This is a sample course used for the demonstration of Linang's features. 

There's a revolution happening around us and all over the world. Smart, connected machines, or robotics and autonomous systems (RAS), are acting as tools to support us, working alongside us or alone, making independent decisions and even learning. They act and sense in the real world, connected and collaborating in the internet of things, generating and enabled by large quantities of data, using artificial intelligence to reason, classify, control and interact. They have emerged from research prototypes into practical applications. Autonomous and semiautonomous cars on our streets are one very public example. Other RAS include manufacturing systems that can personalise bespoke designs and reconfigure during normal operations; robotic fulfilment centres that assemble, package and dispatch goods ordered online; drones that deliver packages, or map, inspect and repair in our offshore oilfields and nuclear facilities; assistive exoskeletons to help us move and lift; and interactive companions for the elderly and isolated. 

In the same way the information and communications technology (ICT) revolution affected everything that uses data, the RAS revolution is changing everything that moves. There are multiple ways robots and autonomous systems are being used to improve safety. As well as removing people from hazardous situations, they may be an integral part of a system in partial control such as an aircraft autopilot. As tools that physically collaborate with people, they can act as assistants to prevent injury, for example as a body exoskeleton during lifting. They can perform inspection of assets such as structures or pressure vessels more frequently, with greater access, more sensors and less down time than people, leading to earlier defect detection and greater reliability.

This self-directed capstone course provides an opportunity for learners under the Analytics Manager pathway to integrate and apply the analytics skills and knowledge learned in their previous courses to real-world problems.

This capstone intends to create a Lean Canvas for data-driven innovation or process improvement initiatives of your current or previous organization.

Learners are required to put together ideas and strategies into one template/document that will translate analytical results into actionable business items.

At the end of the course, learners are expected to submit simple documentation and Lean Canvas that serves as a blueprint of the analytics implementation.

Take note of the following end dates for this course:

• Deadline for submissions: October 26, 2024, 11:59 PM
• Start of peer-graded assessment: October 27, 2024, 11:59
• Closing of peer assessment: October 29, 2024, 11:59 PM
• End of course: November 4, 2024, 11:59 PM

This self-directed capstone course provides an opportunity for learners under the Data Steward pathway to integrate and apply the analytics skills and knowledge learned in their previous courses to real-world problems.

This capstone intends to develop a comprehensive metadata of the Sustainable Development Goal (SDG) Database that may serve as an inventory of available data and as a guide for data enhancement.

At the end of the course, learners are expected to submit simple documentation and comprehensive metadata that presents the inventory of SDG data in the chosen focus areas.

Take note of the following end dates for this course:

• Deadline for submissions: August 17, 2024, 11:59 PM
• Start of peer-graded assessment: August 18, 2024, 00:01 AM
• Closing of peer assessment: August 20, 2024, 11:59 PM
• End of course: August 24, 2024, 11:59 PM

This self-directed capstone course provides an opportunity for learners under the Data Engineer pathway to integrate and apply the analytics skills and knowledge learned in their previous courses on real-world problems. Learners are required to create a relational database that will be used to recreate the dashboard for Sustainable Development Goals (SDG) indicators.

At the end of the course, learners are expected to submit:

• A Relational Database that organizes various datasets to recreate the dashboard for SDG indicators.
  
• An Entity Relationship Diagram (ERD) of the created Relational Database.
  
• A simple documentation.
  

Take note of the following end dates for this course:

• Deadline for submissions: October 12, 2024, 11:59 PM
  
• Start of peer assessment: October 13, 2024, 11:59 PM
• Closing of peer assessment: October 15, 2024, 11:59 PM
• End of course: October 19, 2024, 11:59 PM
  

This self-directed capstone course provides an opportunity for learners under the Data Associate pathway to integrate and apply the analytics skills and knowledge learned in their previous courses to real-world problems.

This capstone intends to create a Provincial Dashboard by utilizing publicly available data. Learners are required to create a dashboard that will serve as a snapshot of the chosen province's current profile, which may help local government units (LGUs) come up with data-driven solutions and resource allocation.

At the end of the course, learners are expected to submit simple documentation about a snapshot of the chosen province's current profile.

Take note of the following end dates for this course:

• Deadline for submissions: July 20, 2024, 11:59 PM
• Start of peer assessment: July 21, 2024, 12:00 AM
• Closing of peer assessment: July 23, 2024, 11:59 PM
• End of course: July 27, 2024, 11:59 PM

This self-directed capstone course provides an opportunity for learners under the Data Scientist pathway to integrate and apply the analytics skills and knowledge learned in their previous courses on real-world problems.

Learners are required to choose a specific local issue of their interest and use Philippine dataset(s) to propose a solution, to allow them to apply data collection, analysis, modeling, evaluation, and presentation skills. At the end of the course, learners are expected to submit:

• a presentation report, summarizing the problem statement, objectives, analyses, results, conclusions, and recommendation
• spreadsheets, scripts, or Jupyter notebooks with the analyses details

Take note of the following end dates for this course:

• Deadline for submissions: November 16, 2024, 11:59 PM
• Start of peer-graded assessment: November 17, 2024, 00:00 AM
• Closing of peer assessment: November 19, 2024, 11:59 PM
• End of course: November 23, 2024, 11:59 PM

This self-directed capstone course provides an opportunity for learners under the Data Analyst pathway to integrate and apply the analytics skills and knowledge learned in their previous courses on real-world problems.

This capstone intends to present the Philippine situation during the pandemic by utilizing publicly available data, which will be useful in providing context and analysis of such data.

Learners are required to choose at least one among the following focus areas: business impact, healthcare system, and education, and use, among other sources, local dataset(s) to propose a dashboard that will provide a snapshot of information about the situation/performance of the Philippines during the pandemic. 

At the end of the course, learners are expected to submit:

• A concept paper behind the rationale of the proposed dashboard
• A dashboard mock-up in Excel

Take note of the following end dates for this course:

• Deadline for submissions: September 21, 2024, 11:59 PM
• Start of peer assessment: September 22, 2024, 11:59 PM
• Closing of peer assessment: September 24, 2024, 11:59 PM
• End of course: September 28, 2024, 11:59 PM

Governance is an important aspect of smart cities, due to the importance of city administration and management, and collaboration between the various stakeholders to achieve the desired levels of development, growth, sustainability, and quality of life. This course aims to introduce the fundamentals of a smart city framework as an emerging trend in urban development and management and as a potential tool for enhancing good governance and public sector productivity. The course, likewise, presents samples of smart innovations and tackles the challenges and implications of pursuing smart city transformation and management. Participants will look at case studies of how successful smart cities are implementing smart governance.

This course aims to introduce you to the field of urban planning in the context of the fourth industrial revolution. It shall discuss key concepts in urban planning and their relevance to the salient features of this revolution. The approach is both theoretical and historical with greater emphasis on key manifestations of the fourth industrial revolution such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data among others. This course also emphasizes the opportunities and challenges of urban planning amid innovative and disruptive technologies.

Many governments are moving towards the use of information and communication technologies (ICT) to allow citizens to easily access information and services. Electronic governance (e-Governance) deals with all regulations and policies to manage and control public service delivery, information dissemination, and citizens' participation in government decision processes through ICT. With the iterative data capability and capacity build that goes with digitalization in the public sector, data can come from many disparate and siloed sources. A systematic application of data collection, data storage, database design and management, and information systems management needs to be considered. This course introduces you to e-governance, possible source systems, the databases that are necessary, and how to store, access, process, and manage them, in preparation for reporting, analytics, and modeling.

Take note that this course will end on November 4, 2024, 11:59 PM.

The government plays a vital role in the macroeconomy. Public Financial Management (PFM), which is one of its key responsibilities, is important in supporting macroeconomic stability, economic growth, and the achievement of the Sustainable Development Goals (SDG). This course teaches how to leverage financial data across different government units, departments, agencies, and revenue streams to accurately recognize revenue, forecast budget, monitor spending, ensure compliance, identify risks, opportunities, and wastes, and deliver against goals. Participants will learn the practical application of basic data, reporting, analytics, and modeling tools and techniques to address common PFM problems, using real-life examples and datasets.

Take note that this course will end on November 4, 2024, at 11:59 PM.