This course provides a comprehensive introduction to Trifacta, a data preparation tool that can help users quickly and easily clean their data. It covers the basics of Trifacta, including how to use its transforms and functions, as well as common pitfalls to avoid. Learners will also gain an understanding of why data preparation is so important and how Trifacta Wrangler can help save time and give them a competitive edge. With this course, users can quickly and easily clean their data and gain a competitive edge.

This Excel Data Cleaning Fundamentals course is perfect for new learners who want to learn how to detect and fix errors in datasets imported into Excel for analysis. It provides step-by-step instructions, follow-along exercises, case study exercises, and quizzes to teach learners how to use basic to advanced Excel functions, concepts, and techniques in a fast and efficient way. No need for macros or any Excel add-on tools - the techniques taught in this course are simple yet effective. Click now to start learning and become an Excel data cleaning expert!

This course on SPSS: Cleaning and Preparing Your Data For Accurate Analyses is designed to help new learners understand the importance of data cleaning and preparing data for accurate analyses. It provides step-by-step examples, PowerPoint slides, and other helpful supporting materials to help learners gain the technical skills needed to conduct rigorous research studies. It is suitable for doctoral students, undergraduate students, early career researchers, and those who wish to learn about the process of data cleaning. Learn how to anticipate cleaning before or during collection, understand why not all missing data are the same, the importance of recording the cleaning process and decisions, and what part of cleaning to report in a manuscript. Click now to get started!

This course provides learners with the skills and knowledge necessary to clean and preprocess data in Python for Machine Learning. It covers a range of data cleaning techniques, such as imputing missing values, feature scaling, fixing data types issues, and more. Through lectures, quizzes, and Jupyter notebooks, learners will gain the skills to deal with real world raw data and be able to detect and remove outliers, clean and preprocess textual data for NLP, and use the Apply Lambda method for advanced cleaning functions. Click this course to gain the skills and knowledge to clean and preprocess data in Python for Machine Learning.