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

1. Leverage data to inform strategic and operational decisions.
2. Utilize data and analytical models to inform specific functions and business decisions.
3. Leverage data analysis and modeling techniques to solve problems and glean insight across functional domains.
4. Create analytical models to derive insights from data. 
5. Oversee analytical operations and communicate insights to executives. 
6. Identify, define, and prioritize ethical concerns related to data analytics as they pertain to persons, organizations and society.

1. Make a meaningful contribution to the strategic decision making of the organization (if capstone is classified as industry application) or to the development of new methods and algorithms (if classified under “Methodology development and innovation); or to the advancement of knowledge in a particular domain (if categorized as “emerging knowledge”).
2. Design and implement data management processes from acquisition or creation, storage, retrieval and maintenance of data that will be analyzed.
3. Correctly apply analytics processes, techniques, algorithms and tools on actual data to derive insights to solve business problem/s.
4. Plan, manage, evaluate and direct analytics projects from beginning to end.
5. Identify, define and resolve ethical and legal concerns specific to data analytics as they pertain to persons, organizations, and society
6. Follow the research process in completing an analytics project. The project makes use of the review of related works as reference in evaluating the usefulness, correctness, feasibility, appropriateness and reliability of its solution and recommendations.
7. Communicate findings of the project effectively in both written and oral presentations.

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.

• Business and Management Theories, Concepts, and Cases  (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, cases,  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.

• Computing for Analytics (2 units)
  This course aims to equip MABA students to perform basic computing for data analytics. The course will introduce and discuss the use of Python, a high level programming language that is among the most popular languages for performing analytics at present.The course will use Anaconda, a popular package for doing projects in analytics. Anaconda includes Jupyter Notebooks, a tool that runs on a web browser and can execute codes in Python and R, among other programming languages. Jupyter combines both code and text formatting, thus facilitating not only the meaningful presentation of an analytics project, but also its reproducibility.Topics in the course include fundamental Python programming concepts, use of Python modules in general, and discussions on specific Python modules commonly used in performing data analytics: numpy for scientific computing, pandas for data manipulation, and matplotlib for plotting.

• 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 teach the students the relevance, challenges and value of building a data-driven enterprise where the role of data and analytics are integral to business decision-making. The students will (1) learn the fundamentals of building a data-driven organization; (2) assess and unlock the business opportunities and create value moments; (3) discern the importance of leadership and influencing skills; (4) design an applicable data-driven ecosystem anchored on business goals.

• Descriptive Analytics, Visualization, and Storytelling (Storifying Analytics) (2 units)
  This course is officially called Descriptive Analytics, Visualization, and Storytelling. But to simplify, we’re calling it Storytelling with Data to highlight the main thrust of this course — to teach one how to tell an engaging and effective story with the data and analysis at hand. In this class, we preach “STORY FIRST” to emphasize that no amount of analytics is useful if it cannot be communicated and understood. In this 2-unit course, we shall focus on three skills, which will serve as the modules: (1) Transforming your data and insight into a compelling story, (2) Representing your story with effective visuals and data visualization, and (3) Delivering your story orally in the simplest and affective manner possible.

• 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 hypotheses, 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)
  The 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 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 fundamentals and standards of project management as outlined by the Project Management Institute in the Project Management Body of Knowledge (PMBOK®). However, Analytics projects are often characterized by uncertain or changing requirements and high implementation risk. The course will, therefore, cover as well various project methodologies – such as the Waterfall Model and Agile – and various Analytics methodologies – such as the Kimball Lifecycle Methodology, KDD, SEMMA, and CRISP-DM – to determine the most apt project management approach to successfully deliver Analytics projects from beginning to end.

• Programming for Databases (2 units)
  This course is about database systems and how to programmatically interact with them. It includes representing information with the relational database model, manipulating data with an interactive query language (SQL) and database programming. The course will also touch 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 problems. 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)
  The capstone research project is a culminating course where a 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. While under the guidance of a Capstone adviser and/or industry expert and business domain advisers, the project is an independent individual analytics research project.

MABA is a 2-year part-time program with 3 terms each:

• Term 1: August to November
• Term 2: January to March
• Term 3: April to July  

Classes which run for 3-hours are scheduled on a weekday evening (6:00pm – 9:00pm), Saturday morning (9:00am – 12:00nn) or Saturday afternoon (1:30pm – 4:30pm) with a total of 9 hours per week. Schedules are confirmed just before the beginning of each term.

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 occupying a management role are highly advantageous. Experience in a domain and managing projects are key factors to your success in the program.

Application Process*

1. The applicant must at least submit the following in soft-copy:

• 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.

2. Instead of an exam, the applicant needs to 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..

3. Applicant will be interviewed by the MABA program representative

4. Applicant will be notified as to the status of his/her application.

5. Applicants who will pass in the initial screening will enroll in Bridging Courses and attend Analytics Bootcamp.

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

Master in Applied Business Analytics (MABA) Program
School of Management (SMN)
6th Floor, APEC Communications Building (ACB)
University of Asia and the Pacific
Pearl Dr, Ortigas Center, Pasig, 1605
[email protected]

Dr. Ruel V. Maningas
Program Director
[email protected]

Ms. Marietta B. Galindes
Program Officer
[email protected]
8-637.09.12 local 309

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