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The Master in Applied Business Analytics (MABA) Program, launched in 2018, is a two-year graduate program for experienced professionals in any industry who want to seize the power of data and analytics in their work to forward their organization. It is designed for working professionals who are starting a career in analytics or wanting to build their managerial expertise and take their analytics career to the next level.<\/span><\/p>\n

MABA is an applied, multi- disciplinary, experience building and collaborative program. 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), Amazon Web Services (AWS) Academy,\u00a0 and other sponsor companies helps the students to work on real problems of real clients with actual data for their analytics course projects. Courses are project-oriented employing inquiry-based approach to learning to build critical thinking and problem-solving skills.<\/span><\/p>\n

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Program Objectives<\/b><\/h5>\n

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1. To become more relevant to the clarion call of Industrial Revolution 4.0 and the changing landscape of the digital economy through a graduate program that is responsive to the growing data analytics needs and concerns of the industry, government and non-government organizations, academe, and other emerging institutions in our society (e.g. humanitarian, response and mitigation teams and networks, quasi-government and commissioned organizations, virtual workplaces, etc).<\/span><\/li>\n
2. To\u00a0 provide organizations and communities across industries with much-needed analytics professionals who are schooled in humanist and ethical perspectives and honed in data-driven leadership and management to make sense of data and derive insights that will drive business and organizational solutions<\/span><\/li>\n<\/ol>\n

   [\/et_pb_text][et_pb_toggle title=”Program Outcomes” icon_color=”#ec9715″ open_icon_color=”#ec9715″ _builder_version=”4.24.1″ title_text_color=”#ec9715″ title_level=”h2″ title_text_align=”left” custom_margin=”||5px||false|false” custom_padding=”||||false|false” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n

   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:<\/span><\/p>\n

   \n
   1. \n
      \n
      1. Leverage data to inform strategic and operational decisions.<\/span><\/li>\n
      2. Utilize data to create analytical models to inform specific functions and business decisions.<\/span><\/li>\n
      3. Leverage data analysis and modeling techniques to solve problems and glean imperatives and recommendations across functional domains.<\/span><\/li>\n
      4. Help the organization\u00a0 implement the digital transformation process\u00a0 through cutting-edge data analytics, artificial intelligence\u00a0 (AI), and other emerging tools and technologies.<\/span><\/li>\n
      5. Oversee analytical operations and communicate insights to executives for planning, policy formulation, and informed decision-making.\u00a0<\/span><\/li>\n
      6. Identify, define, and prioritize ethical and legal concerns related to data analytics as they pertain to persons, organizations, and society.<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

         MABA graduates can pursue the following analytics-related professional careers at the top, middle, and supervisory levels of management in various organizations:<\/span><\/p>\n

         <\/span><\/p>\n

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         • \n
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           • \n
             \n
             • Chief Analytics Manager\/Officer<\/span><\/li>\n
             • Chief Data Officer<\/span><\/li>\n
             • Analytics Project Manager\u00a0<\/span><\/li>\n
             • Data Engineering Manager<\/span><\/li>\n
             • Data Science Manager<\/span><\/li>\n
             • Analytics Translator<\/span><\/li>\n
             • Business Intelligence Officer\u00a0\u00a0<\/span><\/li>\n
             • Business Insights and Innovations Manager<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

               [\/et_pb_toggle][et_pb_toggle title=”Curriculum” icon_color=”#ec9715″ open_icon_color=”#ec9715″ _builder_version=”4.24.1″ title_text_color=”#ec9715″ title_level=”h2″ title_text_align=”left” custom_margin=”||5px||false|false” custom_padding=”||||false|false” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n

               Program Curriculum (Total number of Units \u2013 36)<\/h5>\n

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               Curriculum Map<\/h5>\n

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               [\/et_pb_toggle][et_pb_toggle title=”Course Descriptions” icon_color=”#ec9715″ open_icon_color=”#ec9715″ _builder_version=”4.21.0″ title_text_color=”#ec9715″ title_level=”h2″ title_text_align=”left” custom_margin=”||5px||false|false” custom_padding=”||||false|false” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n

               1st Year Courses<\/b><\/h5>\n

               <\/strong><\/p>\n

               MAB2114: Business and Management Theories, Concepts, & Cases<\/strong><\/p>\n

               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,\u00a0 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.<\/span>
               <\/strong><\/strong><\/p>\n

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               MAB2112: Business Strategy and Analytics<\/strong><\/p>\n

               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.\u00a0<\/span><\/p>\n

               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\u2019s goals and objectives, key activities and targets and KPIs that will be addressed\/supported by the identified BA strategies and key initiatives.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2115: Computing for Analytics<\/strong><\/p>\n

               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.\u00a0<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2122: Descriptive Analytics, Visualization, and Storytelling<\/strong><\/p>\n

               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.<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2120: Statistical Computing<\/strong><\/p>\n

               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.\u00a0<\/span><\/strong><\/p>\n

               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.\u00a0 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.<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2209: Programming for Databases<\/strong><\/p>\n

               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.\u00a0<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2213: Data Engineering<\/strong><\/p>\n

               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.\u00a0 Students will also be introduced to\u00a0 Continuous Integration \/ Continuous Delivery and\/or Continuous Deployment (CI\/CD) and basic concepts in Machine Learning Model Operationalization Management (MLOps).<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2134: Analytics Algorithms 1 (Predictive Analytics 1)<\/strong><\/p>\n

               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. \u00a0 Model implications, impact, and assumptions will be discussed as they pertain to a variety of business problems. <\/span><\/strong><\/p>\n

               <\/span><\/strong><\/p>\n

               MAB2131: Human Perspective in Analytics<\/strong><\/p>\n

               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\u00a0 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.\u00a0<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/strong><\/p>\n

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               2nd Year Courses<\/b><\/h5>\n

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               MAB2211: Management of Analytics Projects<\/strong><\/p>\n

               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\u00ae). However, Analytics projects are often characterized by uncertain or changing requirements and high implementation risk.\u00a0<\/span><\/strong><\/p>\n

               The course will, therefore, cover as well various project methodologies \u2013 such as the Waterfall Model and Agile \u2013 and various Analytics methodologies \u2013 such as the Kimball Lifecycle Methodology, KDD, SEMMA, and CRISP-DM \u2013 to determine the most apt project management approach to successfully deliver Analytics projects from beginning to end.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2216: Analytics Algorithms 2 (Predictive Analytics 2)<\/strong><\/p>\n

               This course is the continuation of Predictive Analytics 1 and covers three major topics namely:\u00a0 Modern and Advanced Machine Learning Tools,\u00a0 Time Series Forecasting,\u00a0 and Text Mining and Natural Language Processing. \u00a0 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. <\/span><\/strong><\/p>\n

               <\/span><\/strong><\/p>\n

               MAB2217: Data-Driven Insights Development and Innovation<\/strong><\/p>\n

               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.\u00a0 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\u00a0 help formulate business strategies,\u00a0 develop key performance metrics and indicators, inform policy decisions, and create business opportunities, to name a few.<\/span><\/strong><\/p>\n

               <\/span><\/strong><\/p>\n

               MAB2223: Analytics Algorithms 3 (Prescriptive Analytics)<\/strong><\/p>\n

               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.<\/span><\/p>\n

               <\/span><\/p>\n

               MAB2220: Ethics and Law in Data Analytics<\/strong><\/p>\n

               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.<\/span><\/strong><\/p>\n

               <\/span><\/strong><\/p>\n

               MAB2215: Capstone 1<\/strong><\/p>\n

               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.<\/span><\/strong><\/p>\n

               In this course, the students\u2019 main deliverable is the\u00a0 capstone proposal that consists of three chapters:\u00a0 Chapter 1 (Introduction), Chapter 2 (Literature Review), and Chapter 3 (Methodology)\u00a0 for presentation to the capstone panel of evaluators.<\/span><\/p>\n

               <\/strong><\/p>\n

               MAB2230: Capstone 2<\/strong><\/p>\n

               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.<\/span><\/strong><\/p>\n

               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.<\/span><\/p>\n

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               Electives<\/b><\/h5>\n

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               MAB2231A: Data Entrepreneurship<\/strong><\/p>\n

               Now that we are part of Industry 4.0 and more organizations are implementing their digital transformation strategy, it is <\/span>vital to understand the capability of different existing and emerging technologies and tools. Leading organizations with mature <\/span>understanding of analytics know how to leverage on data and build efficient infrastructure to create and deliver compelling <\/span>business value and competitive advantage.\u00a0<\/span><\/strong><\/p>\n

               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.\u00a0<\/span><\/strong><\/p>\n

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

               <\/span><\/p>\n

               MAB2231B: IT Service Management Architecture and Frameworks<\/strong><\/p>\n

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

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

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

               <\/span><\/p>\n

               MAB2231C: Advanced Data Visualization<\/strong><\/p>\n

               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 <\/span>and writing texts.<\/span><\/p>\n

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

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

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               MAB2231D: Data Governance<\/strong><\/p>\n

               Data Governance is a core component of an overall data management strategy and like security, should also be <\/span>considered as a \u201cday 0 activity\u201d. 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.<\/span><\/strong><\/p>\n

               The objective of this course is the provide the students an understanding and appreciation of the following:<\/span><\/p>\n

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               1. \n
                  \n
                  1. What is Data Governance and why does it matter?<\/span><\/li>\n
                  2. Critical functions and challenges in implementing Data Governance<\/span><\/li>\n
                  3. Who\u2019s responsible for Data Governance?<\/span><\/li>\n
                  4. Creating a Data Governance framework and its operating model<\/span><\/li>\n
                  5. Data Lifecycle Management<\/span><\/li>\n
                  6. Data Governance Strategy and Roadmap<\/span><\/li>\n
                  7. Common Data Governance initiatives and its best practices<\/span><\/li>\n
                  8. Establishing Data Governance towards Data Driven Organization<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

                     At the end of the course, it is expected that the participants should be able to have a full grasp of how the \u201clifecycle of data\u201d 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.<\/span><\/p>\n

                     <\/span><\/p>\n

                     MAB2231E: Operations Research<\/strong><\/p>\n

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

                     [\/et_pb_toggle][et_pb_toggle title=”Capstone Project Overview & Learning Outcomes” icon_color=”#ec9715″ open_icon_color=”#ec9715″ _builder_version=”4.21.0″ title_text_color=”#ec9715″ title_level=”h2″ title_text_align=”left” custom_margin=”||5px||false|false” custom_padding=”||||false|false” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n

                     The capstone research project is the program\u2019s 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.<\/span><\/p>\n

                     <\/b><\/h5>\n

                     Capstone Project Tracks\u00a0<\/b><\/h5>\n

                     Industry Application
                     <\/strong>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\u2019s work lies on the practical application of known algorithms on organization\/ enterprise\/business datasets for decision-making.<\/p>\n

                     Methodological Development and Innovation<\/strong>
                     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.<\/p>\n

                     Emerging Knowledge<\/strong>
                     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.<\/p>\n

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                     Capstone Project Learning Outcomes<\/strong><\/h5>\n

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                     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 \u201cMethodology development and innovation); or to the advancement of knowledge in a particular domain (if categorized as \u201cemerging knowledge\u201d).<\/span><\/li>\n
                     2. Design and implement data management processes from acquisition or creation, storage, retrieval and maintenance of data that will be analyzed.<\/span><\/li>\n
                     3. Correctly apply analytics processes, techniques, algorithms and tools on actual data to derive insights to solve business problem\/s.<\/span><\/li>\n
                     4. Plan, manage, evaluate and direct analytics projects from beginning to end.<\/span><\/li>\n
                     5. Identify, define and resolve ethical and legal concerns specific to data analytics as they pertain to persons, organizations, and society<\/span><\/li>\n
                     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.<\/span><\/li>\n
                     7. Communicate findings of the project effectively in both written and oral presentations.<\/span><\/li>\n<\/ol>\n

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