2022/2023
Введение в анализ данных в финансах и бизнесе
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Статус:
Маго-лего
Кто читает:
Департамент финансов
Когда читается:
1 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Маилов Эдвард Рафаэлевич
Язык:
английский
Кредиты:
3
Контактные часы:
36
Course Syllabus
Abstract
The course is designed for students interested in data analysis problems. It provides theoretical foundations and practical skills related to data analysis, data mining, text mining, graph mining and ontology-based data analysis.
Learning Objectives
- The course is focused on practical aspects of data mining methods and their applications in finance and business.
Expected Learning Outcomes
- Students able to design and implement typical schemes of analysis
- Students are able to analyze financial data with the use of data mining methods
- Students are able to choose right methods of analysis to a given problem
- Students are able to use data mining in decision making problems
- Students interpret the results delivered by data mining methods
- Students know advantages and disadvantages various methods
- Students know how develop their theoretical knowledge and practical skills related to data analysis area
- Students know the classification of problems
- Students know theoretical foundations of contemporary methods and algorithms used in data analysis area
Course Contents
- Introduction do data analysis. Data preprocessing. Introduction to R language
- Complex data structures in R. Scripts and control statements. Data preprocessing and visualization. Linear regression. Logistic regression
- Neural network models
- Decision tree models. Naive Bayes classifier. Support vector machine model. Association rules
- Problem of dimensionality reduction. Multidimensional scaling. Correspondence analysis. Genetic algorithms
- Cluster analysis
- Introduction to text mining
- Sentiment analysis and ontology-based analysis of text documents
- Network analysis
Bibliography
Recommended Core Bibliography
- Venables, W. N., & Smith, D. M. (2015). An introduction to R. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.63A6F894
Recommended Additional Bibliography
- Max Bramer. (2000). Inducer: a rule induction workbench for data mining, in. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.ABA104A4