Assignments
53
Data Engineering
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
Model Types
24
Feature and Target Processing
25
Feature Engineering
26
Messy Data
Module V. Communication
27
Introduction
28
The Science of Communication
29
Storytelling
30
Presenting
31
Nonverbal Cues
32
Metaphors, Analogies, and Similes
Module VI. Operationalization
33
Introduction
34
Offline and Online Operation
35
System Architectures
36
REST APIs for Data Science
37
Orchestration
38
Lead Scoring Tutorial
39
Data Science Software Engineering
40
Data Science Tools
Module VII. Applied Ethics in Data Science
41
Introduction
42
How Things Go Wrong
43
Bias and Harm in Algorithms
44
Personal Information and Personal Data
45
Project Lifecycle
46
Ethics of Generative AI
Module VIII. Review Topics
47
Probability
48
Statistics
49
Linear Algebra
50
Estimation
Assignments
51
Understanding Data Science
52
Business Understanding: Discovery
53
Data Engineering
54
Communication
55
Applied Ethics & Responsible Data Science
References
Assignments
53
Data Engineering
53
Data Engineering
52
Business Understanding: Discovery
54
Communication