Data Analysis with Python Project
Apply advanced data analysis techniques with Python through hands-on projects in machine learning and data mining.
Apply advanced data analysis techniques with Python through hands-on projects in machine learning and data mining.
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 Analysis 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
Design and implement end-to-end data analysis projects
Apply classification and regression algorithms for predictive modeling
Utilize clustering and dimension reduction techniques
Implement association rule mining and outlier detection
Develop professional data analysis portfolio pieces
Skills you'll gain
This course includes:
16.5 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive project-based course focuses on applying advanced data analysis techniques using Python. Students work on real-world data analysis projects, exploring both supervised and unsupervised learning methods. The curriculum covers classification, regression, clustering, dimension reduction, association rules, and outlier detection. Through hands-on experience, students learn to define project scope, implement various algorithms, and make data-driven decisions in practical scenarios.
Data Analysis Overview
Module 1 · 1 Hours to complete
Classification Analysis
Module 2 · 3 Hours to complete
Regression Analysis
Module 3 · 3 Hours to complete
Clustering Analysis
Module 4 · 3 Hours to complete
Dimension Reduction
Module 5 · 1 Hours to complete
Association Rules
Module 6 · 2 Hours to complete
Outlier Detection
Module 7 · 5 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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