IS-350 Business Analytics

GENERAL INFORMATION:

Course title: IS 350 Business Analytics
Campus: National	Initiator: Jean-Pierre Lukusa and Edper Castro	Date Initiated: June 1st 2021
Course description: Fundamental tools and concepts needed to understand the emerging role of business analytics in organizations will be covered. The course focuses on benefits of employing analytics and a structured approach to problem-solving in management situations with an emphasis on extensive use of data, methods, and fact-based management tools to support and improve decision making. The student will be able to use data and models to explain the performance of a business and how it can be improved.

COURSE HOURS/CREDITS:

	Hours per Week		No. of Weeks		Total Hours		Semester Credits
Lecture	3	x	16	x	48	=	3
Laboratory		x		x		=
Workshop		x		x		=
			Total Semester Credits				3

PURPOSE OF COURSE:

[X] Degree requirement

[ ] Degree elective

[ ] Certificate

[ ] Other

PREREQUISITES:

CA100 Computer Literacy
MS150 Statistics

PSLOs OF OTHER PROGRAMS THIS COURSE MEETS:

PSLO#	Program
N/A

1) INSTITUTIONAL STUDENT LEARNING OUTCOMES (Check all that apply)

[ ]	1. Effective oral communication: capacity to deliver prepared, purposeful presentations designed to increase knowledge, to foster understanding, or to promote change in the listeners’ attitudes, values, beliefs, or behaviors.
[ ]	2. Effective written communication: development and expression of ideas in writing through work in many genres and styles, utilizing different writing technologies, and mixing texts, data, and images through iterative experiences across the curriculum.
[X]	3. Critical thinking: a habit of mind characterized by the comprehensive exploration of issues, ideas, artifacts, and events before accepting or formulating an opinion or conclusion.
[ X ]	4. Problem solving: capacity to design, evaluate, and implement a strategy to answer an open-ended question or achieve a desired goal.
[ ]	5. Intercultural knowledge and competence: a set of cognitive, affective, and behavioral skills and characteristics that support effective and appropriate interaction in a variety of cultural contexts.
[X]	6. Information literacy: the ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively and responsibly use and share that information for the problem at hand.
[X]	7. Foundations and skills for life-long learning : purposeful learning activity, undertaken on an ongoing basis with the aim of improving knowledge, skills, and competence.
[ X ]	8. Quantitative Reasoning: ability to reason and solve quantitative problems from a wide array of authentic contexts and everyday life situations; comprehends and can create sophisticated arguments supported by quantitative evidence and can clearly communicate those arguments in a variety of formats.

2) PROGRAM STUDENT LEARNING OUTCOMES (PSLOs): The student will be able to:

Apply skills concepts and techniques in main functional areas of business and accounting;
Interpret and use quantitative techniques in solving business problems and decision-making using technological tools;
Develop and apply effective intercultural oral and written communication skills appropriate for business; and
Recognize and assess basic legal, environmental, and ethical challenges confronting businesses in general.

3) COURSE STUDENT LEARNING OUTCOMES (CSLOs) (General): The student will be able to:

Explain the principles of data analysis and good spreadsheet design following current professional and/or industry standards;
Manipulate and format dataset using different formulae and functions in spreadsheets;
Perform exploratory and confirmatory data analysis by applying formulae and statistical techniques on spreadsheet primary and/or secondary data; and
Summarize and interpret results of data analysis in spreadsheets.

4. COURSE STUDENT LEARNING OUTCOMES (CSLOs) (Specific): The student will be able to:

5) COURSE CONTENT:

Introduction to Business Analytics
Describing the distribution of variables
Finding relationship amongst variables
Business Intelligence and tools for data analysis
Probability and Probability distributions
Decision making under uncertainty

6) METHOD(S) OF INSTRUCTION:

[X] Lecture [ ] Cooperative learning groups

[ ] Laboratory [ X ] In-class exercises

[ ] Audio visual [ X ] Demonstrations

[ X ] Other Tutorial and Learning Management Systems

7) REQUIRED TEXT(S) AND COURSE MATERIALS:

Albright, S., & Winston, W. Business Analytics. 7th ed., Cengage Learning, 2019 (or most recent edition).

8) REFERENCE MATERIALS:

Parsons, J. J., et al. New Perspectives Microsoft Office 365 & Excel 2016: Comprehensive. 1st ed., (2016) (or most recent edition).

9) INSTRUCTIONAL COSTS: None.

10) EVALUATION:
Summative evaluation is accomplished by having the student complete midterm and final exams.

The student must achieve a grade of “C” or higher to pass the course.

11) CREDIT BY EXAMINATION:
None.

IS 350 Business Analytics	Endorsed by CC: 07/28/22
	Approved by VPIA: 07/29/22

College of Micronesia - FSM

COMFSM Menu

IS-350 Business Analytics

Hours per Week

No. of Weeks

Total Hours

Semester Credits

CSLO (General) 1: Explain the principles of data analysis and good spreadsheet design following current professional and/or industry standards.
Student Learning Outcome (specific)	ISLO	PSLO	Assessment Strategies
1.1 Describe the role of Business Intelligence in organizational decision making.	4, 6, 7	1	The student will complete a quiz graded with a rubric focused on describing the role of Business Intelligence in organizational decision making.
1.2. Describe principles used in spreadsheet data analysis and explain their applications in business analytics.	4, 6, 7	1	The student will complete a class- based activity graded with a rubric focused on describing principles used in spreadsheet data analysis and explain their applications in business analytics.
1.3 Distinguish between graphical, algebraic, and spreadsheet design models.	4, 6, 7	1	The student will complete a class based activity, graded with a rubric, focused on distinguishing between graphical, algebraic, and spreadsheet design models.
1.4 Distinguish between quantitative and qualitative data concepts and their implications in spreadsheet data processing.	4, 6, 7	1	The student will complete a quiz, graded with a rubric, focused on distinguishing between quantitative and qualitative data concepts and their implications in spreadsheet data processing.
CSLO (General) 2: Manipulate and format dataset using different formulae and functions in spreadsheets.
Student Learning Outcomes (specific)	ISLO	PSLO	Assessment Strategies
2.1 Prepare and present graphical, textual, and tabular data summaries.	3, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric, focused on preparing and presenting graphical, textual, and tabular data summaries.
2.2 Demonstrate good use of the four steps in data analysis using a spreadsheet application.	3, 4, 7, 8	2	The student will complete a class- based activity, graded with a rubric, focused on demonstrating good use of the four steps in data analysis using a spreadsheet application.
2.3 Compute measures of dispersion, central tendency, and shape on numerical and categorical data sets using built-in spreadsheet functions.	3, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric, focused on computing measures of dispersion, central tendency, and shape on numerical and categorical data sets using built-in spreadsheet functions.
2.4 Compute cumulative probability distributions of a single random variable.	3, 4, 7, 8	2	The student will complete a class- based activity, graded with a rubric, focused on computing cumulative probability distribution of a single random variable.
2.5 Apply random functions to generate market simulations using built-in statistical functions.	3, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric, focused on applying random functions to generate market simulations using built-in statistical functions.
CSLO (General) 3: Perform exploratory and confirmatory data analysis by applying formulae and statistical techniques on spreadsheet primary and/or secondary data.
Student Learning Outcomes (specific)	ISLO	PSLO	Assessment Strategies
3.1 Examine exploratory and confirmatory data analysis.	3, 4, 7, 8	2	The student will complete a class- based activity, graded with a rubric, focused on examining exploratory and confirmatory data analysis.
3.2 Examine relationships amongst categorical variables using crosstabs and contingency tables.	3, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric. focused on examining the relationships amongst categorical variables using crosstabs and contingency tables.
3.3 Experiment on stacked and unstacked data formats.	3, 4, 7, 8	2	The student will complete a class based activity, graded with a rubric, focused on experimenting on stacked and unstacked data formats.
3.4 Demonstrate relationships amongst categorical variables and a numerical variable.	3, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric, focused on demonstrating relationships amongst categorical variables and a numerical variable.
3.5 Demonstrate relationships amongst numerical variables using scatterplots, pivot-charts/pivot-tables, correlation and covariance.	3*, 4, 7, 8	2	The student will complete a practical assignment, graded with a rubric, focused on demonstrating relationship amongst numerical variables using scatterplots, pivot-charts/pivot-table, correlation, and covariance.
3.6 Experiment on probability concepts of risk, chance, and certainty.	3, 4, 7, 8	2	The student will complete a class based activity, graded with a rubric, focused on experimenting on probability concepts of risk, chance, and certainty.
CSLO (General) 4: Summarize and interpret results of data analysis in spreadsheets.
Student Learning Outcomes (specific)	ISLO	PSLO	Assessment Strategies
4.1 Distinguish between key elements in decision making under uncertainty.	3, 4, 7, 8	2	The student will complete a quiz, graded with a rubric, focused on distinguishing between key elements in decision making under uncertainty.
4.2 Explain sensitivity analysis using payoff tables (i.e. maximin and maximax criterion) and spreadsheet expected monetary value function.	3, 4, 7, 8	2	The student will complete a quiz, graded with a rubric, focused on explaining sensitivity analysis using payoff tables and spreadsheet expected monetary value function.
4.3 Describe and interpret decision trees, decision trees and risk profiles as tool(s) of risk aversion.	3, 4, 7, 8	2	The student will complete a quiz, graded with a rubric, focused on describing and interpreting decision trees, and risk profiles as tool(s) of risk aversion.