Master of Science in Data Science, Asian Institute of Management

The Program

The Master of Science in Data Science (MSDS) program is a 14-month intensive course designed to produce experts in the fastest-growing, most sought-after specialization worldwide.

The program reflects the latest trends and best practices in the field — from data mining and exploration to machine learning, deep learning, and big data analytics. Moreover, learning will directly address one particular pain-point of the enterprise — when its technical experts cannot provide unique actionable insights.

At AIM, data scientists work closely with domain experts familiar with business and management issues. Students will, therefore, learn how to formulate the right questions and identify the correct datasets to address highly diverse business and research problems.

More crucially, they will be taught how to properly communicate results and data-driven insights to maximize their impact on business and industry.

All MSDS students will have access to a world-class computing facility housed at AIM. The facility is part of the Institute’s Analytics, Computing, and Complex Systems (ACCeSs) lab, AIM’s interface with various government and industry projects as well as world-class research to push the boundaries of Data Science and Complex Systems Science.

The Curriculum

The MS in Data Science (MSDS) program curriculum totals to 50 units, exhibiting a proper fusion of technical (data science and computing) courses and general business and management courses. One of the key features of the program is a capstone project, taken during the fourth term, that weaves together all the knowledge the students gain in the program and bringing them all to the real-world. While working with AIM partner business, industries, and other organizations, students will go through the entire process of data-driven project implementation: from problem formulation, to design thinking, to data collection, mining and wrangling, to model building, and finally, to project deployment, while making sure that the results produce valueable insights to the business/industry.

First Term

Programming for Data Science (2u)

Programming for Data Science

At the end of the course, students will:

Understand why programming, in general, is a required skill in data science
Be equipped with the necessary computing skills needed to perform various data science techniques and methods
Build one of their foundational toolboxes in data science
appreciate why Python is a useful scripting language for data scientists
Learn how to design and program Python applications

Christian Alis, Ph.D. Eduardo David Jr.

Units: 2

Mathematics for Data Science (3u)

Mathematics for Data Science

The module introduces the mathematics, including various techniques and formulations, necessary to implement several data science models and algorithms as well as machine learning and neural network models. Students will familiarize themselves with the different concepts, notations, and rules where most data science techniques and models are based upon.

Christopher Monterola, Ph.D

Units: 3

Intro to Data Science and the Fundamentals of Data Privacy (1u)

Intro to Data Science and the Fundamentals of Data Privacy

In this course, students will learn data science fundamentals and how the field has been used in the real-world. At the beginning, students will be provided with a comprehensive overview of the field. Students will then learn about the current state of data science and its future direction(s). The class will have data science practitioners who will share their experiences—from how companies come up with a data strategy toward becoming a truly data-driven organization, to building data science teams, to learning about the challenges companies faced and are currently facing. In this course, the fundamentals of data privacy and data ethics will also be covered. Finally, participants will learn about data workflows and pipelines. They will learn and appreciate how to assemble and lead data science enterprises.

Erika Legara Guest Lecturers: Raul Cortez, Teresa Condicion, JF Darre, Stephanie Sy

Units: 1

Data Viz and Storytelling (1u)

Data Viz and Storytelling

In this course, students will learn to appreciate the importance of successful data visualizations and intelligible data-driven stories in creating actionable insights. Using real-world datasets, learners will assimilate the necessary skills to fashion effective visualizations that exhibit not only good design elements but also layers of information that when weaved together as a narrative can generate actionable insights and new levels of understanding. The course will introduce learners to some business intelligence and analytics tools/software including Tableau (Public), QGIS, and Gephi (a network visualization tool). They will also learn how to create visualizations in Python using pandas, seaborn, networkx, and matplotlib.

Erika Legara, Ph.D.

Units: 1

Design Thinking (1u)

Design Thinking

At the end of the course, students will be able to:

Use Design Thinking for team management, meeting management, new product development, strategic planning and facilitation, process improvement and many others.
Enhance skills of observation and of user empathy
Quickly build prototypes and immediately test them in the field and iterate through the cycle of prototyping and testing

Ricardo Lim, Ph.D.

Units: 1

Human Behavior in Organizations (1u)

Human Behavior in Organizations

At the end of the course, students will be able to:

Discuss how an organization’s investments on its human capital lead to financial performance.
Evaluate the impact of HBO concepts on individual, intergroup and organizational behavior.
Apply HBO concepts to real-world problems faced by managers.

Jose Gerardo Santamaria, Ph.D.

Units: 1

Second Term

Applied Computational Statistics (3u)

Applied Computational Statistics

At the end of the course, students will be able to:

Define the basics of statistical reasoning, inferential methods.
Execute descriptive and diagnostic analytics from data.
Solve problems using foundational concepts in statistics --- probability and mathematics.
Integrate appropriate statistical models and methods for various types of analyses and datasets.
Interpret and communicate statistical results.
Build thorough working knowledge in data analytics using various statistical computing tools to solve problems from different fields

Christopher Monterola, Ph.D. Felix Valenzuela, Ph.D. Guido David, Ph.D.

Units: 3

Data Mining and Wrangling (3u)

Data Mining and Wrangling

At the end of the course, students will be able to:

Define the basics of statistical reasoning, inferential methods.
Execute descriptive and diagnostic analytics from data.
Solve problems using foundational concepts in statistics --- probability and mathematics.
Integrate appropriate statistical models and methods for various types of analyses and datasets.
Interpret and communicate statistical results.
Build thorough working knowledge in data analytics using various statistical computing tools to solve problems from different fields

Christian Alis, Ph.D. Eduardo David, Jr. Erika Legara, Ph.D.

Units: 3

Machine Learning (4u)

Machine Learning

At the end of the course, students will be able to:

Learn and appreciate the processes involved in machine learning from various datasets.
Acquire thorough knowledge of the different learning algorithms and how to apply them.
Select appropriate machine learning models to various problems including, but not limited to, classification, pattern recognition, and optimization.
Implement successful machine learning algorithms for various tasks.
Obtain skills in evaluating the performance of machine learning algorithms.
Know the limitations of machine learning models.
Acquire skills at presenting and accurately communicating results obtained from machine learning models.

Christopher Monterola, Ph.D. Erika Legara, Ph.D.

Units: 4

Management Communication (1u)

Managerial Accounting (1u)

Managerial Accounting

At the end of the course, students will be able to:

Illustrate the concept of the triple bottom line – people, planet and profit.
Analyze business transactions and understand their impact on the three basic financial statements.
Use accounting information for better decision making.
Understand the behavior of cost.
Prepare and explain the role of proforma financial statements in the planning process.
Explain the relationship of costs with volume and profit and the significance of using break-even analysis.

Felipe O. Calderon, CPA, CMA, PhD

Units: 1

Digital Marketing and Analytics I (1u)

Digital Marketing and Analytics I

At the end of the course, students will be able to:

Explain the concepts and processes underlying digital advertising.
Make use of “search” as a strategic channel in digital marketing.
Distinguish how marketers use social media platforms for branding and marketing strategy implementation.
Determine how marketers develop and implement viral marketing campaigns.
Understand and explain the role of data in digital marketing"

Babak Hayati, Ph.D.

Units: 1

Economics for Business

At the end of the course, students will be able to:

Explain the basic principles of economics.
Discuss the role of theory in analyzing complex economic relationships.
Explain market forces through the supply and demand framework.
Identify externalities.
Differentiate competing theories on current macroeconomic issues, economic development and economic policy.
Explain how macroeconomic issues affect business and private enterprise.
Apply economic concepts in formulating strategies for maximizing profits in business

Jamil Paolo Francisco, Ph.D.

Units: 0.5

Econometrics

At the end of the course, students will be able to:

Understand how economic theory informs variable selection in model specification.
Identify sources of microeconomic and macroeconomic data sets.
Communicate findings to stakeholders using their understanding of the basic concepts of econometrics such as hypothesis testing, variable selection, model specification, data collection, estimation techniques, and interpretation of parameter estimation results.
Apply basic econometric tools and techniques using microeconomic and macroeconomic data through learning team projects, and present the results and analysis during class discussions and written reports

Wilfred Manuela, Jr., Ph.D.

Units: 0.5

Third Term

Big Data & Cloud Computing (3u)

Big Data & Cloud Computing

At the end of the course, students will:

Learn the concepts and challenges of big data
Understand the impact of Big Data analytics in strategies and decision-making processes in various sectors
Obtain know-how in determining appropriate big data science tools/methods for analyses, including the knowledge of existing and cutting-edge data science tools and software
Acquire thorough working knowledge of data mining/collection, management, and analytics
Obtain experience in implementing and deploying software/tools to manage and analyze big data
Gain skills in presenting results obtain from big data analytics

Christian Alis, Ph.D. Eduardo David, Jr.

Units: 3

Deep Learning (4u)

Deep Learning

At the end of the course, students will:

Obtain sufficient theoretical knowledge of neural networks to be able to describe the mechanisms behind deep learning algorithms and their limitations
Learn how to build datasets used to train neural networks
Be familiar with the differences between algorithms and models for supervised and unsupervised learning
Design single and multi-layer neural networks
Analyze the performance of deep learning algorithms on various machine learning tasks
Gain know-how in selecting appropriate neural network/deep learning models to various problems including, but not limited to, classification, pattern recognition, and optimization
Learn how to correctly present results from neural network and deep learning models

Christopher Monterola, Ph.D. Christian Alis, Ph.D. Erika Legara, Ph.D.

Units: 4

Data Science Elective

Digital Marketing and Analytics II (1u)

Finance (2u)

Project Management (1u)

Managing for Sustainable Development Impact (1u)

Managing for Sustainable Development Impact

The course will focus on integrated and results-oriented approaches that encourage participatory development and stakeholder. It will also emphasize strategy formulation, and the analysis, evaluation, implementation, and management of sustainable development management (SDM) strategies, policies, financial activities, and projects that aim to contribute towards Sustainable Development Goals.

Kenneth Hartigan-Go, M.D. (with various lecturers)

Units: 1

Fourth Term

Network Science (2u)

Network Science

The module will cover the basics of Complexity Science with particular focus on Complex Networks (network science), which are the backbones of complex systems (e.g. cities, organizations, economies, and financial markets). Complex networks quantify the interactions of various entities/players in complex systems. Examples of complex networks include social networks like those generated from Twitter, Facebook, and Instagram, financial networks, biological networks, and organizational networks. Students will learn how to visualize, analyze, and model complex networks using Python, NetworkX, and Gephi. At the end of the course, students should be able to view and analyze problems in business and marketing, among others, through the lens of complexity science. They should also be able to argue, in descriptive and quantitative manner, why a system-of-systems thinking is necessary to address most real-world issues.

Erika Fille Legara, Ph.D.

Units: 2

Data Engineering (2u)

Data Engineering

The module will cover the basics of Data Engineering including but not limited to:

Designing, building and maintaining data pipeline systems
Data preparation and wrangling
Analytical data storage
Cloud infrastructure

Christian Alis, Ph.D.

Units: 2

Data Science Elective

Capstone Project

Innovation (1u)

Negotiating Change (1u)

Operations (1u)

Strategic Management (1u)

Strategic Management

The module will help managers in coordinating with multiple functional areas in order perform their responsibilities, or contributing to organization level management decisions. The primary intent of the strategic management courses is to develop in the students an integrative, organization level point of view for understanding what it takes for a business enterprise to be successful. As the perspective is organization-wide, the strategic management courses take a systemic, integrative and multi-functional perspective; helping the student understand the interactions of business external context, market dynamics, competitive dynamics, and internal situation and decision-making in influencing business outcomes. The thinking behind the strategic management courses is that managers, especially senior executives, must contribute or add value not only by managing a function such as marketing or finance, but also by incorporating strategic frameworks to develop a strategy for his or her unit taking into consideration the enterprise strategy, and the overall objectives and needs of the entire organization. A complementary or supporting objective of the strategic management courses is to develop the “strategizing and executing” skills of the student-managers.

Maria Elena B. Herrera, Ph.D.

Units: 1

Faculty and Mentors

Core Faculty

Data Science

Chris Monterola, Ph.D

Data Strategy, Mathematics for Data Science, Applied Computational Statistics, Machine Learning, Deep Learning

Professor Analytics, Information, and Operations Department School Head School of Innovation, Technology, and Entrpreneurship Executive Managing Director | Senior Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Erika Fille Legara, Ph.D.

Data Strategy, Data Visualization and Storytelling, Network Science, Machine Learning, Data Mining & Wrangling

Associate Professor Analytics, Information, and Operations Department Academic Program Director Master of Science in Data Science Deputy Director | Senior Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Christian Alis, Ph.D.

Programming for Data Science, Data Mining & Wrangling, Big Data Analytics and Cloud Computing

Assistant Professor Analytics, Information, and Operations Department Senior Data Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Business, Management, Leadership, and Communication

Ricardo A. Lim, Ph.D.

Design Thinking

Chair | Professor Department of Analytics, Information, and Operations

Jose Gerardo O. Santamaria, Ph.D.

Human Behavior in Organizations

Chair | Associate Professor Department of Leadership and People Management

Jamil Paolo S. Francisco, Ph.D.

Business Economics

Associate Dean Asian Institute of Management Associate Professor Department of Finance, Accounting and Economics Head Research and Publications Executive Managing Director Rizalino S. Navarro Policy Center for Competitiveness

Felipe Calderon, CPA, CMA, Ph.D.

Managerial Accounting

Assistant Professor Department of Finance, Accounting and Economics School Head Washington SyCip Graduate School of Business

David Gulliver G. Go, Ph.D.

Finance

Co-chair | Associate Professor Department of Finance, Accounting and Economics

Wilfred S. Manuela, Jr., Ph.D.

Econometrics

Associate Professor Department of Finance, Accounting and Economics

Ma. Nieves R. Confesor

Negotiating Change

Associate Professor Department of Leadership and People Management

Babak Hayati, Ph.D.

Marketing

Chair | Associate Professor Department of Marketing

Kenneth Y. Hartigan-Go, MD, MD (UK)

Managing for Sustainable Development Impact

Associate Professor Department of Strategic Management Head Stephen Zuellig School of Development Management

Maria Elena B. Herrera, FASP, Ph.D.

Strategic Management

Professor Department of Strategic Management Academic Program Director Master in Entrepreneurship

Adjunct Faculty

Ed David, Jr.

Programming for Data Science, Data Mining & Wrangling, Big Data Analytics and Cloud Computing

Senior Data Engineer Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Felix Valenzuela, Ph.D.

Applied Computational Statistics

Research Scientist A*STAR

Rachel Juat-De Leon

Business Analytics (Marketing Analytics)

Head of Analytics Projects QBE Insurance

Guido David, Ph.D.

Applied Computational Statistics

Associate Professor Institute of Mathematics, UP Diliman Coordinator Computational Science Research Center, UP Diliman

Guest Lecturers

Stephanie Sy

Data Strategy, Data Science Applications

Founder and Data Scientist Thinking Machines

Jean-Francis Darre

Data Science Applications

Chief Analytics and Risk Officer Mynt

Erik Wetter, Ph.D.

Data Science in Public Policy

Co-founder and Chairman Flowminder

Mayeth Condicion

Data Science Applications

Co-founder and CDO/COO Snapcart

Atty. Raul Cortez

Data Privacy

Legal and Corporate Affairs Director Microsoft Philippines

Gillian Ann Stevens, Ph.D.

Human Behavior in Organizations

Associate Professor Department of Leadership and People Management

Data Science Mentors

Chris Monterola, Ph.D.

Executive Managing Director | Senior Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Erika Fille Legara, Ph.D.

Deputy Director | Senior Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Christian Alis, Ph.D.

Senior Data Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Ed David, Jr.

Senior Data Engineer Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Antonino Paguirigan, Jr., Ph.D.

Data Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Jill Cabatbat, Ph.D.

Data Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)

Gino Borja

Data Scientist Analytics, Computing, and Complex Systems (ACCeSs@AIM)