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.

This course provides learners with an in-depth understanding of data cleaning techniques in Data Science & Machine Learning. It covers topics such as data reading, merging or splitting datasets, different visualization tools, locating or handling missing/absurd values, and hands-on sessions. By enrolling in this course, learners will gain the knowledge and skills necessary to effectively clean data for Data Science & Machine Learning. They will also understand why data cleaning is important, and how it can improve decision making, efficiency, and productivity. Learners will have the opportunity to practice their data cleaning skills with a dataset. This course is perfect for new learners who want to gain a comprehensive understanding of data cleaning techniques in Data Science & Machine Learning.