Module IV. Modeling Data
25
Messy Data
Foundations of Data Science
Preface
1
Introduction
Module I. Understanding Data Science
2
History and Evolution
3
The Project Lifecycle
4
Data Science Teams
5
Thinking Like a Data Scientist
Module II. Business Understanding: Discovery
6
Introduction
7
Approach and Methodology
8
Example: Email Campaign Optimization
Module III. Data Engineering
9
Introduction
10
File Formats and Data Sources
11
Data Access
12
SQL Basics
13
Data Quality
14
Summarization
15
Visualization
16
Data Integration
17
Project
Module IV. Modeling Data
18
Introduction
19
General Concepts
20
Correlation and Causation
21
The Bias-Variance Tradeoff
22
Testing, Validation, Cross-Validation
23
Feature and Target Processing
24
Feature Engineering
25
Messy Data
Module V. Communication
26
Introduction
27
The Science of Communication
28
Storytelling
29
Presenting
30
Nonverbal Cues
31
Metaphors, Analogies, and Similes
Module VI. Operationalization
32
Introduction
33
Offline and Online Operation
34
System Architectures
35
REST APIs for Data Science
36
Orchestration
37
Lead Scoring Tutorial
38
Data Science Software Engineering
39
Data Science Tools
Module VII. Applied Ethics in Data Science
40
Introduction
41
How Things Go Wrong
42
Bias and Harm in Algorithms
43
Personal Information and Personal Data
44
Ethics of Generative AI
Module VIII. Review Topics
45
Probability
46
Statistics
47
Linear Algebra
48
Estimation
References
Table of contents
25.1
Low Signal to Noise
25.2
Unbalanced Data
25.3
Outliers
25.4
Missing Data
Module IV. Modeling Data
25
Messy Data
25
Messy Data
25.1
Low Signal to Noise
25.2
Unbalanced Data
25.3
Outliers
25.4
Missing Data
24
Feature Engineering
26
Introduction