Tutorial of Deployment of Web App by Python and Streamlit for Data Scientist

: Embark on a Journey of Interactive Data Visualization

Welcome to the realm of data science, where the ability to effectively communicate insights and predictions is paramount. Leveraging the power of Python and Streamlit, this tutorial will guide you through the intricate yet rewarding process of deploying interactive web applications that seamlessly showcase your data science prowess. Whether you aspire to bolster your portfolio, demonstrate your capabilities, or propel your career forward, this comprehensive guide will empower you with the knowledge and skills necessary to excel.

Chapter 1: to Streamlit

Embark on an enlightening journey into the world of Streamlit, a Python-based framework specifically tailored for the development of data science web applications. Discover its intuitive and user-friendly interface, which allows you to effortlessly create visually appealing and informative dashboards, charts, and interactive visualizations. As you delve deeper into this chapter, you will master the art of crafting interactive widgets that empower users to explore data, adjust parameters, and gain actionable insights.

Tutorial of a Deployment of a Web app by Python and Streamlit for a Data Scientist

by Agnieszka Bates

5 out of 5

Language	:	English
File size	:	2861 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	109 pages
Lending	:	Enabled
Paperback	:	46 pages
Item Weight	:	6.2 ounces
Dimensions	:	8.27 x 0.12 x 11.69 inches

FREE

Chapter 2: Setting Up Your Development Environment

Lay the foundation for your data science web application endeavors by establishing a robust development environment. This chapter meticulously guides you through the installation of essential Python libraries, including Streamlit and its dependencies. You will also learn how to set up a virtual environment, ensuring the isolation and management of your project-specific dependencies. By the end of this chapter, you will have a fully equipped and customized development environment, ready to accommodate your web application creations.

Chapter 3: Building Your First Web App

Witness the transformation of your data science models into captivating web applications as you embark on the creation of your first interactive masterpiece. This chapter provides a step-by-step tutorial, guiding you through the development of a basic web app that showcases a simple data visualization. You will learn how to integrate your data, construct interactive charts, and deploy your web app locally. As you progress through this chapter, you will gain hands-on experience with the core concepts of Streamlit and lay the groundwork for more complex applications.

Chapter 4: Enhancing Your Web App with Widgets

Elevate your web application to new heights of interactivity by incorporating a variety of widgets, each designed to empower users with control over data exploration and visualization. This chapter introduces you to the diverse range of widgets available in Streamlit, covering options for user input, data selection, and dynamic content manipulation. You will learn how to seamlessly integrate these widgets into your web app, enhancing the user experience and enabling deeper exploration of your data.

Chapter 5: Data Manipulation and Processing

Unleash the full potential of your data by mastering the art of data manipulation and processing within your Streamlit web applications. This chapter delves into the techniques for loading, cleaning, and transforming data, ensuring its accuracy and suitability for analysis and visualization. You will also explore the use of Python libraries for data manipulation, empowering you to perform complex operations on your data with ease. By mastering the skills in this chapter, you will elevate your web applications to become robust and data-driven tools.

Chapter 6: Deployment and Sharing

Showcase your data science prowess by deploying your web applications to the world. This chapter provides a comprehensive guide to deploying your creations on platforms such as Heroku, Streamlit Cloud, and AWS. You will learn how to configure your web app for deployment, manage user access, and ensure the smooth operation of your application in a production environment. By mastering the techniques in this chapter, you will gain the confidence to share your data science insights with a wider audience and establish yourself as a thought leader in your field.

: Unveiling the Power of Data Visualization

In the concluding chapter, reflect on the journey you have undertaken, from the fundamentals of Streamlit to the deployment of interactive web applications. Explore the broader implications of data visualization, its impact on decision-making, and its role in shaping the future of data science. As you complete this tutorial, you will possess the knowledge, skills, and confidence to leverage the power of Python and Streamlit to transform your data science models into impactful and actionable tools. Embrace the opportunities that lie ahead and continue to innovate at the intersection of data science and web development.

Tutorial of a Deployment of a Web app by Python and Streamlit for a Data Scientist

by Agnieszka Bates

5 out of 5

Language	:	English
File size	:	2861 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	109 pages
Lending	:	Enabled
Paperback	:	46 pages
Item Weight	:	6.2 ounces
Dimensions	:	8.27 x 0.12 x 11.69 inches

FREE