Information Management

2024/2025

Academic Year

ENG

Instruction in English

5

ECTS credits

Delivered at:

Department of Sociology

Course type:

Elective course

When:

4 year, 1, 2 module

Instructor

Serova, Elena

Course Syllabus

Abstract

The course focus is on management information systems (MIS) in the Innovation Digital Economy. The general course goal is studying systems-wide concepts relevant to using and managing information in order to make students acquire skills in management IT/IS Innovation for business value. This course contains the examples and analysis of the current practices of Enterprise Information Systems (EIS) development and implementation in Russian companies as well as abroad. The course explores MIS from two perspectives – theoretical and practical. At the theoretical level, participants will deal with the MIS discourse – a rich conversation of competing ideas, debate, and case-studies. At the practical level, participants will examine a systematic overview both about intelligent methods used throughout the data analysis process and about intelligent, complex machine learning algorithms used in modern data analysis.The course includes the IS concepts and classification, the evolution of Information Systems from departmental to enterprise-wide applications, business processes design, and enterprise systems implementation strategies. One of the main topics of the course is devoted to Global Information Systems and emerging computing environments and Web-based Information Systems.This course is intended to provide a study of what contemporary Enterprise Recourse Planning (ERP) systems are. The course also covers Customer Relationship Management (CRM), Supply Chain Management (SHM) and Financial Planning systems and deals with a range of Knowledge Management, Business Intelligence, and Artificial Intelligence issues. This is a business systems-oriented course in which students explore the deployment of enterprise systems to support critical business processes. To meet the demands of a constantly changing environment, organizations need a clear understanding of how their processes work. The course supports business process improvement by capturing a comprehensive view of the way companies do business and teaches students how to conduct business-processes using the powerful features and capabilities of modern software. It provides an overview of the tools and platforms known as a Business Process Management Suite. Student will learn about the practices and the technologies of EIS to solving business problems and continuously improving organizational performance.Data Culture level: 0.0.4 - Professional. Data Analysis

Learning Objectives

• The main goal is to provide participants with a broader understanding of Information Management, including its impact on business models, sources of competitive advantage, and organizational structure.

Expected Learning Outcomes

• Define information systems and describe various types of information systems
• Understand the importance of managing knowledge and understand the role of IT/IS innovation
• Identify the major enterprise internal and external information systems and relate them to managerial functions
• Understand business process management and how to enhance effectiveness
• Analyse the benefits and issues of integrating functional information systems
• Learn about Intelligent Data Analysis and its applications
• Gain a stronger grasp of AI to address and exploit its opportunities
• Learn how to install and use Python programming language to create machine learning algorithms and use them for decision making
• Learn from case studies and applications

Course Contents

• Course Introduction.
• Information and the information culture
• Information Technologies: the current state, role and the evolution tendency
• Information technologies and Organization management.
• Ethical and privacy challenges. Ethical considerations in the use of ICT. Societal impacts
• Information system, company management system and data architecture
• Concepts of creating, development and implementation of information systems
• Artificial Intelligence and systems in company management
• Review of the course

Assessment Elements

• A group research project
  The major academic purpose of the Group Project is to challenge students to apply theories, concepts, models and tools from the classes and from their previous academic background and to learn additionally. The Group Project is a research & business practice project where students act as a consulting team (no more than 6 persons) to analyze the main business processes, assess the current state of the business; and design the new BP NetWork. In the process of detailed work during 2 modules, the team sets the project goals and research areas, develops a strategy to reach the goal, and then implements it to achieve results and to produce sound recommendations. At the end of the course students should submit and present the final group project report electronically. Group reports should be done in the form of consulting reports.
• Individual assignment
  Exercises, case-studies, quizzes, panel discussion, and presentations
• Written examination
  1,5-hour exam. Exam is held as a written test based on all course issues and materials. Participants have to show their knowledge or ability in a particular subject, or to obtain a qualification.

Interim Assessment

• 2024/2025 2nd module
  0.2 * A group research project + 0.5 * Individual assignment + 0.3 * Written examination

Bibliography

Recommended Core Bibliography

• 9781491962992 - Bengfort, Benjamin; Bilbro, Rebecca; Ojeda, Tony - Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning - 2018 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1827695 - nlebk - 1827695
• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705

Recommended Additional Bibliography

• Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
• Yang, Y. (2005). Information Theory, Inference, and Learning Algorithms. David J. C. MacKay. Journal of the American Statistical Association, 1461. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.jnlasa.v100y2005p1461.1462

Presentation

• Course Introduction

Authors

• SEROVA ELENA GENNADEVNA