Магистратура
2021/2022
Бизнес-аналитика
Статус:
Курс обязательный (Международный менеджмент / Master in International Management)
Направление:
38.04.02. Менеджмент
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 1 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Днепровская Наталья Витальевна
Прогр. обучения:
Международный менеджмент
Язык:
английский
Кредиты:
5
Контактные часы:
40
Course Syllabus
Abstract
The course addresses a variety of theoretical and practical aspects of Business Analytics (BA) applying to problem solving. The case applications, methods and instruments are considered to discover advantages of BA for the business performance improve. Analytics is something every business needs to stay competitive in today’s data-filled environment. The course focuses on Data Analytics as a popular approach to get insights from digital world. The course assignments are designed to teach students to use open data resources, Data Studio, Power BI, and other cloud services to extract, manipulate, analyze and visualize data with the end goal of making better, data-informed decisions. The course is taught using the combination of lectures, data processing exercises, case analysis, discussions, quizzes and individual assignment for homework. Lectures discover the main theoretical aspects for BA and supplemented by the additional reading sources. Short quizzes as the assessment activities will be given after every theoretical part to make sure of the material clearance. Exercises are designed to gain students’ practice methods and tools of BA using different types of sources (datasets, online data, open data), of data (structured, semi structured, textual data), to explore the ability of PC desktop and SaaS technologies. Case based on actual data of business problem solving is included into the course. Home assignment is the individual activity to summarize theory and practice of the course. Home assignment will be given and explained at the first class.
Learning Objectives
- The course offers a comprehensive introductory approach to business analytics that includes an emphasis on data analytics, applications BA for solving business problem.
- Understanding how business analytics can be used in business performance and in developing industrial processes.
- Describing and using a wide variety of BA methods in a business or an industry domain.
- Carrying out the selection and application of IT to support BA process (collaboration, digital technology).
- Building skills in applying business analytics to real-world business and industrial problems.
Expected Learning Outcomes
- Ability to apply data analytics methods to solve business tasks
- Ability to apply data analytics methods to solve business tasks.
- Ability to create knowledge from data and information resources
- Ability to interpret and effectively communicate the message to stakeholders who need to act on the findings based on BA.
- Ability to use descriptive analytics in business tasks.
- Ability to use variety data sources and tools in BA process.
- Building skills in applying business analytics to real-world business and industrial problems.
- Carrying out the selection and application of IT to support BA process (collaboration, digital technology).
- Creation interactive dashboards and reports using IT
- Data acquiring include finding, extraction, evaluation, preparing and upgrading.
- Data processing include descriptive analysis and visualization.
- Describing and using a wide variety of BA methods in a business or an industry domain
- Principles of visualization of data and preparing presentation
- Understanding how business analytics can be used in business performance and in developing industrial processes.
Course Contents
- Business Analytics Introduction
- Data Platforms
- The concept of analytics
- Information Sources for BA
- Data analytics approaches
- Communicating Data Analysis Findings
- Datamining and machine learning
- Knowledge creation methods
- Business analytics ecosystem
Bibliography
Recommended Core Bibliography
- Business analytics : data analysis and decision making, Albright, S. C., 2020
- Business analytics, Evans, J. R., 2014
- Handbook of organizational learning and knowledge management, , 2011
- Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612
- Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data Mining for Business Analytics : Concepts, Techniques and Applications in Python. Newark: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2273611
- The definitive guide to DAX : business intelligence for Microsoft Power BI, SQL server analysis services, and Excel, Russo, M., 2020