Curated learning path for Data Curation & Quality. Build practical skills through expert-selected courses.
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Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Follow these courses in order to complete the learning path. Click on any course to enroll.
Part of the Johns Hopkins Data Science Specialization, this course covers the essential steps of obtaining and cleaning data, a critical prerequisite for any statistical analysis.
Part of the Google Data Analytics Professional Certificate, this course teaches how to clean data using spreadsheets and SQL, covering topics like data integrity, data validation, and handling missing data.
This course provides a thorough introduction to handling missing data in R, with a focus on multiple imputation using the 'mice' package.
Learn how to identify and fix common data quality issues in R, including missing values, outliers, and inconsistent data.
This beginner-level course teaches how to develop curation files to document information about datasets and related business processes. It is a foundational course for anyone looking to get into data curation.
One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision making Improve the efficiency Increase productivity Remove the errors and inconsistencies from the dataset Identifying missing values Remove duplication Why should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions whe
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Curated learning path for Data Curation & Quality. Build practical skills through expert-selected courses.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Perform exploratory data analysis (EDA) on complex datasets; Clean and preprocess data for analysis; Create compelling data visualizations.
It's pitched at beginner level — a solid starting point if you're new to the topic.
6 curated courses, sequenced from foundational to advanced.
Around $144 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.