courseraАнализ данных

Hands-On Data Science with PyTorch & Pandas

Курс от Packt

Средний≈ 7.6 чАнглийский

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Data science is transforming how organizations analyze information, build intelligent systems, and create interactive data applications. In this course, you will gain hands-on experience using Python tools such as PyTorch, Pandas-style data workflows, and Shiny for Python to build powerful data-driven applications. You will learn how to visualize data, create dashboards, and implement machine learning workflows using modern data science tools and libraries. The course begins by introducing interactive data applications using Shiny. You will learn how to design responsive user interfaces, implement inputs and outputs, and deploy interactive apps directly from development environments like VSCode. Through guided demonstrations and official Shiny examples, you will understand how real-world dashboards and analytical tools are built for data exploration. Next, the course walks you through building a complete CSV data dashboard. You will implement file uploads, compute quick statistics, and create dynamic visualizations such as histograms, bar charts, and pie charts. By the end of this section, you will understand how to transform raw data into interactive visual insights. In the final modules, you will explore PyTorch fundamentals, including tensors, broadcasting, indexing, GPU acceleration, and tensor operations. You will then apply these skills to build a real-world image classification application using PyTorch and TorchVision integrated with a Shiny interface. This course is designed for aspiring data scientists, Python developers, and analytics professionals who want practical experience building data applications and machine learning systems. Basic knowledge of Python programming and data handling concepts is recommended, and the course is suitable for learners at an intermediate level. By the end of the course, you will be able to build interactive data dashboards, manipulate and analyze datasets, implement PyTorch tensor operations, and deploy machine learning–powered applications using Python and Shiny.