Description:
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.
Target Audience:
Duration: 00:51
Description:
Explore essential approaches of deriving value from existing data in this 12-video course. Learn to produce meaningful information by implementing certain techniques such as data cleansing, data wrangling, and data categorization. The course goal is to teach learners how to derive appropriate data dimension, and apply data wrangling, cleansing, classification, and clustering by using Python. You will examine such useful data discovery and exploration techniques as pivoting, de-identification, analysis, and data tracing. Learn how to assess the quality of target data by determining accuracy of the data being captured or ingested; data completeness; and data reliability. Other key topics covered include data exploration tools; Knime data exploration; data transformation techniques; and data quality analysis techniques. The concluding exercise asks learners to list prominent tools for data exploration; recall some of the essential types of data transformation that can be implemented; specify essential tasks that form the building block to finding data with data; and recall essential approaches of implementing data tracing.
Target Audience:
Duration: 00:53