Data Cleaning Online Courses & Certifications
Data cleaning, also known as data cleansing or data scrubbing, refers to the process of identifying, correcting, and removing errors, inconsistencies, and inaccuracies in a dataset. When working with real-world data, it’s common to encounter issues such as missing values, duplicate entries, incorrect formatting, outliers, and contradictory information. Data cleaning is a critical step in the data preprocessing pipeline, as it ensures that the data is accurate, reliable, and suitable for analysis or modeling.
Data cleaning is a crucial step to ensure the quality and reliability of the data before any analysis or modeling is performed. If data quality issues are not addressed, they can lead to incorrect conclusions, biased results, and poor decision-making. Automated tools and scripts can assist in the data cleaning process, but human oversight and domain knowledge are often necessary to make informed decisions about how to handle specific data issues.
People say that data scientists spend 80% of their time cleaning data and only 20% of their time doing analysis. Learn some of the most common techniques for getting your data ready to analyze. Along the way, you’ll learn the basics of Regex, a fun and powerful tool to find patterns in strings.