The "Data Processing and Manipulation" course provides students with a comprehensive understanding of various data processing and manipulation concepts and tools. Participants will learn how to handle missing values, detect outliers, perform sampling and dimension reduction, apply scaling and discretization techniques, and explore data cube and pivot table operations. This course equips students with essential skills for efficiently preparing and transforming data for analysis and decision-making.
Data Processing and Manipulation
Data Processing and Manipulation
This course is part of Data Wrangling with Python Specialization
Instructor: Di Wu
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
28 hours to complete
3 weeks at 9 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the importance of data processing and manipulation in the data analysis pipeline.
Learn techniques to handle missing values and outliers, data reduction, and data scaling and discretization.
Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
6 assignments
Taught in English
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This course is part of the Data Wrangling with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
The "Missing Values and Outliers" week focuses on how to handle missing values and detect outliers using the Pandas library. You will learn essential techniques to identify and address missing data effectively, as well as methods to detect and manage outliers in datasets.
What's included
3 videos5 readings2 assignments1 discussion prompt
The "Data Reduction" week focuses on how to reduce data through sampling and dimensionality reduction using the Pandas library. You will learn essential techniques to obtain manageable subsets of data while preserving meaningful information for analysis and visualization.
What's included
2 videos3 readings1 assignment1 discussion prompt
The "Scaling and Discretization" week focuses on the importance of data scaling and discretization in the data preprocessing process. You will learn why and how to perform data scaling to normalize variables and handle data with different scales. Additionally, you will explore the concept of data discretization and its application in transforming continuous data into categorical representations.
What's included
2 videos3 readings1 assignment1 discussion prompt
The "Data Warehouse" week focuses on the concepts and methodologies of organizing data using data cubes and pivot tables in Pandas. You will learn the importance of data warehousing for efficient data management and analysis, as well as how to construct data cubes and pivot tables to facilitate multidimensional data exploration.
What's included
2 videos3 readings2 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
Coursera Instructor Network
Duke University
University of Michigan
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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