Delivering a Business Analytics Course Focused on Data Mining for Both Technical and Non-Technical Students
Many colleges and universities have been designing business analytics courses and programs to meet the expanding job market demand for graduates possessing adequate knowledge and skills to apply business analytics to the real world. Since business analytics is multidisciplinary, with a wide variety...
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| Veröffentlicht in: | Journal of information systems education 2024-01, Vol.35 (1), p.86-98 |
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| creator | Zhang, Yulei Zhang, Mandy Albritton, M. |
| description | Many colleges and universities have been designing business analytics courses and programs to meet the expanding job market demand for graduates possessing adequate knowledge and skills to apply business analytics to the real world. Since business analytics is multidisciplinary, with a wide variety of perspectives regarding the preparation, analysis, understanding, and presentation of different types of business data, various academic institutions include their own "flavor" of business analytics offerings in their curricula. [...]we used these fundamental theories to help organize course topics and develop hands-on lab activities where students can practice applying what they are learning. ALT asserts that "active learning" occurs when learners go beyond passive participation (e.g., watching, listening, and taking notes; Felder & Brent, 2009), and become experientially involved in the learning process using two-way communication (e.g., writing, discussing, and engaging in problem-solving; Romanow et al., 2020) and higher-ordered thinking (e.g., analysis of data, synthesis of information, evaluation of alternatives and self-reflection; Bonwell & Eison, 1991). Information systems (IS) activities cascade across all other disciplines. Because of the bridging nature of IS in the business school, which aims to provide students with both technical skill backgrounds with business-specific knowledge, many of the business analytics courses and programs at US universities have been deployed by business IS departments, with complementary efforts made in the computer science and informatics domains as well. |
| doi_str_mv | 10.62273/MWCG1518 |
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Since business analytics is multidisciplinary, with a wide variety of perspectives regarding the preparation, analysis, understanding, and presentation of different types of business data, various academic institutions include their own "flavor" of business analytics offerings in their curricula. [...]we used these fundamental theories to help organize course topics and develop hands-on lab activities where students can practice applying what they are learning. ALT asserts that "active learning" occurs when learners go beyond passive participation (e.g., watching, listening, and taking notes; Felder & Brent, 2009), and become experientially involved in the learning process using two-way communication (e.g., writing, discussing, and engaging in problem-solving; Romanow et al., 2020) and higher-ordered thinking (e.g., analysis of data, synthesis of information, evaluation of alternatives and self-reflection; Bonwell & Eison, 1991). 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Since business analytics is multidisciplinary, with a wide variety of perspectives regarding the preparation, analysis, understanding, and presentation of different types of business data, various academic institutions include their own "flavor" of business analytics offerings in their curricula. [...]we used these fundamental theories to help organize course topics and develop hands-on lab activities where students can practice applying what they are learning. ALT asserts that "active learning" occurs when learners go beyond passive participation (e.g., watching, listening, and taking notes; Felder & Brent, 2009), and become experientially involved in the learning process using two-way communication (e.g., writing, discussing, and engaging in problem-solving; Romanow et al., 2020) and higher-ordered thinking (e.g., analysis of data, synthesis of information, evaluation of alternatives and self-reflection; Bonwell & Eison, 1991). 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| subjects | Active learning Algorithms Analytics Behavioral Objectives Big Data Business analytics Business operations Colleges & universities Computers Data mining Decision making Experiential learning Information systems Labor market Learner Engagement Learning Learning Processes Learning Theories Literature reviews Mathematical analysis Problem solving Skills Student Surveys Students Undergraduate Students |
| title | Delivering a Business Analytics Course Focused on Data Mining for Both Technical and Non-Technical Students |
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