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Complete course summary for Data Science Methods at Universiteit van Amsterdam, covering Python fundamentals, model evaluation, regularization, dimensionality reduction, classification, clustering, and Bayesian updating. The document includes lecture notes, tutorials, worked exam exercises, and a quick-reference guide spanning core topics from Python basics (lists, dictionaries, NumPy, Pandas) through advanced techniques like PCA, logistic regression, LDA, and model averaging. Ideal for exam preparation and consolidating material across the full course—saves time by having all key concepts and worked examples in one structured resource.

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Data Science Methods
Complete Course Summary

Python Fundamentals, Model Evaluation & Regularization,
Dimensionality Reduction, Classi
cation, Clustering &
Bayesian Updating, and Model Averaging

Lecture Notes, Tutorials, Worked Exam Exercises & Quick-Reference Guide

,Data Science Methods Complete Course Summary 1

Contents
I Lecture Notes & Tutorials 3
1 Introduction to Python 3
1.1 Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Tuples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 Control Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.6 Input, Functions & Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.7 String Formatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.8 Mathematical Sequences (Fibonacci) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.9 NumPy Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.10 Pandas Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.11 Function Calls & Code Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Object-Oriented Programming 5
2.1 Classes and Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Attributes and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Inheritance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4 Polymorphism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.5 Practical Example: Linear Regression Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 Model Evaluation 6
3.1 Linear Model for Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Determine Model Parameters using Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Types of Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.4 Bias-Variance Decomposition and Trade-O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.5 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.6 Data-Rich Situations: Many Data Available . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.7 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4 Dimensionality & Nonparametric Methods 9
4.1 Shrinkage Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Dimension Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.3 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.4 Selecting the Optimal L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.5 Nonparametric Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5 LDA and Logistic Regression 12
5.1 Classi
cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2 Bayes Classi
er . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.3 Decision Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.4 Linear Classi
ers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.5 Linear Probability Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.6 Linear Discriminant Analysis (LDA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.7 Quadratic Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.8 Receiver Operating Characteristic (ROC) Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.9 Logistic Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.10 Stochastic Gradient Descent (SGD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.11 Comparison Logistic Regression and LDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

6 Clustering and Bayesian Updating 17
6.1 Clustering Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.2 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.3 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.4 Bayesian Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.5 Bayes Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.6 Priors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.7 Bayesian Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.8 Bayes Estimator (Squared Loss) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

,Data Science Methods Complete Course Summary 2

6.9 Bayes Estimator (Absolute Loss) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7 Model Averaging 21
7.1 Model Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.2 The Simplest Way for Model Averaging Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.3 Granger-Ramanathan Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.4 Expectation-Maximization (EM) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

II Additional Worked Exam Exercises 25
8 Generalization Error & Bias-Variance (Exam Exercises) 26
9 PCA & Dimensionality Reduction (Exam Exercises) 26
10 Classi
cation: LDA & k-NN (Exam Exercises) 28
11 Clustering & Bayesian Updating (Exam Exercises) 30
12 Code Questions (Debugging & Reading Python) 31
13 Machine Learning Methods Quick Reference 32
13.1 1. Classi
cation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13.2 2. Non-Parametric Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13.3 3. Regression & Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13.4 4. Optimization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13.5 5. Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
13.6 6. Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
13.7 7. Fisher's Discriminant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
13.8 Key Formulas Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

, Data Science Methods Complete Course Summary 3

Part I
Lecture Notes & Tutorials
1 Introduction to Python
1.1 Lists
# Create
a = [1 ,2 ,3]
# Add element
a . append (4)
# Insert element at position
a . insert (2 , " June " )
# Negative indexing
a [ -1] # last element
# Loop through list
for i in a :
print ( i *2)
# Array indexing with indices
indices = [0 ,2 ,1]
result = a [ indices ]

1.2 Tuples
# Create
t = (2 ,2 ,3)
# Immutable ! Cannot be changed

1.3 Dictionaries
# Create
d = { 'a ' :1}
# Add element
d [ 'b '] = 2
# Remove element
d . pop ( 'a ')

1.4 Sets
# Unique values , unordered
s = {1 ,2 ,3}
# Add element
s . add (4)

1.5 Control Flow
# If - else
if x <0:
print ( " negative " )
elif x ==0:
pass
else :
print ( " positive " )

# For - loop
a = [1 ,2 ,3 ,4]
for i in a :
print ( i *2)

# While - loop with stopping criterion
n = 5
while n < 10:
print ( n *( n -1) /2)

01 Lists 3
02 Tuples 3
03 Dictionaries 3
04 Sets 3
05 Control Flow 3
06 Input, Functions & Operators 4
07 String Formatting 4
08 Mathematical Sequences (Fibonacci) 4
09 NumPy Basics 4
10 Pandas Basics 5
11 Function Calls & Code Reading 5
12 Classes and Objects 5
13 Attributes and Methods 5
14 Inheritance 6
15 Polymorphism 6
16 Practical Example: Linear Regression Class 6
17 Linear Model for Regression 6
18 Determine Model Parameters using Maximum Likelihood 7
19 Types of Errors 7
20 Bias-Variance Decomposition and Trade-O 7
21 Model Evaluation 7
22 Data-Rich Situations: Many Data Available 7
23 Cross-Validation 8
24 Shrinkage Methods 9
25 Dimension Reduction 9
26 Principal Component Analysis (PCA) 9
27 Selecting the Optimal L 10
28 Nonparametric Regression 10
29 Classication 12
30 Bayes Classier 12
31 Decision Boundary 13
32 Linear Classiers 13
33 Linear Probability Model 13
34 Linear Discriminant Analysis (LDA) 13
35 Quadratic Discriminant Analysis 14
36 Receiver Operating Characteristic (ROC) Curve 14
37 Logistic Regression Model 14
38 Stochastic Gradient Descent (SGD) 15
39 Comparison Logistic Regression and LDA 15
40 Clustering Methods 17
41 K-means Clustering 18
42 Hierarchical Clustering 18
43 Bayesian Updating 18
44 Bayes Estimator 18
45 Priors 19
46 Bayesian Linear Regression 19
47 Bayes Estimator (Squared Loss) 19
48 Bayes Estimator (Absolute Loss) 19
49 Model Averaging 21
50 The Simplest Way for Model Averaging Methods 21
51 Granger-Ramanathan Averaging 21
52 Expectation-Maximization (EM) Algorithm 22
53 13.1 1. Classication Methods 33
1. 2. Non-Parametric Methods 33
2. 3. Regression & Regularization 33
3. 4. Optimization Techniques 33
4. 5. Dimensionality Reduction 33
5. 6. Clustering 34
6. 7. Fisher's Discriminant 34
7. Key Formulas Summary 34

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Study: Financial Econometrics
Uploaded on: August 27, 2026
Number of pages: 35
Written in: 2025/2026
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