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Бакалаврская программа «Социология и социальная информатика»


Information Management

Учебный год
Обучение ведется на английском языке
Курс по выбору
Когда читается:
4-й курс, 1, 2 модуль


Course Syllabus


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, learn data mining and machine learning with Python. 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: software-as-a-service (SaaS), service-oriented architecture (SOA) 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 and Business Intelligence Systems 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

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

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 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. The main key points addressed in the course.
Assessment Elements

Assessment Elements

  • non-blocking Individual assignment
    Exercises, case-studies, quizzes, panel discussion, and presentations
  • non-blocking 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.
  • non-blocking A group research project
    Includs the production of a report (15%) and group presentation (15%) - 30%
  • non-blocking Python online course
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    The final assessment is composed as follows: written examination (1,5-hour exam) - 30%; coursework - 70% (Individual assignment - 20%; A group research project including the production of a report (15%) and group presentation (15%) - 30%; Python online course – 20%)


Recommended Core Bibliography

  • Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning. Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=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