Coursera – Data Wrangling with Python Specialization

Coursera – Data Wrangling with Python Specialization
English | Tutorial | Size: 833 MB


Launch your career in Data Science. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.

This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.

Applied Learning Project

The final project provides students with an opportunity to apply the knowledge gained throughout the specialization in a real-life data wrangling project of their interest. Participants will follow the data wrangling pipeline step by step, from identifying data sources to processing and integrating data, to achieve a fine dataset ready for analysis. This course enables students to gain hands-on experience in the data wrangling process and prepares them to handle complex data challenges in real-world scenarios.

Buy Long-term Premium Accounts To Support Me & Max Speed


RAPIDGATOR
rapidgator.net/file/9629efa062684323198e2f657b9a726a/Coursera_-_Data_Wrangling_with_Python_Specialization.part1.rar.html
rapidgator.net/file/d397511d6e757c2a1927ba52769f7d3d/Coursera_-_Data_Wrangling_with_Python_Specialization.part2.rar.html

NITROFLARE
nitroflare.com/view/ACCA7AFAE5B5856/Coursera_-_Data_Wrangling_with_Python_Specialization.part1.rar
nitroflare.com/view/7EC393F0C190B22/Coursera_-_Data_Wrangling_with_Python_Specialization.part2.rar

If any links die or problem unrar, send request to goo.gl/aUHSZc

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.