Bachelor's Programmes - Catalogue 2021/2022 | Business Studies (Joint programme with The University of York) | SHEB505

SHEB505 Decision Making with Information Technology

Annotation:

Information Technology (IT) is in a constant state of evolutionary change. IT now enables the transformation of business supply chains into networks of collaborating business organisations adopting structures built around business processes exploiting core competencies. Before businesses can reap the benefits of such change, managers need to be able to exploit IT to make better operational, tactical and strategic decisions. This involves not only routine internal decisions, but also one-off unanticipated decisions and decisions involving collaborating organisations.

As a consequence, effective business managers need to remain current in terms of the IT available and to acquire the concepts and skills involved in analysing, designing and implementing the Management Information Systems (MIS) which are needed to provide the information used in the decision making process. They need to understand the nature of management decisions and the decision making process itself.

Without such knowledge managers will not be equipped to work alongside systems analysts and other IT professionals and play a full part in the process of MIS provision. Ultimately it is the role of managers to interpret and apply information in order to be more effective in their jobs, and thereby to be fully involved in the process of transforming their organisation to be capable of working in an IT enabled supply chain network.

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To the program courses Schedule VIRTUAL LEARNING
Business Studies (Joint programme with The University of York)

Lecturers:

Dr. Andrew Salisbury
Assoc. Prof. Polina Mihova, PhD

Course Description:

Competencies:

LO1 - Recognise the nature of operational, tactical and strategic decision making incorporating appropriate tools such as decision tables, databases and algorithms.

LO2 - Understand the stages of the information systems development life cycle.

LO3 - Apply methods of digital processing information into knowledge.

LO4 - Collect, store, analyse and interpret information in a simulated decision making exercise.

LO5 - Comprehend the importance of spreadsheets and create a fully functional MS Excel spreadsheet.

LO6 - Communicate the findings of a decision making exercise through working in teams.

LO7 - Recognize fundamentals of machine learning, and their significance in decision making processes and big data.

LO8 - Develop elementary algorithms (for beginners in Business studies) using tools such as pseudo codes and Python.


Prerequisites:


Types:
Full-time Programmes

Types of Courses:
Lecture

Language of teaching:
English

Topics:

  1. Introduction – Information systems in Decision Making Lab session 1 Introduction to spreadsheets and databases. Types of databases. Creating MS Excel sheets with rows and columns.
  2. Representing and manipulating information using appropriate tools Lab session 2 MS Excel mathematical functions: ABS, FACT, INT, LOG10, POWER, PRODUCT, RAND, RANDBETWEEN, ROUND, ROUNDDOWN, ROUNDUP, SQRT, SUM, TRUNC
  3. Artificial Intelligence (AI) and big data: a new era in data science Lab session 3 MS Excel statistical functions: AVERAGE, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA, MIN, MINA
  4. Processing information into knowledge ? Data Mining ? Machine learning and data analysis languages: introduction to Python ? Pseudo codes ----------------------------------------------------------------------------------------------- Lab session 4 MS Excel logic functions: AND, IF, OR – combine them with mathematical and statistical functions Python: basics of programming. Websites will be used from students for learning and practicing Python programming: W3Schools (https://www.w3schools.com/python) and Learn Python (https://www.learnpython.org). Note: These websites are recommended and will be used for this module; however students can use other functional websites of their choice with embedded testing and compiling tools.
  5. Programming with Python – PART 1 ? Distinguishing Python from other programming languages ? Capabilities and limitations Lab session 5 Python variables, variables’ names, multiple values and printing commands. Examples with different data types and numbers.
  6. Quiz
  7. Programming with Python – PART 2 ? Python for machine learning ? Statistical models for data analysis Lab session 7 Python basic calculations: arithmetic and logic operators. If...else command. Databases: arrays and lists
  8. Programming with Python – PART 3 ? Iterations and loops in programming languages ? Examples with pseudo codes and with Python Lab session 8 Iterations and loops in practice: what, how and why? Examples in Python.
  9. Programming with Python – PART 4 (continued as of PART 3) ? While loops ? For loops Lab session 9 Develop codes using While and For loops in practice: differences and similarities.
  10. Different types of problems require different approaches: is MS Excel or a programming language more appropriate? ? Standard and scientific calculations ? MS Excel and Python do have limitations ? Reasons for choosing the appropriate tools. ----------------------------------------------------------------------------------------------- Lab session 10 Students will be asked to create a database in MS Excel and perform functions and calculations they have learned so far.
  11. Artificial intelligence and machine learning in Business and Economics ? Computer science is evolving very fast ? The traffic generated on the Internet over the last decade is at the scale of Exabytes (EB) per month: big data analysis tools are used by businesses and enterprises ? Different scientific disciplines use computer science tools to analyse data ? Machine learning and AI are robust tools in Financial Global Markets and Econometric Models ----------------------------------------------------------------------------------------------- Lab session 11 Students will be asked to develop programming codes in Python for decision making. This should also reflect on their experience in Python from previous weeks.

Bibliography:

Course Book (primary): Business Driven Information Systems 8th Edition By Paige Baltzan and Amy Phillips, ISBN 9781265070403, Published: April 26, 2022.

Course Book (secondary): Paige Baltzan. (2018) M: Information Systems 4th Edition. Mcgraw hill.

Extra reading material:

● Ram, Sudha; Goes, Paulo. (Mar 2021) Management Information Systems. Business Source Complete.

● Andri Ikhwana, Sasi Dianti + (Jan 2022) + The Influence of Information Technology and SCM on Competitive Advantage to Improve MSMEs Performance + International Journal of Computer and Information System+ [ARTICLE] + Vol 3 + Issue 1 + https://ijcis.net/index.php/ijcis/article/download/54/55 + [Accessed 22/3/22]

● Dreyer, Sonja,Werth, Oliver, Olivotti, Daniel, Guhr, Nadine, Breitner, Michael H. Knowledge Management Systems for Smart Services A Synthesis of Design Principles. [2021]. e-Service Journal. Vol. 13 Issue 2, p27-67.

● Eduardo Luis Casarottoa, Guilherme Cunha Malafaiab, Marta Pagán Martínezc, and Erlaine Binottoa + (March 2021) + Big Data for Creating and Capturing Value in the Digitalized + Journal of Intelligence Studies in Business + [ARTICLE] + Vol 11 + Issue 1

● Emel Seyma KÜÇÜKASCI, Mustafa Gökçe BAYDOGAN, Z. Caner TASKIN. A linear programming approach to multiple instance learning. Turkish Journal of Electrical Engineering & Computer Sciences. (2021) vol 29, pp 2186 – 2201.

● (CASE STUDY) THE IMPACT OF INFORMATION TECHNOLOGY IN DECISION MAKING PROCESS OF COMPANIES IN KOSOVO. UTJECAJ INFORMACIJSKE TEHNOLOGIJE U PROCESU DONOŠENJA ODLUKA U PODUZEĆIMA NA KOSOVU. By: Neziraj, Emin Qerim; Shaqiri, Aferdita Berisha. Informatologia. (Jun2018), Vol. 51 Issue 1/2, p13-23. 11p.

● The Role of Information Technology Capability and Innovative Capability: An Empirical Analysis of Knowledge Management in Healthcare.Christie Hui-chuan Chen; Cates, Tommy. International Management Review. (2018), Vol. 14 Issue 1, p5-16. 12p.

● The Effect of Information Technology on IT-Facilitated Coordination, IT-Facilitated Autonomy, and Decision-Makings at the Individual Level. Jonghak; Applied Economics, (January 2017), v. 49, iss. 1-3, pp. 138-55.

● PMI (2016) A Guide to the Project Management Body of Knowledge (PMBOK® Guide)–Fifth Edition 5th Edition

● Towards an attention-based view of technology decisions. Palmié, Maximilian; Lingens, Bernhard; Gassmann, Oliver. R&D Management. (Sep2016), Vol. 46 Issue 4, p781-796. 16p.

● The impact of information and communication technology on decision making process in the big data era lukić, JELENA. Megatrend Review. (2014), Vol. 11 Issue 2, p221-233. 13p.