Published 4/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.48 GB | Duration: 7h 42m
Master data analysis, visualization, web scraping, and machine learning using Python and Streamlit.
What you’ll learn
Students will learn data science basics, including data preparation, analysis, and visualization using Python, Pandas, and Seaborn.
Learners will master machine learning models, data scraping, and using Streamlit to build interactive data applications.
The course teaches data analysis, correlation techniques, and multivariate analysis to analyze and interpret real-world data.
Students will gain hands-on experience building data-driven projects, from data cleaning to visualization and machine learning.
Requirements
Basic understanding of programming concepts and Python is recommended to get the most out of this course.
Familiarity with fundamental statistical concepts will help learners grasp the material quickly, though no advanced knowledge is required.
Students should have access to a computer with an internet connection to follow along with coding exercises and projects.
No prior experience in data science is required. This course is suitable for beginners looking to dive into data science with practical examples and applications.
Description
This comprehensive course is designed for beginners to intermediate learners who want to dive deep into the world of data science and machine learning. Whether you are looking to start a career in data science or enhance your analytical skills, this course will provide you with the practical knowledge you need.Throughout the course, you will learn how to prepare and clean data using popular Python libraries like Pandas and Numpy. You’ll gain hands-on experience with data visualization using tools like Seaborn and Matplotlib, creating meaningful visual insights from complex datasets.We will also cover essential machine learning concepts, teaching you how to build and evaluate models. You’ll learn how to automate data collection with web scraping techniques using BeautifulSoup and requests, allowing you to gather and analyze data from the web.One of the highlights of this course is the practical application of Streamlit, a tool for creating interactive web applications. You will learn how to build, deploy, and share your own data applications, making it easier to present your findings to stakeholders.By the end of this course, you will have a strong foundation in data science, equipped with the skills to analyze datasets, create predictive models, and build data-driven applications. You’ll be ready to tackle real-world challenges and apply machine learning to solve business problems.Join now and start your journey to becoming a skilled data scientist!
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