Objectives and competences
Students are able to distinguish and analyse the use of artificial intelligence in e-commerce in web-based IT solutions. They are able to apply appropriate artificial intelligence tools and platforms in web-based information solutions.
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
- guided classes in the computer room ;
- case analysis.
Intended learning outcomes - knowledge and understanding
Students in this course:
1. Review the concepts of e-commerce and e-commerce technologies
2. systematically build on their knowledge of e-business models in the organisation-individual and organisation-organisation domains in the context of the integration of artificial intelligence (PILO 2a, PILO 3b)
3. be trained to apply theoretical knowledge in the integration of artificial intelligence in websites (PILO 2a, PILO 3a)
4. be trained to apply theoretical knowledge in the integration of artificial intelligence in web-based information solutions (PILO 2a, PILO 3a)
5. be able to select AI tools and platforms for different e-business purposes (PILO 2a, PILO 3a)
6. gain practical experience in the use of AI tools and platforms in e-commerce solutions (PILO 3b, PILO 3c)
7. be aware of the ethical and sustainability aspects in the field of artificial intelligence in e-commerce (PILO 4a, PILO 4c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme
Intended learning outcomes - transferable/key skills and other attributes
Students in this course:
1. Review the concepts of e-commerce and e-commerce technologies
2. systematically build on their knowledge of e-business models in the organisation-individual and organisation-organisation domains in the context of the integration of artificial intelligence (PILO 2a, PILO 3b)
3. be trained to apply theoretical knowledge in the integration of artificial intelligence in websites (PILO 2a, PILO 3a)
4. be trained to apply theoretical knowledge in the integration of artificial intelligence in web-based information solutions (PILO 2a, PILO 3a)
5. be able to select AI tools and platforms for different e-business purposes (PILO 2a, PILO 3a)
6. gain practical experience in the use of AI tools and platforms in e-commerce solutions (PILO 3b, PILO 3c)
7. be aware of the ethical and sustainability aspects in the field of artificial intelligence in e-commerce (PILO 4a, PILO 4c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme
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 Final exam (40%)
Exam in the computer lab (40%)
Homework/project (20%)
Final exam - written exam with at least one question from each section within the course.
Exam in the computer lab - practical exam with at least one task from each set of computer tools/solutions included in the course.
Homework/project - ongoing assignment with at least one assignment from each section of the course.
