Master in Applied Business Analytics

Master in Applied Business Analytics (MABA) is UA&P’s pioneering program that was launched in 2018. It is a 2-year part-time graduate program for experienced professionals in any industry who want to seize the power of data and analytics in his/her work for his/her organization. MABA will enable its graduates to reconnect to the world’s new social landscape intricately painted by the pandemic. With the analytics expertise, they will collaborate with their colleagues in charting a path to the next normal that unfolds new horizons and opportunities for everyone. They will help enable governments, businesses, and individuals to creatively define the right analytics solution for their citizens, employees, customers, and selves as they treat data with ethics and values.

Learning Outcomes

UA&P MABA graduates will have the capacity to carry out the following:

  1. Help institutions/companies use analytics as strategic tool for decision making.
  2. Describe and visualize data and communicate its insights effectively in story form.
  3. Correctly apply analytics processes, techniques, algorithms and tools on actual data to derive insights to solve business problem/s.
  4. Design and implement data management processes from acquisition or creation, storage, retrieval and maintenance of data that will be analyzed.
  5. Plan, manage, evaluate and direct analytics projects from beginning to end.
  6. Follow the research process in completing an analytics project.
  7. Identify, define, and resolve ethical and legal concerns specific to data analytics as they pertain to persons, organizations, and society.

Program Delivery

The key to a successful analytics program for business students is in the implementation. As an applied business analytics program, MABA is an applied, multi-disciplinary, experience building, and collaborative program. Its pedagogy and emphasis will differ from Data Science programs. Faculty from the academe and industry work together to combine theory and practice i.e., integrating business, technology, communication, and quantitative disciplines with liberal education.  Our partnership with the Analytics Association of the Philippines (AAP) and sponsor-companies will help you work on real problems of real clients with actual data for your analytics course projects. Courses are project-oriented employing inquiry-based approach to learning to build thinking skills such as critical thinking and problem-solving skills.

Curriculum

Applied Business Analytics 9

Foundational Courses

• Fundamentals of Business and Management (2 units)

This course provides the students with an opportunity to demonstrate understanding of the underlying theories, concepts, and principles in organization, business, and management domains including the critical analysis of various internal and external factors affecting them.   It  aims to help the students explore and make sense of  contemporary issues, trends and constructs  in managing  today’s business organization especially  those that pertain to key business functions such as  marketing, strategy,  finance, accounting, human resources, information systems, and operations.  The students will also be guided on how to critically examine and analyze business decisions in each of afore-mentioned business functions.

• Mathematics for Analytics (2 units)

The course will focus on math topics that are necessary in understanding concepts in analytics. It includes topics in linear algebra, specifically matrices and their applications. This course will also cover basic concepts in differential and integral calculus and their applications.

• Basic Computing (2 units)

It is assumed that the students have some basic familiarity with computer programming. This course aims to further enhance the knowledge and skills of MABA students to conduct computing for analytics. The course will introduce and discuss the use of Python, a high-level programming language that is currently among the most popular languages for performing analytics.

The first part of the course will cover fundamental Python programming concepts, data structures, object-oriented programming, and the use of Python modules. The second part will focus on specific Python modules that are useful for performing analytics: numpy for scientific computing, pandas for data manipulation, and matplotlib for plotting.

The course will use Jupyter Notebooks, a tool that runs on a web browser and can execute codes in Python and R.  Jupyter combines both code and text formatting, thus facilitating not only the meaningful presentation of an analytics project but also its reproducibility.

• Business Strategy and Analytics (2 units)

The course will focus on the use of data as a strategic tool for decision making. It will instill a general analytical intuition needed to develop strategies for organizations to compete and operate more effectively and efficiently. Students will understand the organizational environment in which Analytics exists. They will learn the interaction of many competencies, people, and processes involved in any Analytics project to properly manage the information flow between the business-driven and technically-oriented environments.

• Data-Driven Organization and Management (2 units)

This course will cover the relevance, challenges and value of building a data-driven enterprise where the role of data and analytics is integral to decision-making. The students will learn how to explore and leverage data-driven opportunities and insights from multiple perspectives that would unlock business value and moments. They will learn to inspect, interpret, structure and synergize relevant data and processes with the objective of discovering useful information, sound solution and supporting a decision.

• Descriptive Analytics, Visualization & Storytelling (Storifying Analytics) (2 units)

The course renders itself to be called “Storytelling with Data”, which aims to teach the business executive (or student) to make sense of big data, describe and visualize it, and communicate it effectively in story form. This course preaches “Story first” and it contextualizes data and analytics as the driving force in making an organizational story more organic and strategic. The course has three major parts: 1) Storifying Analytics, 2) Visualization and Presentation, and 3) Storytelling and Delivery

• Statistical Computing (2 units)

This course will introduce the basics foundation of Statistics. It will take students to undergo the process of data analysis which is very crucial in the data science practice; from getting to know the data, spotting anomalies and dealing with them, exploring patterns, formulating hypothesis, and testing them then finally making inferences based on findings. Moreover, with the growing use of technology today, this course will be taught manually and with the use of R. The students are expected to have at least a background on basic mathematical notation and some algebra. Knowledge of basic Statistics and R will be beneficial.

• Insight Development and Innovation (2 units)

In this course, the student will learn how to develop and leverage consumer insights to inspire brand and business growth. The student will be able to:  (1) acquire a deeper understanding of consumer psyche and behavior; (2) develop and leverage consumer understanding to generate insights through the step-by-step insight generation process; and  (3) identify the innovation approach that will bring to life the most relevant insight that will respond to a business opportunity.

• Analytics Algorithms 1 (2 units)

This course will cover popular data mining frameworks to model patterns and trends in the data to understand the future or fill in missing information. It will introduce computational methods in statistics, natural language processing and machine learning, and how these techniques are integrated and deployed to effectively harness the power of predictive analytics in an organization. Model implications, impact, and assumptions will be discussed as they pertain to a variety of business problems. Students will learn common supervised and unsupervised methods, algorithms, and techniques for answering predictive questions from data.

• Human Perspective of Analytics (2 units)

This ethics course is fundamentally grounded on a philosophical anthropological understanding of the human person. It builds on the premise that the human person starts at the moment of conception and is essentially structured with a nature that comprises of a body, emotions, and a spiritual soul, all of which have their dynamic natural tendencies towards their ends. A clear understanding of these tendencies, the basic features of the person, and especially the will and freedom are key to an ethics that is suited to the flourishing of the person as a human being. The course offers principles not merely to avoid doing what is evil as required by human dignity but aims at promoting the excellence that is worthy of being human (Greek arete, Eng. virtues). Ethics proper specifically studies the nature and principles of human action but the perspective adopted is how human action aligns with the last end of the person. This means that it takes on the natural law framework in the critical assessment and evaluation of ethical issues.  It includes virtues because it is not possible to be a good person without the perfection of the human powers that enable us to do the good.

• Management of Analytics Projects (2 units)

The course will cover the typical phases in an Analytics project. However, projects are often characterized by uncertain or changing requirements and high implementation risk. These projects could either be to deploy new Analytics solutions or maintaining or enhancing existing solutions. The students will learn how to plan, manage, evaluate and direct Analytics projects from beginning to end.

• Programming for Databases (2 units)

This course introduces students to database management systems that handle data that no longer fit typical desktop software such as Excel. First to be covered will be relational databases and relational data modeling. Students will then be introduced to the Structured Query Language (SQL), which will enable them to implement their data models, and add, update, delete and analyze data within the database. Next topic will be on designing, implementing, and querying data warehouses in a relational database. Finally, students will be introduced to NoSQL databases that are also widely used to enable analytics and processes and tools used to ensure data integrity and security.

• Analytics Algorithms 2 (2 units)

The course will cover theories and practices aimed at identifying solutions to an optimization problem given the constraints and the objective. It will start where the predictive analytics course left off, covering more advanced topics: time series forecasting, support vector machines, deep learning, and natural language processing. Additionally, it will introduce computational methods in operations research e.g. optimization and simulation models, linear programming, and transportation problem. Students will be able to transform a given optimization problem into analytical models to provide decision-makers with advice on what best action to take.

• Data Engineering (2 units)

This course deals with the processes and techniques used to move data from different formats and from different sources into something that can be readily and efficiently used for analytics. Using what they learned from the Basic Computing and Programming for Databases projects, students will learn to perform Extract, Transform and Load (ETL) data from different types of sources to a data warehouse. The latter part of the course will also discuss cloud computing and tools typically used in the industry to achieve moving large amounts of data in an efficient and timely manner.

• Ethics and Law in Data Analytics (2 units)

The course will cover the ethical and legal frameworks of data analytics. Powerful tools in analytics to create real-world outcomes which are either for good or for ill. Students will develop and implement data management governance and strategies that incorporate privacy and data security, policies and regulations, and ethical considerations.

• Capstone | 6 units (Broken in two courses i.e., Capstone 1 and 2)

In a culminating project, the student draws on the breadth and depth of the curriculum to provide an integrative and comprehensive analytics solution for the client in a capstone project. The student applies the science of analytics to data to inform strategic and operational decisions that will drive business and organizational value with humanist, ethical and legal perspectives and presents the analytics solution to a panel comprising academics, industry analytics practitioners, and the client sponsor.

Faculty and Capstone Advisers

MABA students will be mentored by the following esteemed faculty and capstone advisers:

Reynaldo C. Bonita, Jr.
Lecturer, UA&P
Master of Data Science, Monash University
Bachelor of Science in Information Technology, UA&P

Antonio C. Briza
Consultant, University Center Foundation
MS in Computer Science, UP Diliman
BS in Computer Science, UP Diliman

Atty. Jo Aurea M. Imbong
University Counsel, UA&P
Bachelor of Law, UP Diliman

Yves Y. Kangleon
Director – Customer Analytics & Loyalty, Cebu Pacific Air
MBA, De La Salle University
BS Mathematics, ADMU

Macrina P. Lazo
Head of Banking Analytics, Eastwest Bank
Master in Business Economics, UA&P
BS Statistics, UP Diliman

Ruth L. Legaspi
Assistant Vice President for Business Development Strategic Planning & Analytics, McDonald’s Philippines
BA in Communication, UP Diliman

Dominic Vincent D. Ligot
Founder & Managing Director, Cirrolytix Research Services
Co-founder, Analytics Association of the Philippines
BS in Business Administration, UP Diliman

Ruel V. Maningas
Program Director, Master in Applied Business Analytics
Adjunct Assistant Professor, UA&P
Ph.D. major in Extension Education and minor in Computer Science, UP Los Baños
Master in Management (M.M.) major in Development Management, UP Los Baños
Bachelor of Arts major in Economics, Colegio de San Juan de Letran-Calamba

Elmer C. Peramo
Project Development Officer, Advanced Science and Technology Institute (ASTI-DOST)
ME in Electrical Engineering, UP Diliman
BS in Computer Science, Mondriaan Aura College

Enrique P. Portugal
Instructor, UA&P
MA in Philosophy, UP Diliman
BA in Philosophy, UP Diliman

Sherwin M. Pelayo
Analytics Practice Leader, Teradata GCC Philippines
Co-Founder, Analytics Association of the Philippines
BS in Physics, AdMU

Maria Veronica P. Quilinguin
Assistant Professor, UA&P
Ph.D. in Mathematics, UP Diliman
MS in Mathematics, UP Diliman
BS in Applied Mathematics, UP Diliman

Brenda A. Quismorio
Business Process and Analytics Lead
Corporate Planning and Review, UA&P
Assistant Professor, UA&P
Co-founder, Analytics Association of the Philippines
PhD in Business Administration, UP Diliman
MS in Management, UP Diliman
MBA, UP Diliman
BS in Statistics, UP Diliman

Fatima R. Ravalo
Lecturer, UA&P
Market Research Consultant
BS Statistics, UP Diliman

Eva M. Rodriguez
Assistant Professor, UA&P
PhD in Mathematics, UP Diliman
MS in Mathematics, UP Diliman
MA in Education Major in Liberal Education, UA&P
BS Mathematics, UP Diliman

Varsolo C. Sunio
Specialist, Accenture
PhD Urban Management, Kyoto University, Japan
MSc Industrial and Systems Engineering, National University of Singapore
MS Physics, UP Diliman
BSc Physics and Computer Engineering, ADMU

Johann Vincent Paul U. Tagle
Senior Data Engineer, Packetworx, Inc.
MBA, AdMU
BS Electrical Engineering, UP Diliman

Angelita P. Tobias-Lozano
Data Scientist, JG Summit Holdings, Inc.
Doctor of Philosophy in Statistics​​ (candidate), UP Diliman
MS in Statistics​​, UP Diliman
BS in Statistics​​, Central Luzon State University

Noemi B. Torre
Assistant Professor, UA&P
Doctor in Mathematics, UP Diliman
MS Applied Mathematics, UP Diliman
BS Mathematics, UP Diliman

Kimberly May M. Vallesteros
Program Director, Bachelor of Science in Data Science (BSDS)
Instructor, UA&P
MS Applied Mathematics, UP Diliman
BS Mathematics, UP Diliman

Christian R. Vallez
Head of Business Strategy, Overmind
Director and Consultant, Arsokos, Corporation
Narrative Consultant, USAID-COMPETE
Chairman and Artistic Director, Arts and Culture Asia, Inc.
Instructor, UA&P
Masters in Business Economics, UA&P
MA Humanities, UA&P
MS Information Technology, UA&P
BS Information Technology, UA&P

Jayson T. Yodico
Data Scientist, Union Bank of the Philippines
Lecturer, UA&P
Master of Science in Data Science, Asian Institute of Management
Bachelor of Science in Industrial Engineering

 

ADVISORY COUNCIL

Bernardo M. Villegas
Doctor in Economics, Harvard University

Winston Conrad B. Padojinog
Doctor in Business Administration, DLSU
MS Industrial Economics, UA&P
BS Economics and Management, UP Visayas

Amado P. Saquido
VP for Academic Affairs, UA&P
PhD in Business Administration (Finance), UP Diliman
MS in Information Management, AdMU
BS in Electrical Engineering, UP Diliman

Schedule

Classes which run for 3-hours are scheduled on weekday nights (6pm to 9pm), Saturdays morning (9am -12nn) or Saturday afternoon (1:30pm-4:30pm). Schedules are confirmed just before the beginning of each term.

Admission

Admission Criteria

Applicants to the UA&P MABA Program must hold a bachelor’s degree from an accredited institution with substantial professional experience in a particular domain. Having a quantitative background and occupied a management role are highly advantageous. Experience in a domain and managing projects are key factors to your success in the program.

Application Process*

  • The applicant must at least submit the following in soft-copy:
    • Complete the application form. (Request a copy of the form)
    • Transcript of records or any copy of the applicant’s collegiate grades;
    • Curriculum Vitae or Resume; and
    • A 2-minute video
    • The other documents stated in the application form may also be submitted in soft copy. If not possible, may be submitted at a later date.
  • Instead of an exam, submit a two-minute video of the applicant talking about the analytics for a specific person e.g. leader (government official, policy maker, business (large, SME, NGO), worker, citizen, parent, student, etc.
  • Get interviewed by the MABA program.
  • Know the status of your application.
  • For successful applicants, enroll in Bridging Courses and attend Analytics Bootcamp.

*current application process, subject to change when the COVID-19 pandemic situation eases.

Important Dates

  • Information Session: March 27, 2021 | Saturday
  • Application Deadline: April 30, 2021 | Friday
  • Interview Dates: May 8, 15, & 22, 2021 | Saturdays only
  • Release of Application Result: May 29, 2021 | Saturday
  • Bridging Classes: June 5 – July 31, 2021 | Saturdays only
  • Analytics Bootcamp: August 7, 2021 | Saturday
  • Student’s Orientation: August 21, 2021 | Saturday
  • Class Start Date: August 28, 2021 | Saturday

Contact Us

MABA Program
School of Management
6th Floor, APEC Communications Building
Telephone: 8637-0912 local 309
E-mail: businessanalytics@uap.asia

Get in touch

Address

Pearl Drive, Ortigas Center, Pasig City 1605, Philippines

 

Contact

Phone: (632) 8637-0912 to 26
E-mail: info@uap.asia

 

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