Data Wrangling with Python Project
Apply practical data wrangling techniques in Python through a hands-on project, from raw data collection to refined analysis-ready datasets.
Apply practical data wrangling techniques in Python through a hands-on project, from raw data collection to refined analysis-ready datasets.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Wrangling with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Conduct end-to-end data wrangling projects from raw data to refined datasets
Apply data collection and integration techniques effectively
Perform statistical analysis and create meaningful visualizations
Implement data processing and manipulation methods
Develop professional data wrangling pipelines
Skills you'll gain
This course includes:
0.5 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This project-based course provides hands-on experience in data wrangling using Python. Students work through the complete data wrangling pipeline, from identifying data sources to processing and integrating data. The curriculum covers key stages including data collection, validation, integration, statistical analysis, visualization, and manipulation techniques. Through practical assignments, participants learn to handle real-world data challenges and prepare datasets for analysis, gaining essential skills in data preparation and transformation.
Data Wrangling Pipeline
Module 1 · 3 Hours to complete
Identify Your Data
Module 2 · 4 Hours to complete
Data Collection and Integration
Module 3 · 4 Hours to complete
Data Understanding and Visualization
Module 4 · 4 Hours to complete
Data Processing and Manipulation
Module 5 · 10 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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