Data Processing and Manipulation
Master Python data processing techniques including missing value handling, outlier detection, scaling, and data warehouse concepts.
Master Python data processing techniques including missing value handling, outlier detection, scaling, and data warehouse concepts.
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
Master techniques for handling missing values and outlier detection
Learn data reduction methods through sampling and dimension reduction
Apply scaling and discretization techniques for data preprocessing
Understand data warehouse concepts and multidimensional analysis
Create and manipulate pivot tables and data cubes
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
5 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on essential data processing and manipulation techniques using Python. Students learn to handle missing values, detect outliers, perform data reduction through sampling and dimensionality reduction, apply scaling and discretization methods, and work with data warehouse concepts. The curriculum covers practical applications using Pandas for data transformation, multidimensional analysis using data cubes, and creating pivot tables for complex data exploration.
Missing Values and Outliers
Module 1 · 7 Hours to complete
Data Reduction
Module 2 · 6 Hours to complete
Scaling and Discretization
Module 3 · 6 Hours to complete
Data Warehouse
Module 4 · 7 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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