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.
Using Data to Find Data: Data Discovery & Exploration
Course Overview
recognize the differences between data exploration and data discovery
list the components of the Exploratory Data Analysis framework, which is involved in finding data facts
recall the tools that can be used to facilitate data Exploration
describe how Knime can be used to apply data exploration and data finding methods
describe the techniques that can be used to implement data transformation in order to get essential data attributes
pivot data using Python to find informative data
recognize the need for data de-identification and describe data de-identification techniques
recall the need for data quality analysis and the intended outcomes of the analysis
recognize the need for data tracing and describe data tracing techniques
describe the essential building blocks of finding data using data
recall essential data exploration tools, identify critical data transformation and tracing techniques, and specify the building blocks of using data to find data