Module VI. Operationalization
49
Assignments & Exercises
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
6
Quantitative Intuition
7
Assignments & Exercises
Module II. Business Understanding: Discovery
8
Introduction
9
Approach and Methodology
10
Example: Email Campaign Optimization
11
Assignments & Exercises
Module III. Data Understanding & Engineering
12
Introduction
13
File Formats and Data Sources
14
Data Access
15
SQL Basics
16
Data Quality
17
Summarization
18
Visualization
19
Data Integration
20
Project
21
Assignments & Exercises
Module IV. Modeling Data
22
Introduction
23
General Concepts
24
Correlation and Causation
25
The Bias-Variance Tradeoff
26
Testing, Validation, Cross-Validation
27
Model Types
28
Feature and Target Processing
29
Feature Engineering
30
Messy Data
31
Assignments & Exercises
Module V. Communication
32
Introduction
33
The Science of Communication
34
Storytelling
35
Presenting
36
Nonverbal Cues
37
Listening
38
Metaphors, Analogies, and Similes
39
Teams
40
Assignments & Exercises
Module VI. Operationalization
41
Introduction
42
Offline and Online Operation
43
System Architectures
44
REST APIs for Data Science
45
Orchestration
46
Lead Scoring Tutorial
47
Data Science Software Engineering
48
Data Science Tools
49
Assignments & Exercises
Module VII. Applied Ethics in Data Science
50
Introduction
51
How Things Go Wrong
52
Bias and Harm in Algorithms
53
Personal Information and Personal Data
54
Project Lifecycle
55
Ethics of Generative AI
56
Assignments & Exercises
Module VIII. Review Topics
57
Introduction
58
Probability
59
Statistics
60
Linear Algebra
61
Estimation
62
Classical Linear Model
References
Module VI. Operationalization
49
Assignments & Exercises
49
Assignments & Exercises
48
Data Science Tools
50
Introduction