Objectives and competences
The course aims to acquaint students with electronic business information systems and provide e-business experts with the knowledge and skills to use data analytics in e-business models. The process of selecting e-business models and implementing these ones into the company. In part dedicated to e-commerce information systems, the subject focuses on models of e-commerce information systems and technologies that appear in e-commerce, as well as data collection methods, data analysis and data visualization. The part is intended for websites, online portals and social media to identify analytical tools suitable for specific activities. They will understand the appropriate ways to collect, analyze and visualize data from the web and social media and use the data to make decisions for their companies/organizations.
The course focuses on paradigms and models of online solutions and social media, technologies of online solutions and social media, and understanding data science in e-business.
Transferable/Key Skills and other attributes:
Use of information systems in different fields of e-business and internet solutions for business purposes.
Ability to use various online tools to analyze large amounts of data, ability to use databases effectively, understanding and ability to use mass data in e-business, in-depth understanding of data visualization and analytics in e-business, knowledge of the latest trends in data science in e-business, discovery patterns and trends with data analysis to create business strategies, selection of appropriate tools for e-business data analysis for key business decisions, communication, organizational and professional skills and orientation to problem-solving for successful operation in a global environment.
Content (Syllabus outline)
The course Data Science in E-business introduces students to the science of web analytics, addressing the use of data found in the digital space. The goal is to provide e-business professionals with the knowledge and skills to apply data analytics to real-world challenges while doing business via e-business models. Students will learn to identify the web analytic tool suitable for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data from the web; and utilize data in decision-making for their companies/organizations/institutions. Students will gain an understanding of data science in e-business; learn to evaluate and choose appropriate web analytics tools and techniques; understand frameworks to measure consumers' digital actions; earn familiarity with the unique measurement opportunities and challenges presented by Social Media; gain hands-on, working knowledge of a step-by-step approach to planning, collecting, analyzing, and reporting data; utilize tools to collect data using today's most important online techniques: performing bulk downloads, tapping APIs, and scraping webpages; and understand approaches to visualizing data effectively.
Learning and teaching methods
Lectures, case analysis, discussion
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students:
Knowledge base: has depth and systematic understanding of knowledge in e-business management and e-business solutions and can work with theoretical / research-based knowledge at the forefront of their academic discipline
Ethical issues: has the awareness and ability to manage the implications of ethical dilemmas and work pro-actively with others to formulate solutions
Disciplinary methodologies: comprehensively understands many methods/techniques applicable to data science in e-business.
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
Analysis: can critically analyze complex, incomplete (incomplete) and conflicting areas of knowledge and understandably explain the results of critical data analysis of e-business.
Synthesis: with critical awareness, can synthesize information that may be innovative, utilizing knowledge or processes from a data science perspective in e-business.
Evaluation: has a level of conceptual understanding using data science in e-business information solutions, which enables him/her to critically assess research, knowledge and methodological approaches and justify different (alternative) approaches.
Application: can demonstrate initiative and originality in problem solving. Can act autonomously in planning and implementing e-business solutions at a professional or equivalent level, making decisions in complex and unpredictable situations in the area of data science of e-business.
Key/Transferable skills
Group working: can work effectively with a group as a leader or member. Can clarify tasks and make appropriate use of the capacities of group members. Can negotiate and handle conflict with confidence
Learning resources: can use a full range of learning resources
Self-evaluation: is reflective on own and others' functioning to improve practice
Management of information: can competently undertake research tasks with minimum guidance
Autonomy: is an independent and self-critical learner, guiding the learning of others and managing his/her own requirements for continuing professional development.
Communications: can engage confidently in academic and professional communication with others, reporting on action clearly, autonomously and competently
Problem-solving: has independent learning ability required for continuing professional study, making professional use of others where appropriate
Practical skills:
Application of skills: can operate in complex and unpredictable and/or specialized contexts and has an overview of the issues governing good practice with the different toos.
Autonomy in skill use: is able to exercise initiative and personal responsibility in professional practice
Technical expertise: has the technical expertise, performs smoothly with precision and effectiveness; can adapt skills and design or develop new skills and/or procedures for new situations.
Readings
Temeljna študijska literatura (Compulsory textbooks):
Provost, F., Fawcett, T. (2013). Data Science for Business. Beijing, Campridge etc.: O'Reilly.
https://www.researchgate.net/publication/256438799_Data_Science_for_Business
Phillips, J. (2016). Ecommerce Analytics: Analyze and Improve the Impact of Your Digital Strategy. Pearson FT Press.
Chaffey, D. (2019). Digital Business and E-Commerce Management. Pearson
Dodatna študijska literatura (Additional textbooks):
Laudon, K., Traver, C. E-Commerce 2020–2021: Business, Technology and Society, Global Edition. Pearson.
Additional information on implementation and assessment Type (examination, oral, coursework, project):
Written exam
seminar work
practial work
