Course details

Using Data to Find Data: Data Discovery & Exploration

Using Data to Find Data: Data Discovery & Exploration


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.



Expected Duration (hours)
0.9

Lesson Objectives

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
  • Course Number:
    it_dsudfddj_01_enus

    Expertise Level
    Intermediate