University of Colorado Boulder - Data Processing and Manipulation
- Offered byCoursera
Data Processing and Manipulation at Coursera Overview
Duration | 28 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Processing and Manipulation at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 5 quizzes, 1 assignment
Data Processing and Manipulation at Coursera Course details
- 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.
- 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.
- Learning Objectives:
- 1. Understand the importance of data processing and manipulation in the data analysis pipeline.
- 2. Learn techniques to handle missing values in datasets, including imputation and exclusion strategies.
- 3. Identify and detect outliers to assess their impact on data analysis and decision-making.
- 4. Explore sampling methods and dimension reduction techniques for large datasets and high-dimensional data.
- 5. Apply data scaling techniques to normalize and standardize variables for meaningful comparisons.
- 6. Utilize discretization to transform continuous data into categorical representations, simplifying analysis.
- 7. Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.
- 8. Create pivot tables to summarize and reshape data, gaining valuable insights from complex datasets.
- Throughout the course, students will actively engage in practical exercises and projects, allowing them to apply data processing and manipulation techniques to real-world datasets. By the end of the course, participants will be well-equipped to effectively prepare, clean, and transform data for subsequent analysis tasks and data-driven decision-making.
Data Processing and Manipulation at Coursera Curriculum
Missing Values and Outliers
Missing Values
Outliers Detection using Statistics
Outliers Detection using IQR
Assessment Strategy
Activity Strategy
Missing Values Demo
Outliers Detection using Statistics Demo
Outliers Detection using IQR
Missing Values Quiz
Outliers Detection Quiz
Missing Value and Outliers Detection Exploration Exercise
Data Reduction
Dimension Elimination
Sampling
Dimension Elimination Demo
Sampling Demo
Data Reduction Case Study
Data Reduction Quiz
Data Reduction Exploration Exercise
Scaling and Discretization
Data Scaling
Data Discretization
Data Scaling Demo
Data Discretization Demo
Scaling and Discretization Case Study
Scaling and Discretization Quiz
Scaling and Discretization Exploration Exercise
Data Warehouse
Pivot Table
Data Cube
Pivot Table Demo
Data Cube Demo
Data Warehouse Case Study
Data Warehouse Quiz
Self Reflection
Data Warehouse Exploration Exercise
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