Магистратура 2020/2021

Методы практического машинного обучения для работы с данными

Статус: Курс обязательный (Менеджмент и аналитика для бизнеса)

Направление: 38.04.02. Менеджмент

Кто читает: Департамент менеджмента

Где читается: Санкт-Петербургская школа экономики и менеджмента

Когда читается: 1-й курс, 3 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Паклина София Николаевна

Прогр. обучения: Менеджмент и аналитика для бизнеса

Язык: английский

Кредиты: 6

Контактные часы: 24

Course Syllabus

Abstract

During this practically oriented data science module students will learn how machine learning uses computers to run predictive models. The main principal is to explore existing data to build new knowledge, forecast future behaviour, anticipate outcomes and trends. Explore theory and practice, and work with statistical software (R and RStudio) to solve advanced data science problems.

Learning Objectives

• Choose methods adequately corresponding to the objectives of a research project
• Collect, store, process and analyze data automatically with the use of scripting languages according to high standards
• Conduct empirical research in economics, management and marketing using modern analytic software tools
• Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
• Solve economic and managerial problems using best practices of data analysis using modern computational tools
• Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of management
• Can identify the data needed for addressing the management and business objectives; to collect data and process it

Expected Learning Outcomes

• Choose methods adequately corresponding to the objectives of a research project
• Collect, store, process and analyze data automatically with the use of scripting languages according to high standards
• Conduct empirical research in management and marketing using modern analytic software tools
• Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
• Solve economic and managerial problems using best practices of data analysis using modern computational tools
• Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of management

Course Contents

• Introduction to Data Science
• Data Sources and Data Structures
• Data Collection and Data Processing
• Data Collection and Data Processing
• Machine Learning Algorithms
• Practical Use of ML Algorithms

Assessment Elements

• Test
• Seminar coding tasks
• Exam
  The exam will be in the format of an online project that should be submitted during 10 days before the session. It will include data collection/import, preparation and analysis in the R environment. Students are required to prepare and send a text report of the solution and working R script.

Interim Assessment

• Interim assessment (3 module)
  0.4 * Exam + 0.3 * Seminar coding tasks + 0.3 * Test

Bibliography

Recommended Core Bibliography

• Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

Recommended Additional Bibliography

• Cirillo, A. (2017). R Data Mining. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1643003