Course Description

Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. This process is essential for ensuring that data is accurate, complete, and reliable, which is necessary for making informed decisions and drawing meaningful insights. The course on Data Cleaning is designed to provide learners with the skills and knowledge they need to effectively clean and prepare data for analysis. The course will cover a range of topics, including data quality, data preprocessing, and data wrangling. Learners will gain an understanding of the different types of errors that can occur in datasets, such as missing data, duplicate records, and outliers, and learn how to identify and correct these errors using a variety of techniques. They will also learn how to preprocess data by transforming it into a format that is suitable for analysis, such as standardizing variables and normalizing data. In addition, the course will cover data wrangling, which involves the process of transforming and restructuring data to make it easier to analyze. This can include tasks such as merging datasets, reshaping data, and aggregating data. Learners will gain hands-on experience with popular data wrangling tools, such as R and Python, and learn how to apply these tools to real-world datasets. The course will also cover best practices for data cleaning, such as documenting the cleaning process and ensuring that all changes are reversible. Learners will learn how to document their cleaning process using tools such as Jupyter notebooks and GitHub, which can help ensure that their work is reproducible and transparent. Overall, the course on Data Cleaning is ideal for anyone who wants to learn how to effectively clean and prepare data for analysis. This includes data analysts, data scientists, and researchers who work with large datasets. By the end of the course, learners will have a solid understanding of the data cleaning process and the skills they need to prepare data for analysis. They will be able to apply these skills to real-world datasets and make informed decisions based on accurate and reliable data. Author: Rachael Tatman (Kaggle)