Overview
Data validation is the process of ensuring that data is accurate, complete, and consistent. It involves verifying that data meets certain criteria, such as data type, value range, and formatting rules. Data quality control, on the other hand, is a broader process that includes all activities related to maintaining and improving the quality of data. It involves identifying and addressing data quality issues, implementing processes to improve data quality, and ensuring that data is fit for purpose. Data validation and quality control are important in data wrangling, which is the process of preparing data for analysis. During data wrangling, data may be sourced from various systems, and it may need to be cleaned, transformed, or combined with other data. This process can introduce errors or inconsistencies, which can negatively impact the accuracy and reliability of subsequent analyses.
In this course, you will:
- Understand the importance of data validation and quality control
- Use data profiling techniques to identify data quality issues
- Evaluate the different methods for analyzing missing values
- Evaluate the effectiveness of outlier detection techniques
- Discuss various approaches to measure data accuracy
Course Contents
1. Data Profiling
- What is data profiling?
- Why is it important?
- Conducting data type analysis
- Conducting value frequency analysis
- Conducting value distribution analysis
- Conducting data uniqueness analysis
- Conducting statistical summary analysis
- Conducting data correlation analysis
- Knowledge check
2. Missing Value Analysis
- What is missing values analysis?
- Why is it important?
- Identify missing values
- Knowledge check
3. Outlier Detection
- What is outlier detection?
- Why is it important?
- Applying z-score method
- Applying interquartile range method
- Applying local outlier factor method
- Applying median absolute deviation method
- Applying box plot method
- Knowledge check
4. Checking Data Accuracy
- What is checking data accuracy?
- Why is it important?
- Validating data range
- Validating data format
- Knowledge check
