Course Description
Data quality can make or break your analysis. Good techniques can't make up for bad data. This course will teach you how to assess the quality of your data and how well your data will serve to answer your questions.
What am I going to get from this course?
Get a better understanding of context and limitations of data. Understand how well-suited
data are for generating meaningful analyses.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
None required. Some experience working with data and some knowledge of statistics will be helpful.
Who should take this course? Who should not?
Anyone who works with data.
Curriculum
Lecture 2
Why Do We Need to Understand Data?
Lecture 3
Biggest Problems with Data
Lecture 4
Data Science Is Both Science and Art
Module 2: Creating a Roadmap for Your Analysis
Lecture 5
Why Do I Need an Analysis Roadmap?
Lecture 6
Creating an Analysis Roadmap, Part 1
Lecture 7
Creating an Analysis Roadmap, Part 2
Lecture 9
Exercise 1 - Explanation
Module 3: Understanding Your Data-Generating System, Part I
Lecture 10
Overview of Data Generating System
Lecture 11
Data Generating System - Content Provider
Lecture 12
Data Generating System - Data Collector
Lecture 13
Data Generating System - Customer
Lecture 14
Data Generating System - Client
Lecture 15
Exercise 2: Understanding Data Collector, Customer, Client
Lecture 16
Exercise 2 - Explanation: Understanding Data Collector, Customer, Client
Module 4: Understanding Your Data-Generating System, Part II
Lecture 17
Understanding Your Data-Generating System, Part II
Lecture 18
Context/Environment: The Situation
Lecture 19
Context/Environment: Choice Architecture
Lecture 20
Context/Environment: TED Talk Example
Lecture 21
Context/Environment: Summary/Conclusions
Lecture 22
Exercise 3: Understanding Context/Environment
Lecture 23
Exercise 3 - Explanation: Understanding Context/Environment
Module 5: Clearly Understanding Your Data
Lecture 24
Why Do I Need to Clearly Understand My Data?
Lecture 25
What Information Does Your Data Capture?
Lecture 26
What Do the Variables Look Like?
Lecture 27
Exercise 4: Understanding Your Data
Lecture 28
Exercise 4 - Explanation: Understanding Your Data
Module 6: Understanding Limitations of Your Data
Lecture 29
Why Do I Need to Understand Limitations of Data?
Lecture 30
Are the Data Biased?
Lecture 31
Are the Data Bad Proxies?
Lecture 32
Are the Data Inconsistent?
Lecture 33
Did Context/Environment Affect the Data?
Lecture 34
Exercise 5: Understanding Limitations of the Data
Lecture 35
Exercise 5 - Explanation: Understanding the Limitations of the Data
Module 7: Summary and Conclusions
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