Students develop analytics solutions and begin leading data-driven projects using different perspectives. Working on real data, students apply algorithms and other related tools and methodologies to derive insights to solve problems of stakeholders across industries. When they graduate from the program, they will have the capacity to carry-out the following:

1. 1. Leverage data to inform strategic and operational decisions.
   2. Utilize data to create analytical models to inform specific functions and business decisions.
   3. Leverage data analysis and modeling techniques to solve problems and glean imperatives and recommendations across functional domains.
   4. Help the organization  implement the digital transformation process  through cutting-edge data analytics, artificial intelligence  (AI), and other emerging tools and technologies.
   5. Oversee analytical operations and communicate insights to executives for planning, policy formulation, and informed decision-making. 
   6. Identify, define, and prioritize ethical and legal concerns related to data analytics as they pertain to persons, organizations, and society.

MABA graduates can pursue the following analytics-related professional careers at the top, middle, and supervisory levels of management in various organizations:

• • • Chief Analytics Manager/Officer
    • Chief Data Officer
    • Analytics Project Manager 
    • Data Engineering Manager
    • Data Science Manager
    • Analytics Translator
    • Business Intelligence Officer  
    • Business Insights and Innovations Manager

1st Year Courses

MAB2114: Business and Management Theories, Concepts, & Cases

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 organization, profit or non-profit ones,  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 functional areas of operations.

MAB2112: Business Strategy and Analytics

This course focuses 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. 

As one of the key and integrative outputs of the course, the students will build a Business Analytics Strategy Roadmap, highlighting, among others, the case organization’s goals and objectives, key activities and targets and KPIs that will be addressed/supported by the identified BA strategies and key initiatives.

MAB2115: Computing for Analytics

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. 

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 such as numpy for scientific computing, pandas for data manipulation, and matplotlib for plotting. Among the tools that will be used or introduced in the course delivery are: Jupyter Notebooks, Google Colaboratory, Anaconda data science package, and integrated development environments (e.g. PyCharm and Visual Studio Code) and virtual environments.

MAB2122: Descriptive Analytics, Visualization, and Storytelling

This course is about 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.

The course focuses on three key modules: (1) transforming your data and insight into a compelling story; (2) representing your story with effective visuals built using Microsoft Excel, Microsoft Power BI, and (3) delivering your story orally in the simplest and most effective manner possible.

MAB2120: Statistical Computing

This course will introduce the basic foundations of Statistics, especially the process of data analysis which is very crucial in the practice of data science and analytics – from getting to know the data, spotting anomalies and dealing with them, exploring patterns, formulating hypotheses, testing them then finally making inference based on findings. 

This course will be taught with the use of R, a language and environment for statistical computing and graphics that is widely and commonly used in the academics and business communities.  R provides a wide variety of statistical modeling, testing, analysis, classification, and clustering of data which are essential to understanding the various topics that will be covered by the succeeding analytics algorithm courses.

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.

MAB2209: Programming for Databases

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.

MAB2213: Data Engineering

This course deals with the processes and techniques used to move data from in 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 key concepts related to data warehousing and perform Extract, Transform and Load (ETL) data from different types of sources to a data warehouse.  Students will also be introduced to  Continuous Integration / Continuous Delivery and/or Continuous Deployment (CI/CD) and basic concepts in Machine Learning Model Operationalization Management (MLOps).

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. Cloud computing platforms that are available and widely used by business organizations such as Google Cloud, Azure and AWS, will be discussed, compared and analyzed for better understanding of their use and benefits, to name a few.

MAB2134: Analytics Algorithms 1 (Predictive Analytics 1)

The course will introduce the students to the concepts of predictive analytics and to 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, machine learning fundamentals, common supervised and unsupervised methods, algorithms and techniques for answering predictive questions from data, 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.

MAB2131: Human Perspective in Analytics

This 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 good.

2nd Year Courses

MAB2211: Management of Analytics Projects

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.

MAB2216: Analytics Algorithms 2 (Predictive Analytics 2)

This course is the continuation of Predictive Analytics 1 and covers three major topics namely:  Modern and Advanced Machine Learning Tools,  Time Series Forecasting,  and Text Mining and Natural Language Processing.   Business cases and applications will reinforce the understanding on how these techniques are integrated and deployed to effectively harness the power of predictive analytics in an organization. Just like in the Analytics Algorithm 1, model implications, impact, and assumptions will be discussed also in reference to a variety of business problems.

MAB2217: Data-Driven Insights Development and Innovation

This course will provide the students the opportunity to learn and make sense of the relevance, challenges and value of a data-driven enterprise where the role of data and analytics are integral to business decision-making.  The student will learn how to develop and leverage data to derive insights for strategic and operational decisions in the organization and identify the innovation approach that will bring to life the most relevant insight to  help formulate business strategies,  develop key performance metrics and indicators, inform policy decisions, and create business opportunities, to name a few.

MAB2223: Analytics Algorithms 3 (Prescriptive Analytics)

This course will focus on how optimization modeling techniques can be used to make decisions for different business analytics applications. The emphasis is on the formulation of different optimization problems and the use of the correct quantitative techniques to solve these problems. Several case studies related to topics such as financial planning, logistics, production planning, and inventory management will be discussed.

MAB2220: Ethics and Law in Data Analytics

The course will cover the ethical and legal frameworks of data analytics. Powerful tools in analytics 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. The course also focuses on leveraging responsible use of digital technology guided by ethical norms and legal principles as applied in case studies.

MAB2215: Capstone 1

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, the project is an independent individual analytics research project.

In this course, the students’ main deliverable is the  capstone proposal that consists of three chapters:  Chapter 1 (Introduction), Chapter 2 (Literature Review), and Chapter 3 (Methodology)  for presentation to the capstone panel of evaluators.

MAB2230: Capstone 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/business domain expert and technical adviser, the project is an independent individual analytics research project.

In this course, the students will implement the capstone project based on the approved capstone proposal with added components to complete the whole capstone project in written and actual form. These added components are Data Understanding, Data Preparation, Modeling, Evaluation, Results and Discussion, Conclusion and Recommendations (Chapters 4 to 10 of the project’s manuscript.

Electives

MAB2231A: Data Entrepreneurship

Now that we are part of Industry 4.0 and more organizations are implementing their digital transformation strategy, it is vital to understand the capability of different existing and emerging technologies and tools. Leading organizations with mature understanding of analytics know how to leverage on data and build efficient infrastructure to create and deliver compelling business value and competitive advantage. 

In this course, students will learn the business (e.g. business intelligence and data science domain) and technical (e.g. IT and data infrastructure) needs and requirements in the perspective of analytics. Students are expected to design a data entrepreneurship plan enumerating the key process, resources and tools that will support either the industry, organization or functions on their business strategic initiatives and operations. 

As success in every entrepreneurship endeavor is not warranted solely by the might of knowledge and skills in business technology, and other fields, it is imperative for students to be able to distinguish the attributes or traits that most successful entrepreneurs have in common, and to be able to recognize the impact of entrepreneurial pursuits to society.

MAB2231B: IT Service Management Architecture and Frameworks

The essence of scenario creations, understanding of realities or future states prediction is mostly determined by the reliability and availability of tools, platforms, and systems. When reliability and availability is being discussed in the IT landscape, IT Service Management, has been known as the prolific set of guiding principles. In this course, students will learn the best practices in IT Service Management, specifically ITIL or IT Infrastructure Library.

In building analysis models and simulations, one of the abundant sources of data is the enterprise resource planning (ERP) system in an organization. It is imperative for students to be able to understand that ERP dynamically attempts to break down silos in a firm and is best operated with the governing principles of IT Service Management.

Where speed or instantaneity has become the norm for almost everything in the business, students must be familiar with the basic cloud computing concepts and technologies as cloud is already a common structure that is critical in most business transactions.

MAB2231C: Advanced Data Visualization

Data visualization is used to explore, understand and communicate trends of quantitative data. With the explosion of data, visualization literacy which is the ability to read, interpret, and create data visualizations is becoming as important as reading and writing texts.

Good data visualization demands three different skills: substantive knowledge, statistical skill, and artistic sense. As such, this course is intended to introduce participants to key principles of analytic design and useful visualization techniques for the exploration and presentation of univariate and multivariate data. This course is highly applied in nature and emphasizes the practical aspects of data visualization to equip students to be good analysts and presenters.

In this course, students will not only learn how to evaluate data visualizations based on principles of analytic design but also how to construct compelling visualizations using static presentation and dashboards. Business intelligence tools such as Power BI and Tableau will be introduced in the course. Students will leverage the capabilities of these tools to further build their visualization skills.

MAB2231D: Data Governance

Data Governance is a core component of an overall data management strategy and like security, should also be considered as a “day 0 activity”. Like any other governance, Data Governance is also required to regulate practices and processes- specifically around data ingestion, storage, access and usage until data retention and archiving and deletion-lifecycle of data.

The objective of this course is the provide the students an understanding and appreciation of the following:

1. 1. What is Data Governance and why does it matter?
   2. Critical functions and challenges in implementing Data Governance
   3. Who’s responsible for Data Governance?
   4. Creating a Data Governance framework and its operating model
   5. Data Lifecycle Management
   6. Data Governance Strategy and Roadmap
   7. Common Data Governance initiatives and its best practices
   8. Establishing Data Governance towards Data Driven Organization

At the end of the course, it is expected that the participants should be able to have a full grasp of how the “lifecycle of data” is managed and governed effectively and efficiently in enabling the organization to organize, enable/democratize its data consumption to drive activities in an acceptable manner in order to make informed decisions, create value, resolve conflicts and manage risks, among others.

MAB2231E: Operations Research

The course is designed to introduce the students to the business modeling applications of Operations Research. The course starts with a discussion of the tools and techniques in graph and network theory, looking at its applications in transportation, scheduling and allocation problems. Afterwards, the course will tackle conceptual frameworks in Queueing Theory and Inventory Modelling with the goal of understanding its use in optimal design and inventory and queueing systems. The course will then discuss how Monte Carlo simulation can be used to understand and forecast real world business systems. The course proceeds with a discussion of the theory and application of Markov Chains. Finally, the course introduces the method of optimization via Dynamic Programming as a bridge to linear optimization methods in Algorithms II.

The capstone research project is the program’s culminating course. Students apply 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 present the analytics solution to a panel. While under the guidance of faculty-in-charge, capstone adviser and/or industry expert, the project is an independent individual analytics research project.

Capstone Project Tracks 

Industry Application
The capstone project directly benefits an organization, enterprise, or business. The students utilize the organization/ enterprise/business data and analyze it in order to help managerial decision-making. They act like a consultant for the organization. The value of the student’s work lies on the practical application of known algorithms on organization/ enterprise/business datasets for decision-making.

Methodological Development and Innovation
The capstone project focuses on the development of new methodologies and algorithms to solve business problems. Students may utilize open/public data. Since it is highly likely that various methods have been developed using these open datasets, the objective is now to come up with a new or innovative methodology.

Emerging Knowledge
The capstone primarily focuses on advancing scientific knowledge. The primary objective is to make use of established methods to gain a deeper understanding and knowledge of phenomena. This knowledge may then be used to inform policy and practice.

 

Capstone Project Learning Outcomes

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 MABA program is designed for working professionals who want to study on a part-time basis. Classes will be taking place on weekdays (6pm to 9pm) and/or Saturdays (9am to 12nn and 1:30pm to 4:30pm) in UA&P’s Ortigas campus. The tri-term schedules are:

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

Admission Criteria

Applicants to the UA&P MABA Program must hold a bachelor’s degree from an accredited institution with at least three years of professional experience. 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. Applicants will be interviewed by the MABA program representative.

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

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

S.Y. 2024 – 2025

• Information Session: March 30, 2024 | Saturday
• Application Deadline: April 20, 2024 | Saturday
• Interview Dates: May 4 & 11, 2024 | Saturdays
• Release of Application Result: May 18, 2024 | Saturday
• Bridging Classes: June 1 – July 27, 2024 | Saturdays
• Analytics Boot Camp: August 3, 2024 | Saturday
• Enlistment & Enrollment: August 13 – 17, 2024 | Tuesday – Saturday
• Student Orientation: August 23, 2024 | Friday
• Class Start Date: August 24, 2024 | Saturday

Analytics Association of the Philippines (AAP)

Amazon Web Services, Inc.

Technistock (Philippines), Inc.

Accenture

Department of Science and Technology – Advanced Science and Technology Institute

1. Are students allowed to complete the program at their own pace or are they required to follow the prescribed course of study?
Students may take the program at their own pace provided they complete it within the 4-year Maximum Residency Rule (MRR).

2. What is the difference between MABA and a data science program?
UA&P’s MABA program is geared at decision-makers, favoring reasoning with data over pure statistics and heavy data science. It takes a strategic, process, and unified view of Business Analytics for value creation with an applied and experience building approach. Unique to MABA is its blend of the various disciplines of Math and Statistics, Leadership and Management, and Ethics and Humanities for analytics applied to business. Our graduates may pursue roles as Analytics Managers and Consultants, data analysts or data scientists, or data-driven functional analysts and managers in a variety of industries. Best is to compare MABA with existing Data Science programs. However, it may be safe to say that Data Science programs in general tend to focus more on the technical aspects of Analytics.

3. Do you have to know how to use programs like Python and SQL before going to the classes?
No, but you MUST attend the bridging class and be ready to study more on your own to catch up. 

4. What are the laptop requirements?
Preference for Windows and at least 4GB RAM (but still depends on the data size to be uploaded). For technical subjects such as Computing for Analytics, Data Engineering, AA1, AA2 —  there may be no need for more storage capacity since they will be using cloud computing and online notebooks. But you still need to get the advice of the assigned faculty.

5. What are the bridging classes for?
The aim of the three bridging courses is to prepare you for specific foundational courses of MABA:

Mathematics for Analytics:  for  Analytics Algorithms 1,2, & 3
Statistics:  for Statistical Computing
Programming Logic:  for Computing for Analytics

6. What if I fail a bridging course?
Failure in the bridging classes does not automatically disqualify you for admission to the program. The three bridging courses are base knowledge to help you when you will enroll in formal MABA courses such as Computing for Analytics, Statistical Computing, and the Analytics Algorithms 1, 2, and 2 courses, to name a few. Your performance in the bridging classes is our way (and you also) to assess your readiness to the program. We encourage you to actively participate and ask questions or clarifications during the class session and work on and promptly submit the course requirements.

7. What is the Analytics Boot Camp for?
The Analytics Boot Camp is where you will present an analytics solution using what you have learned from the three bridging courses.

8. What is MABA’s mode of instructional delivery?
The mode of instruction for SY 2023-2024 will be ideally 50% or more onsite sessions, and the rest will be divided into online and asynchronous sessions.

9. Is your program delivery online?
We are looking into offering the whole program online in due course. Rest assured that we will announce it when it is already in place.

10. How much is the tuition fee?
Depending on the number of subjects enrolled, tuition fee is at Php5,525.00 per unit and total miscellaneous fee is around Php15,000.00 per term.

11. Do I have to pay the entire matriculation fee before classes start?
It can be paid either in full or in installments.

12. For students who want to take MABA subjects for audit.
The student must submit a letter of intent addressed to the Program Director. Once approved, the program will endorse the letter to the Registrar’s Office for processing.

13. Do you offer scholarships?
Unfortunately, we don’t offer scholarships or financial aid for the program as of the moment.

14. What are the class schedules?
One weekday evening, usually every Wednesday; 6pm-9pm and the whole day for Saturday; 9am-12pm and 1:30pm-4:30pm. All class schedules are confirmed within the month prior to the opening of classes.

15. What does the capstone project look like?
The Capstone is your integrative and final project for MABA. The class meetings are occasional and panel sessions are scheduled for students to defend their project requirements. Outside of class meetings and panel sessions, students must spend this time on their Capstone projects.

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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