Data Analysis (A.Y. 2023/24)
Lectures
| Lecture | Date | Topics | References | Additional material (private) |
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01 | 06/03 | Intro to the course | This website | - |
| 02 | 06/03 | Intro to data analysis | [ISL, Ch. 1] | 02-Examples-Intro.zip |
| 03 | 07/03 | Intro to Python, Notebooks, Numpy | [PDA, Ch. 2-4] | 03-Python-Intro.zip |
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04 | 13/03 | Review of Probability I: Axioms of probability, Conditional probability, Bayes theorem, Random variables, Mean and variance, CDF, PMF, PDF, Expected value | [PSES, Ch. 3] | 04-Probability_I.pdf |
| 05 | 13/03 | Review of Probability II: Sample mean and variance, Percentiles, Boxplot, Histogram, Sample correlation coefficient | [PSES, Ch. 4] | 05-Probability_II.pdf |
| 06 | 14/03 | Intro to Python Scipy.stats | - | 06-Python-Scipy-Stats.zip |
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07 | 20/03 | Describing a data set: Sample mean and variance, Percentiles, Boxplot, Histogram, Sample correlation coefficient | [PSES, Ch. 2] | - |
| 08 | 20/03 | Describing a data set with Python Numpy, Matplotlib, Pandas | [PDA, Ch. 4, 9] | 08-Python-Describing-Data-Set.zip |
| 09 | 21/03 | Confidence intervals | [PSES, Ch. 7] | - |
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10 | 27/03 | Estimating a PDF: Maximum Likelihood Estimation | [PSES, Ch. 7] | - |
| 11 | 27/03 | Confidence intervals and PDF estimation with Python | - | 11-Python-CI-ML.zip |
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12 | 10/04 | Introduction to hypothesis testing I: Null and alternative hypotheses, Level of significance, p-value | [SAGE, HT] | - |
| 13 | 10/04 | Introduction to hypothesis testing II: Courtroom trial analogy, Types of error, One-sided and two-sided tests | [SAGE, HT] | - |
| 14 | 11/04 | t-Tests on the mean: 1-sample, 2-sample, paired t-Test | [PSES, Ch. 8, HT] | - |
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15 | 17/04 | t-Tests with Python | - | 15-Python-t-Tests.zip |
| 16 | 17/04 | Tests on variance and proportion: Chi-2 test (variance), F-test, z-Test | [PSES, Ch. 8] | - |
| 17 | 18/04 | Tests on variance and proportion with Python | - | 17-Python-Other-Tests-I.zip |
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18 | 24/04 | Tests on uncorrelation and independence (categorical variables): t-Test, Chi-2 test | [Wikipedia], [PSES, Sect. 11.4] | - |
| 19 | 24/04 | Tests on uncorrelation and independence with Python | - | 19-Python-Other-Tests-II.zip |
| 20 | 02/05 | Further topics in hypothesis testing: Other common tests, Normality assumption, Connection with confidence intervals | [Tutor, HT], Wikipedia | - |
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21 | 08/05 | Introduction to model estimation: Prediction and Inference, Parametric vs Non-Parametric approach, Model Accuracy | [ISL, Ch. 2] | - |
| 22 | 08/05 | Assessing normality with Python | - | 22-Python-Normality.zip |
| 23 | 09/05 | Simple linear regression: Coefficient estimates, Accuracy of coefficient estimates, Model accuracy | [ISL, Ch. 3] | - |
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24 | 14/05 | Multiple linear regression (MLR): Coefficient estimates, Accuracy of coefficient estimates, Model accuracy, Selecting important variables | [ISL, Ch. 3] | - |
| 25 | 14/05 | MLR with Python | - | 25-Python-MLR.zip |
| 26 | 15/05 | Prediction and prediction intervals, qualitative input variables in MLR | [ISL, Ch. 3] | - |
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27 | 22/05 | Further topics in MLR: Residual plot, Outliers, Non-constant variance, Collinearity, Log transform | [ISL, Ch. 3] | - |
| 28 | 22/05 | Advanced MLR with Python | - | 28-Python-MLR-Misc-I.zip 28b-Python-MLR-Misc-II.zip |
| 29 | 23/05 | Introduction to classification, Logistic regression | [ISL, Ch. 4] | - |
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29b | 27/05 | Extra class: Information about project work and oral exam | - | - |
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30 | 29/05 | Logistic Regression with Python | - | 30-Python-Logistic-Regression.zip |
| 31 | 29/05 | Linear discriminant analysis (LDA) | [ISL, Ch. 4] | |
| 32 | 30/05 | Quadratic discriminant analysis (QDA) | [ISL, Ch. 4] | - |
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33 | 12/06 | LDA and QDA with Python | - | 33-Python-LDA-QDA.zip |
| 34 | 12/06 | Naive Bayes (NB) and K-Nearest Neighbors (KNN) | [ISL, Ch. 2, 4] | - |
| 35 | 13/06 | NB and KNN with Python | - | 35-Python-NB-KNN.zip |
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