Modern Data Analysis 2021 2022

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Course: Modern Data Analysis (2021–2022)

Lecturer: Dmitry Ignatov


All the material are available via our telegram channel.

Final mark formula: FM = 0.8 Homeworks + 0.2 Exam (under voting)


  • Homework 1: Classification

Announced: 11.10.2021

Soft deadline: changed to 08.11.2021 due to pandemic regulations (before it was 03.11.2021)

Hard deadline: 10.11.2021


Lecture 1

Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?

Practice: demonstration with Orange.

Lecture 2

Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).

Practice: demonstration with Orange.

Lecture 3

Classification (continued). Quality metrics. ROC curves.

Practice: demonstration with Orange.

Seminar 1


Practice: scikit-learn.

Lecture 4

Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan

Practice: DBScan Demo.