References
Thinking in Uncertainty
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Preface
Part 1: Foundations — Thinking Statistically as an Engineer
1
From deterministic to probabilistic thinking
2
Distributions: the type system of uncertainty
3
Descriptive statistics: profiling your data
4
Probability: from error handling to inference
Part 2: Inference — From Hypothesis to Evidence
5
Hypothesis testing: the unit test of data science
6
Confidence intervals: error bounds for estimates
7
A/B testing: deploying experiments
8
Bayesian inference: updating beliefs with evidence
Part 3: Modelling — From Architecture to Algorithms
9
Linear regression: your first model
10
Logistic regression and classification
11
Regularisation: preventing overfitting
12
Tree-based models: when straight lines aren’t enough
13
Dimensionality reduction: refactoring your feature space
14
Clustering: unsupervised pattern discovery
15
Time series: modelling sequential data
Part 4: Engineering for Data Science
16
Reproducible data science
17
Data pipelines: ETL and feature stores
18
Model deployment and MLOps
19
Working with data at scale
20
Testing data science code
Part 5: Applied Data Science — Industry Contexts
21
Predicting customer churn
22
Building a recommendation system
23
Demand forecasting
24
Fraud detection
25
Natural language processing for business
26
Computer vision fundamentals
References
Appendices
A
Mathematical foundations refresher
B
The SE to DS concept bridge
C
Recommended reading and resources
D
Exercise answers
References
Code
[1]
Y. Hu, Y. Koren, and C. Volinsky,
“Collaborative filtering for implicit feedback datasets,”
in
Proceedings of the 8th IEEE international conference on data mining
, 2008, pp. 263–272.
26
Computer vision fundamentals
A
Mathematical foundations refresher
Source Code
# References {.unnumbered}
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