Наука о данных для бизнеса

Магистратура 2021/2022

Статус: Курс обязательный (Бизнес-аналитика и системы больших данных)

Направление: 38.04.05. Бизнес-информатика

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1-й курс, 1 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Кузнецова Юлия Александровна

Прогр. обучения: Бизнес-аналитика и системы больших данных

Язык: английский

Кредиты: 3

Контактные часы: 24

Full Syllabus

Abstract

The course is aimed at building a systematic view of the possibilities and limitations of machine learning and practical experience of using data analysis algorithms for solving business problems in various economic fields. The discipline is applied one and involves working with business data based on cases using low-code platforms to solve problems of descriptive, predictive, and prescriptive analytics.

Learning Objectives

Building the theoretical and methodological foundations of data-analytical thinking, understanding of the main methods and models of data analysis.
Obtaining practical skills in using data analysis algorithms, choosing the best methods and models for solving a wide range of problems.
Gaining the skills in working with basic Data Science tools for practical application.
Building the skills in working with data researchers, and project management skills in the field of data science.

Expected Learning Outcomes

Student chooses appropriate models and methods to solve business problems.
Student chooses tools for Data Science project.
Student sets objectives, organizes the process and evaluates the results of Data Science project.
Student understands the role of Data Science in a strategic development of a business.

Course Contents

Introduction to Data Science
Big Data Analytics
Data Science Solutions for Business. Basic models and methods
Data Science Tools
Data Science Project Management

Assessment Elements

Computer practice
Final examination
Online test

Interim Assessment

2021/2022 1st module
0.5 * Computer practice + 0.5 * Final examination

Bibliography

Recommended Core Bibliography

Kirill Dubovikov. (2019). Managing Data Science : Effective Strategies to Manage Data Science Projects and Build a Sustainable Team. Packt Publishing.
Kotu, V., & Deshpande, B. (2019). Data Science : Concepts and Practice (Vol. Second edition). Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1866160
Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. – " O'Reilly Media, Inc.", 2013.
Qurban A Memon, & Shakeel Ahmed Khoja. (2019). Data Science : Theory, Analysis and Applications. [N.p.]: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2198593
Sarangi, S., & Sharma, P. (2020). Big Data : A Beginner’s Introduction. Abingdon, Oxon: Routledge India. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2168187

Recommended Additional Bibliography

Berman, J. J. (2018). Principles and Practice of Big Data : Preparing, Sharing, and Analyzing Complex Information (Vol. Second Edition). London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1731816
Mahmood, Z. (2016). Data Science and Big Data Computing : Frameworks and Methodologies. Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1203573
Ogrean Claudia. (2018). Relevance of Big Data for Business and Management. Exploratory Insights (Part I). https://doi.org/10.2478/sbe-2018-0027
Ogrean Claudia. (2019). Relevance of Big Data for Business and Management. Exploratory Insights (Part II). https://doi.org/10.2478/sbe-2019-0013

Course Syllabus