UNIVERSITAS INDONESIA
FACULTY OF ECONOMICS AND BUSINESS
DEPARTMENT OF MANAGEMENT
MASTER OF MANAGEMENT PROGRAM
SYLLABUS
BUSINESS ANALYTICS
ECMM801081
TERM 1 (ODD) SEMESTER
2023/2024
Instructors
No.
1
Arviansyah
2
Buddi Wibowo
3
Imam Salehudin
4
Jonathan Marpaung
Subject Code
Subject Title
Credit Value
Year/Semester
Day/Hour
Subject Type
Pre-requisite/
Co-requisite/
Exclusion
Role and
Purposes
Name
E-mail
arviansyah@ui.ac.id
buddi.wibowo@ui.ac.id
imams@ui.ac.id
jonathan.nahum@ui.ac.id
ECMM801081
Business Analytics (Analitika Bisnis)
3 Credits
2/3 or 4
Refer to SIAK NG
Compulsory
Research and Statistical Methods in Business
Roles:
Businesses can now collect a tremendous amount of (internal and external) data
with advanced technology. However, without analytics, this data offers little or
even no value for businesses. Business analytics refers to how organizations can
use data to gain insights and make better decisions. Business analytics is applied
in operations, marketing, finance, strategic planning, and other management
functions. By analyzing the data, businesses can make predictions about
individuals or markets’ behavior, diagnose systems or situations, or prescribe
actions for people or processes.
This course will focus on understanding the fundamental concepts of analytics
and analytical methods used to transform data into insights. Students will learn
by analyzing case studies on organizations that successfully deployed these
techniques. Furthermore, students will also learn how to communicate,
effectively use, and interpret analytical results to solve business problems and
make better business decisions. This course emphasizes applications, concepts,
and interpretation of results rather than theory or mathematical formulation.
Students use a computer software package for data analysis.
Purpose:
After completing the course, participants are expected to be able to:
• Understand the process of business analytics to solve business problems.
• Select, understand, and apply appropriate analytical methods to analyze data
across major industries and business functions.
• Implement these methods using analytical software and interpret the output
(e.g., graphs, tables, mathematical models) and make recommendations
and/or decisions based on the insights.
Subject Learning
Outcomes
Program Objectives:
1. MM-FEBUI graduates should demonstrate integrity, ethical behavior, and
respect for diversity.
2. MM-FEBUI graduates should demonstrate concerns towards the society.
3. MM-FEBUI graduates should demonstrate effective leadership qualities.
4. MM-FEBUI graduates should have effective communication skills within
the global setting.
5. MM-FEBUI graduates should be able to conduct applied business research.
6. MM-FEBUI graduates should exhibit entrepreneurial spirit.
7. MM-FEBUI should demonstrate creativity and innovative thinking.
8. MM-FEBUI graduates should be able to formulate business models using
contemporary approaches.
Learning Goal (LG) and Learning Objective (LO):
1. LG: Critical Thinking - Students are able to demonstrate that they are
critical thinkers - ASM
a. LO: Students are able to argue and draw a conclusion on an issue based
on supportive evidence in business cases (LO1)
1) Trait: Deliver Key Ideas/Points (T1)
2) Trait: Comparison, Evaluation, and Analysis (T2)
3) Trait: Demonstrate ability to justify an argument with supporting
evidence/relevant references (T3)
4) Trait: Conclusion and generalization (T4)
2. LG: Research - Students are able to demonstrate knowledge in management
research methods
a. LO: Students are able to apply research methods in management (LO2)
1) Able to apply relevant research design to business research
problem (T1) - ASM
2) Able to analyze data for solving business research problems (T2) TLA
Week#
Subject Synopsis/
Indicative Syllabus
1
2.
3.
4.
5.
Topics
Reading Materials and
Activities
Overview of Business Analytics
• Introduction to business analytics
• Examples of practical use of business
analytics (analytics in various sectors of
businesses –e.g., retail, sports, financial
services, hospitality)
Data Issues & Preprocessing
• Data sources and data types
• Importance of data quality
• Data cleaning (e.g., dealing with missing
or incomplete data)
• Data integration
Descriptive Analytics I
• What is descriptive analytics?
• Nature of data, statistical modeling, and
visualization
• Describing and summarizing data
• Dimension reduction
SPB1, SPB2JE1
CCF1SDT1AO4
Descriptive Analytics II
• Business intelligence and data
warehousing
• Descriptive Data Mining
Clustering
• What is cluster analysis?
• Types of data and clustering methods
Predictive Analytics I
• What are classification and prediction?
(classifying binary outcomes, forecasting
numeric value, forecasting time series
data)
• Classification and prediction methods
• Predictive modeling process
Data mining process, methods, and
algorithms
Predictive Analytics II
• How to quantify the errors from making
predictions (performance measures)
• Model selection (e.g., how to account for
such errors when making economic
trade-offs)
• How in-sample and out-of-sample
predictions can increase the quality of
predictions
Social network and text analytics
• Concepts and definition
• Text, web, and social media analytics
• Information retrieval
• Text mining process & approaches
SPB15 CCF4 SDT3 AO7,
AO8
Case analysis*
SPB3, SPB4JE3
CCF3SDT2AO5
Lab descriptive analytics
Lab clustering
SPB5, SPB13SDT4
Lab predictive analytics*
SPB19, SPB20SDT5
Lab text mining
6.
7.
Predictive Analytics III
• k-Nearest Neighbors (k-NN)
• Naive Bayes
Predictive Analytics IV
• Introduction to time series analysis
and forecasting techniques
SPB7, SPB9, SPB10,
SPB12
JE10
CCF9
Case analysis
Package: KKNN
SPB16, SPB17, SPB18
JE9, JE12
CCF8, CCF11
RSHH5, RSHH13
AO9
Lab predictive analytics*
Guest lecture (tentative)
MIDTERM ASSIGNMENT
8.
9.
10.
11
12
Prescriptive Analytics I
• What is prescriptive analytics?
• Linear optimization and simulation
• Linear programming models: graphical
and computer methods
Prescriptive Analytics II
• Linear programming applications
• Large scale experimentation: A/B
Testing
• Practical examples of companies
nowadays using A/B testing
Prescriptive Analytics III
• Integer optimization
• Nonlinear optimization
RSHH8
JE13
CCF12
SDT6
Prescriptive Analytics IV
• Decision analysis
• Simulation considerations
Simulation concept, modeling, and
practical example
Big data concepts and tools
Storytelling with data
RSHH3, RSHH13
JE16
SDT6
CCF15
Lab simulation
SDT7
13
Future trends, ethics, customer privacy, and
data security
14
Team projects
RSHH9
JE14
CCF12
SDT6
Lab optimization (linear)
RSHH10
JE15
CCF13, CCF14
SDT6
Case analysis
SDT8
Case analysis
Guest Lecturer (tentative)
Presentation
Course recap & review
FINAL ASSIGNMENT
Teaching/Learning
Methodology
The instructor(s) serves as a facilitator in the learning process. Specifically, the
instructor(s) are responsible for:
1. Creating learning opportunities for the students
2. Assigning readings and challenging assignments
3. Assessing students’ work and stimulating participation and learning
Recommended software/applications:
•
•
•
•
General/multipurpose: Microsoft Excel (plus add-ins/Analytic Solver), IBM SPSS,
R, JMP
Descriptive Analytics: Tableau, Power BI, IBM Cognos, Looker Google
Cloud
Predictive Analytics: Orange, KNIME, IBM SPSS Modeler
Prescriptive Analytics: Excel QM, QM for Windows
Furthermore, this course uses the following teaching-learning method:
1. Participant Centered Learning (PCL): participants should actively
engage in the material being taught by sharing their ideas or opinions in the
class discussion.
2. Participants will present a case analysis (e.g., HBR cases) in groups. The
lecturer one week would give the case before class. Each group should
submit a case analysis in PowerPoint format before class. The assigned
group would present the material in class, and the other groups must actively
participate in the discussion session. A case with * means tentative.
3. Students are also required to complete passive learning activities such
as going over definitions, rules, and concepts discussed in the textbook
ahead of time so that they can have enough time during the week to work
on hands-on exercises, put the concepts from the textbook into practice, and
learn how to analyze and solve problems.
4. For the final project, four students (one team) must submit a proposal using
presentation slides. The slides outline a plan to apply analytical methods to
a problem you identify using some concepts and tools discussed in the
course. The proposal should propose a project that combines descriptive,
predictive, and prescriptive analytics. The proposal should include a
description of (1) the problem, (2) the data that you have or plan to collect
to solve the problem, (3) which analytic techniques you plan to use, and (4)
the impact or overall goal of the project (if you could build a perfect model,
what would it be able to do?). The submission for the final project proposal
will consist of presentation slides and a video. The final project submission
includes a written report (ten pages maximum, not including appendices) in
a PowerPoint or a PDF format. The report describes your analysis, as well
as a 15-minute video presentation of your project.
5. Six labs + two homework (a lab with * means switchable)
All homework assignments are individual work assignments. You can
discuss class exercise problems with your fellow students. However, the
work you submit must be your own. You must acknowledge in your
submission any help received on your assignments. That is, you must
include a comment in your submission that clearly states the names of the
students, books, or online references from which you received assistance.
Participation:
Individually, each student should be an active participant in the learning
process:
1. Reading assigned chapters and/or other materials provided.
2. Asking questions following the topic.
3. Preparing to answer questions.
4. Discussing issues related to the topic.
Attendance:
Minimum 80% of Total Lecture:
1. Only a maximum of 3 (three) times of absence without explanation are
allowed.
2. Students who come 15 minutes after the class begins are not considered
present.
Assessment
Method in
Alignment with
Intended Learning
Outcomes
Details of learning
methods
Student Study
Effort Expected
Reading List and
References
Description
Assessment
Percentage (%)
Participation/PCL
10%
Case analysis (first half)
10%
Lab homework/quiz (first half)
5%
Proposal for the final project (first half)
10%
Individual midterm assignment (first half)
20%
Lab homework/quiz (second half)
10%
Final project (group)
15%
20%
Individual final assignment
100 %
Total
The specific learning methods used in this subject are:
1. Lectures
2. Class discussion
3. Group Presentation
4. Labs (hands-on, practical exercises)
5. Midterm Exam
6. Final Exam
Class Contacts:
Lecturing (12 sessions each 1.5 hours)
19.5 Hours
Presentation and Discussion (12 sessions each 1 hour, 1
15.5 Hours
session equal to 2.5 hours)
Other student study effort:
Preparation for projects/assignments/quizzes
28 Hours
Required readings:
• Camm, J. D., Cochran, J. J., Fry, M. J., Ohlmann, J. W., Anderson, D. R.,
Sweeney, D. J., & Williams, T. A. (2019). Business analytics. (Third
edition). Cengage. [CCF]
• Evans, J. R. (2017). Business analytics: Methods, models, and
decisions (Second edition, global edition). Pearson. [JE]
• Render, B., Stair, R. M., Hanna, M. E., & Hale, T. S. (2018). Quantitative
analysis for management (Thirteenth edition, global edition). Pearson.
[RSHH]
• Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence,
analytics, and data science: A managerial perspective (Fourth edition).
Pearson. [SDT]
•
Shmueli, G., Patel, N. R., & Bruce, P. C. (2016). Data mining for business
intelligence: Concepts, techniques, and applications in Microsoft Office
Excel with XLMiner. Wiley. [SPB]
Recommended readings:
• Ohri, A. (2013). R for business analytics. Springer New York.
https://doi.org/10.1007/978-1-4614-4343-8 [AO]
• Pochiraju, B., & Seshadri, S. (Eds.). (2019). Essentials of business
analytics: An introduction to the methodology and its applications (Vol.
264). Springer International Publishing. https://doi.org/10.1007/978-3319-68837-4
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What you
need to know about data mining and data-analytic thinking. O’Reilly.
Plagiarism
Articles:
• Davenport, T. H. (2006). Competing on analytics. Harvard Business
Review, 84(1), 98.
• LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N.
(2011). Big data, analytics and the path from insights to value. MIT Sloan
Management Review, 52(2), 21-32.
• Zhang, S., Lee, D. D., Singh, P. V., & Srinivasan, K. (2017). How much is
an image worth? Airbnb property demand estimation leveraging large
scale image analytics. Airbnb Property Demand Estimation Leveraging
Large Scale Image Analytics (May 25, 2017).
• Varma, A. (2017). Zara’s secret to success lies in big data and an agile
supply chain’. The Straits Times.
Plagiarism is defined as inserting words/sentences/ideas belonging to other
author(s) in part or whole without referring to the sources. Students must
indicate the source of any words/sentences from other author(s) in his/her
writing. Plagiarism also refers to the copying in part or whole other students’
assignments or copying from books, journals, web, magazines, newspapers,
etc. Plagiarism also includes the act of auto-plagiarism defined as the use of
one’s own words/sentences/ideas taken from other assignments/papers that
have been submitted for grading in different or the same courses without any
reference to its/their source(s).
In accordance with the disciplinary rules and code of ethics for students as
indicated on the Guidebook of FEBUI, students are prohibited from
conducting plagiarism and will be sanctioned/punished accordingly.
The sanctions/punishment are as follows:
•
For the first-time offense, the minimum sanction is a zero (0) grade
for the assignment or maximum an F
•
•
The second-time offense, the grade for the course will be an F.
The third-time offense, the student will be expelled from the Department
of Management, FEBUI.
Statement of
Authorship
It is mandatory that a Statement of Authorship must be included and posted on
the front page of the assigned paper.
Statement of Authorship
I/We ........................ the undersigned declare to the best of my/our ability that
the paper/assignment herewith is an authentic writing carried out by
myself/ourselves. No other authors or work of other authors have been used
without any reference to its sources.
This paper/assignment has never been presented or used as paper’ assignment
for other courses except if I/we clearly stated otherwise.
I/We fully understand that this assignment can be reproduced and/or
communicated for the purpose of detecting plagiarism.
Name
:
Student’s ID Number
:
Signature
:
Course
:
Paper/Assignment Title
:
Date
:
Lecturer
:
(signed by all and every single student if it is a group assignment)