Teaching Analytics: APEC List of Competencies

Teaching Analytics: An Instructor’s Guide to implementing the recommendations of Project DARE (Data Analytics Raising Employment).

APEC (Asia Pacific Economic Cooperation) hosted an event on May 4-5 bringing together over 50 analytics experts and visionaries from over 14 counties across the Asia-Pacific region to form an advisory group.

The APEC Project DARE (Data Analytics Raising Awareness) Advisory Group started with an agenda set to develop recommended “APEC Data Analytics Competencies.

There are still being finalized, but as of last week here is a high level of the competencies that we came up with:

Business and Organizational Skills

  1. Operational Analytics: Use data analytics and specialized business analytics (i.e. business intelligence) techniques for the investigation of all relevant data to derive insight in support of decision-making.
  2. Data Visualization and Presentation: Ability to create and communicate compelling and actionable insights from data using visualization and presentation tools and technologies.
  3. Data Management and Governance: Develop and implement data management strategies and governance, incorporating privacy, data security, polices and regulations, and ethical considerations.
  4. Domain Knowledge and Application: Apply domain-related knowledge and insights to effectively contextualize data, achieved by practical experience (e.g. apprenticeships) and exposure to emerging innovations.

Technical Skills

  1. Statistical Techniques: Apply statistical concepts and methodologies for data analysis.
  2. Computing: Apply information technology, computational thinking and utilize programming languages and software and hardware solutions design and development for data analysis.
  3. Data Analytics Methods and Algorithms: Capture, clean and inspect data. Evaluate and implement data analytics and machine learning methods and algorithms on the data to derive insights for decision making.
  4. Research Methods: Utilize the scientific and engineering methods to discover and create new knowledge and insights.
  5. Data Science Engineering Principles: Use software and system engineering principles and modern computer technologies, incorporating a data feedback loop, to research, design, and prototype data analytics applications. Develop structures, instruments, machines, experiments, processes, and systems to support the data lifecycle.

Workplace Skills

  1. 21st Century Skills: Exhibit crosscutting skills essential for DSA at all levels including, but not limited too; collaboration, customer focus, communication and storytelling, organizational awareness, critical thinking, planning and organizing, problem solving, decision making, business fundamentals, awareness of social and societal awareness, intelligibility, cross cultural awareness, dynamic (self) re-skilling, professional networking, ethical mindset and entrepreneurship.

Once this list is finalized I will update you all.

My end goal is two fold, (1) to help craft a version of the competencies into an academic discipline that the Analytics Council can present to CHED for adoption and (2) to design a vocational education track to address the basic skills needed for entry level DSA work here in the Philippines.

Stay Tuned!


Analytics Training – DMAIPH offers a wide range of analytics centric training solutions for professionals and students via public, in-house, on-site, and academic settings. We tailor each training event to meet the unique needs of the audience. If you need empowerment and skills enhancement to optimize the use of analytics in your organization, we are here to help. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.


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