Yahoo Finance) Utilisation de Streamlit Cloud Créer des visuels attrayants avec les librairies interactives de Python Créer une interface utilisateur attractive (UI / UX) Structurer son programme Python pour du développement web Savoir optimiser une application Streamlit (Cache / Session / Form...) Utilisation de Git et Github Surpasser le Jupyter Notebook et donner vie à son projet Data Requirements Une connaissance élémentaire du language de programmation Python est requise pour mieux comprendre les concepts abordés dans cette formation. De simples connaissances suffisent. Aucune compétence en développement web et/ou en data engineering n'est nécessaire. L'ensemble des concepts sont abordés depuis le début. Aucune expérience dans le cloud n'est requise. Vous apprendrez tout ce qu'il est utile de savoir pour la partie déploiement / mise en production. Description Avez-vous déjà ressenti la frustration d'avoir développé un super modèle de Machine Learning sur votre Jupyter Notebook et de ne jamais pouvoir le confronter à une utilisation réelle ? C'est la proposition de valeur de Streamlit et de cette formation: Pouvoir déployer votre projet Data sur le web afin que le monde entier puisse l'utiliser grâce à votre propre application web !Ainsi, l'ensemble de vos projets Data vont prendre vie ! Vous allez ainsi pouvoir : Partagez votre superbe classificateur d'images afin que d'autres personnes puissent utiliser votre modèle en y téléchargeant leurs propres images.Déployez en temps réel le score de sentiment des derniers tweets d'Elon Musk avec du NLP.Ou encore réaliser des dashboards interactifs à destination de vos équipes en entreprise avec un système d'authentification pour restreindre l'accès à seulement quelques personnes.J'ai développé ce cours après que des dizaines de personnes m'aient contacté pour me demander comment j'avais fait pour développer une application web de réservation de trains en temps réel, utilisée par plus de 10 000 personnes. Car oui on peut utiliser streamlit pour tous types d'applications et non seulement des applications data / IA !Bref, des centaines de cas d'usage sont possibles avec streamlit !Ce qui est formidable dans tout ça, c'est qu'il suffit uniquement d'avoir des connaissances en Python.Et qu'aucune compétence en Développement web, en Data Engineering ou même en cloud n'est nécessaire.Ce cours est scindé en 2 parties : Une partie exercice où nous verrons l'ensemble des fondamentaux de Streamlit, depuis la connection à un système de base de donnée, en passant par la création de l'interface puis finalement la partie sur le déploiement dans le cloud !Une seconde partie destinée au projet de formation : Développement et mise en production d'une application de tracking et d'analyse des actions du S&P5O0 avec notamment la visualisation de l'évolution du cours des actions et le calcul d'indicateurs de performances. Les données seront requêtées via une API.Faites passer vos projets data à l'étape supérieure avec Streamlit !Bonne formation :) Who this course is for Des personnes s'intéressant à la Data et à Python mais qui sont frustrés de ne jamais pouvoir partager leurs modèles de Machine Learning autour d'eux ! Des Data Scientist en entreprise qui souhaitent partager leurs travaux de Machine Learning ou des dashboards en interne pour leurs collaborateurs. Une personne qui a une idée de projet d'application web et qui souhaite développer un MVP en quelques heures ! Tous bons Data Sientists ! Homepage https://www.udemy.com/course/streamlit-deployer-son-app-de-machine-learning-sur-le-web/ Screenshot Rapidgator https://rg.to/file/05c4fd25d34d31558952388d1adb14c8/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part2.rar.html https://rg.to/file/56728b99d11983f74923ed3d0c6dfd5f/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part1.rar.html Fikper Free Download https://fikper.com/N3Ko4FbJHc/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part2.rar.html https://fikper.com/d2lF2Fw9Dl/fktew.Streamlit..Deployer.son.app.de.Machine.Learning.sur.le.web.part1.rar.html No Password - Links are Interchangeable

Free Download Data Pipelines with Snowflake and Streamlit Published 9/2024 Created by Marcos Vinicius Oliveira MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 40 Lectures ( 5h 17m ) | Size: 2.1 GB Using Snowflake to data engineer Kaggle and Google Trends data with Python procedures and tasks What you'll learn: Setup Snowflake and AWS Accounts Work with Kaggle and SerpAPI Download and manipulate data with Jupyter Notebooks on VS Code Work with External Access Integration and Storage Integration on Snowflake Create Snowflake Python based procedures Create Snowflake tasks Create Streamlit apps inside of Snowflake Requirements: Proficient knowledge on SQL and basic knowledge on Snowflake database Basic knowledge on data modeling and engineering Proficient Python knowledge Description: This course focuses on building a data engineering pipeline that integrates multiple data sources, including Kaggle datasets and Google Trends data (fetched via SerpAPI), to analyze the relationship between Netflix show releases and the popularity of actors. You'll learn to gather and combine data on Netflix actors and their trends on Google, particularly in the weeks following a show's release.You will use Kaggle as a source for the Netflix shows and actors dataset and Google Trends (accessed via SerpAPI) to fetch real-time search data for the actors. This data will be stored and processed within the Snowflake database, leveraging its cloud-native architecture for optimal scalability and performance.Technical Stack Overview:Snowflake Database: The central repository for storing and querying data.Streamlit in Snowflake: A web app framework to visualize the data directly inside Snowflake.AWS S3: For data storage and retrieval, particularly for intermediate datasets.Snowflake Python Procedures: Automating data manipulation and pipeline processes.Snowflake External Access & Storage Integrations: Managing secure access to external services and storage.By the end of the course, you'll have a fully functional data pipeline that processes and combines streaming data, cloud storage, and APIs for trend analysis, visualized through an interactive Streamlit app within Snowflake. Who this course is for: Data Engineers looking to get proficient on Snowflake and Streamlit for building data pipelines Homepage https://www.udemy.com/course/data-pipelines-with-snowflake-and-streamlit/ Rapidgator https://rg.to/file/f0629de1f792eeeebd379ae716fd2bad/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part1.rar.html https://rg.to/file/7f9c73ecc73cc0f8d2c385710cbea0fa/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part2.rar.html https://rg.to/file/6731a5ce1a015365253b5a46c6bf42a0/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part3.rar.html Fikper Free Download https://fikper.com/hKSvOh0u9B/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part1.rar.html https://fikper.com/WRYC5cNE7A/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part2.rar.html https://fikper.com/GRagTmZo87/yjmzv.Data.Pipelines.with.Snowflake.and.Streamlit.part3.rar.html No Password - Links are Interchangeable