University of Colorado Boulder
Data Processing and Manipulation
University of Colorado Boulder

Data Processing and Manipulation

This course is part of Data Wrangling with Python Specialization

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

28 hours (approximately)
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.

Details to know

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Assessments

5 quizzes, 1 assignment

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

28 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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 quizzes1 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 quiz1 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 quiz1 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 readings1 quiz1 assignment1 discussion prompt

Instructor

Di Wu
University of Colorado Boulder
15 Courses32,623 learners

Offered by

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