Data professionals working with various data management systems must be able to implement data correction by using R and have a good understanding of data and data management systems. In this 12-video course, learners explore how to apply and implement various essential data correction techniques; to follow transformation rules; and to use deductive correction techniques and predictive modeling by using critical data and analytical approaches. Learn more about data wrangling, essentially the process of transforming and mapping data into another format to ensure that data are appropriate for analytical requirements. Along the way, you will learn key terms and concepts, including how to design data dimension; dimensional data design; cleansing data, and cleansing data with Python; data operations for fact finding; and common data operations for fact-finding. Next, learn about data categorization with Python; data visualization in general; and data visualization with Python. In a concluding exercise, you create a series data set by using Python; create a data frame using the series data; and, finally, calculate the standard deviation of the data frame.
Using Data to Find Data: Correction & Categorization