Business Analytics Thinking in

Data Science

June 30, 2016, 9am to 5pm

Data science – the up and coming emergent field of expertise and competency touted to be the next big thing that will give companies their competitive advantage and open the space for their next level growth. It is spreading and getting adopted very quickly by business organizations in the developed nations and it is fast creeping into the realm of the developing economies as well. This is not a surprise with the continuous expansion of the internet with the ongoing explosion and evolution of digital and computational technology leading to the generation of big data and the capability to store and process these.

This is a 1-day learning session on data science but with particular focus on its key component– analytics! Data science is too large a topic to tackle for a day but business and data analytics plays a core role. Aside from introducing the participants to the data science methodology, the session will familiarize them with the major categories of analytics under which the gamut of tools, techniques and algorithm will fall. Note that aside from a brief and bird’s eye view, it is not the intent of the session to run through and let participants be a master of each and every available tool and algorithm.  Select tools and algorithm might be picked and be given a bit more attention in so far as these will help in the understanding of the purpose and use of each type of analytics which will be linked to the business problem or issue that it can address. In fact the main objective underlying this session is the imparting of fundamental concepts and principles for the mind in doing business data analytics that should guide the crafting of data science solutions for business problems and issues. The main value of data science after all is how it can make possible, enable and improve a business’ data-driven decision making. If data mining and analytics doesn’t contribute to this, it will just be a waste of effort and investment. The design to veer away from algorithms and data mining technology is deliberate. In the still fluid and developing arena of data science these will pass, evolve and be replaced by new ones but the core principles and concepts of how business analytics can be tagged and relevant to business  decision making will remain.


This session will consists of case exercises and discussions where these will serve as a channel for the participants to learn and put into practice the concepts and principles involved and also give them a view of how it could apply to the real-work settings in their organizations. The session will be strictly limited to 18 participants only. This is to ensure a high engagement level and interaction between the Mentors/Facilitators and the participants.

Learning Session Outline

How you will benefit


  • Familiarize yourself with the new and emerging field of data science and analytics and be not left behind.

  • Realize, comprehend and see opportunities on what you can do with data and gain a competitive edge out of it. [Many organizations gather data but do not know what to do with it.]

  • Gain inputs and ideas how data science and analytics can be applied, used, deployed and eventually capitalized on by your business.

  • Understand the major categories of analytics, what each of them are for and how they can be linked and tied together for a more potent data driven decision-making capability.

  • Visualize the next possible steps into your organization’s journey of institutionalizing the data science and analytics function and practice. 



Who Should Attend


Business executives, managers, business function (ie marketing, supply chain, retailing, etc.) analyst, business planners, data analyst and specialist or any company personnel assigned to doing, exploring or laying down the foundations for an institutionalized data-driven business decision-making process and function.


It is suggested that participants bring their own laptops for the case exercises and showcase of some data analytics software.  Installation instructions for the trial version of the analytics software will be provided on the seminar dates.


Training Venue


The training site will be at Technopoly Inc’s Training Hub located at 2901 One San Miguel Avenue Building, 1 San Miguel Avenue corner Shaw Blvd, Pasig City. Those coming from faraway places could explore the multiple accommodation options near the training venue such as Red Planet Hotel, Richmond Hotel, Lancaster Hotel, etc.


To know more about our facility please Click here














Learning Investment and Inclusions


This one-day learning session is worth Php 7,280.00 (VAT included).


The inclusions are:

  • Workbook Materials

  • Business Snacks and Lunch, Unlimited espresso coffee and tea

  • Certificate of Completion


Paid parking is available in One San Miguel Avenue Building with entrance at the Shaw Boulevard



For Course-Related Questions


To reserve for this learning session or should you have any questions, please feel free to contact us via email through with your name and contact details


Payment Process


To secure your slot please pay at least 1 week before the learning session. Please deposit payment to:


Bank of the Philippine Islands

Account Name: Technopoly Inc.

Account Number: 1413-0041-59

Branch: Taft-Quirino Avenue


Banco de Oro

Account Name: Technopoly Inc.

Account Number: 004580-3638-01

Branch: Taft-Vito Cruz


Please email a copy of your deposit slip to this will serve as your confirmation. 



Our Facilitators and Mentors

Dennis T. Beng Hui

Dennis has been coaching and mentoring business leaders in their Lean implementation for the last 15 years. He is a frequent speaker in the Philippine Lean Six Sigma Conference. Aside from the practice of Lean, he is currently a faculty at the Department of Industrial Engineering at the Gokongwei College of Engineering of De La Salle University Manila since 1991. He has helped trained and coached lean projects in manufacturing, health care, retail and food service. He has a bachelor in science and masters in science degree in Industrial Engineering. He is currently completing his doctor of philosophy in Industrial engineering.



Bryan Gobaco

Bryan has trained various companies on Statistical Process Control. These companies are in the field of consumer goods and personal care, agri-business, specialty chemicals, plastics and packaging, electronics, food ingredients to name some. He is also the resident trainer of the Philippine Trade Training Center on the use and application of Statistical Process Control for the last 10 years. He is Black Belt on Six Sigma and is also currently a faculty at the Department of Industrial Engineering at the Gokongwei College of Engineering of De La Salle University Manila where he also earned his bachelor in science and masters in science degree in Industrial Engineering.



Emil Adrian V. Fernandez

Emil Adrian Fernandez is currently a part-time faculty at the Department of Industrial Engineering at the Gokongwei College of Engineering of De La Salle University Manila and holds a Bachelor and Masters of Science degree in Industrial Engineering from the same university. He has helped trained and assisted lean project implementation in manufacturing, health care, retail, education, agriculture and food service for the last 8 years. He is also the resident speaker for Lean Training, Kaizen and Six Sigma at the Philippine Trade Training Center under the Department of Trade and Industry.

Lean Manufacturing is a term coined by James Womack that basically means “waste elimination” that leads to creating more value for the customer. The fundamental principle of Lean is the continuous recognition and elimination of waste in operations and reducing time from order to delivery while maintaining or improving product quality. Although lean was first originated its concepts and principles in manufacturing systems its use and principles has been widely recognized by several companies in service sectors both from local and abroad as an essential approach in streamlining processes.