Магистратура
2025/2026
Аналитика и майнинг данных
Статус:
Курс по выбору (Магистр аналитики бизнеса)
Где читается:
Факультет экономических наук
Когда читается:
2-й курс, 3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сохраби Маджид
Язык:
английский
Кредиты:
3
Контактные часы:
16
Course Syllabus
Abstract
The course provides a comprehensive foundation in data processing, visualization, and fundamental analytical techniques. Covers essential methods for supervised and unsupervised learning, including data pre-processing, dimensionality reduction, and prediction models. Students will gain hands-on experience with data mining techniques and explore key concepts in machine learning, from classical approaches to an introduction to deep neural networks. The course also emphasizes practical applications, equipping students with the skills needed to analyze data effectively and extract meaningful insights across various domains. The students will gain different skills:
use Python in data analysis applications;filter/sort/process data/create new variables.;calculate descriptive statistics and interpret the results; convert feature values to z-scores.; handle missing values and outliers; implement exploratory data analysis. apply parametric statistical tests to test hypotheses; mplement deep neural networks for prediction tasks. implement machine learning models for prediction. Students will visualize data using the charts: line, scatter, heat map, box plot, and others.
Learning Objectives
- The students will get familiar with Data Analysis and Mining techniques as well as basic concepts in Machine Learning. How to work with tabular data, preprocessing, cleaning, and exploratory data analysis. Also, the students will learn how to implement a simple machine-learning model for prediction task.
Expected Learning Outcomes
- - Students get familiar with Overview of Data Analysis and Mining, Importance and Applications in Industry, Types of Data: Structured vs. Unstructured, Overview of the Data Mining Process
