SPARTA

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: October 12, 2024, 11:59 PM
• Start of peer-graded assessment: October 13, 2024, 00:01 AM
• 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 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: November 2, 2024, 11:59 PM
  
• Start of peer assessment: November 3, 2024, 12:01 AM
• Closing of peer assessment: November 5, 2024, 11:59 PM
• End of course: November 9, 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: October 5, 2024, 11:59 PM
• Start of peer assessment: October 6, 2024, 12:00 AM
• Closing of peer assessment: October 8, 2024, 11:59 PM
• End of course: October 12, 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: October 19, 2024, 11:59 PM
• Start of peer assessment: October 20, 2024, 12:01 AM
• Closing of peer assessment: October 22, 2024, 11:59 PM
• End of course: October 26, 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.

For public sector organizations to control labor costs and improve productivity, support programs and services, and use budgets more efficiently, they need to significantly improve how they recruit, train, and manage talent. However, labor and workforce data collected by national and local governments often get trapped in siloed, disparate systems — making it difficult to leverage for making better human resource management (HRM) decisions. In this course, participants will gain a holistic understanding of how public HRM and data analytics intersect. Participants will learn how data value chains should be set up in organizations in order to execute data-driven strategies. Topics to be covered also include HR measurement framework and techniques using data and sophisticated analysis on workforce-related concerns and issues.

Policy analysts and managers should be able to analyze various issues logically and systematically and be equipped with tools and skills to support such scientific reasoning. In this course, participants will learn how to identify and analyze policy problems correctly, formulate policy problems properly, and interpret results professionally. Mathematical and statistical methods that are frequently used to solve policy and management problems will also be introduced. This course will cover the practical uses of management science, operations research, econometrics, and data storytelling in policy analytics, modeling, and simulation. 

Take note of the following end dates for this course on November 4, 2023, 11:59 PM

Effective data science and analytics project management ensure that the organization, big or small, will be able to reap the benefits of a well-planned and properly managed project, resulting in satisfied stakeholders and achievement of business objectives. Participants of the course gain knowledge on project management processes, the efforts required for each process group, and best practices to successfully implement data science and analytics projects. Through exercises and use case discussions, participants move toward understanding the concepts, to practical applications.

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

Every business in the industry is generating loads of financial data. It is important to derive insights out of them to inform and optimize decision-making. This course combines statistical tools with machine learning tools and algorithms, to equip the participants with the requisite skill set in analyzing data to achieve better financial results. Practical use cases, such as financial performance analysis, forecasting, anomaly detection, and loss and default modeling, will be discussed.

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

Tackle real-life challenges in business operations by applying analytical and modeling techniques. Practical business cases will allow participants to explore the spectrum of data analytics skills to provide insight for making operational decisions, primarily to improve service delivery to customers and become more efficient and effective in resource allocation. This course will also tackle use cases such as quality control, capacity planning, and failure and fault modeling.

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

To create relevant and meaningful data-driven solutions for the country, this course intends to introduce a human-centered approach in solving problems, that is, Design Thinking. Using the available online materials, discussion boards, and webinars, the course offers learners a detailed discussion of the Design Thinking framework which is composed of modes, methods, and mindsets. The course zooms in on the Empathize and Define modes for learners to effectively scope the needs of the intended audience for their Analytics projects and to properly frame the right problem to solve for the same intended audience. The course also details the mindsets that learners need to develop to conduct Design Thinking. The ultimate goal is for the learners to effectively scope and frame Analytics projects that are not just data-driven but also human-centered.

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

Deep Learning is a more advanced implementation of machine learning. By having knowledge in deep learning, data scientists may accomplish complex machine learning tasks including artificial intelligence that may provide deep levels of perceptual recognition. This course equips participants with practical knowledge in Deep Learning using Python. The course covers widely used Python libraries useful in efficiently performing Deep Learning, together with various scenarios and algorithms that go along with it."

Take note that this course will end on November 30, 2024, at 11:59 PM.

Data science and machine learning are achieved through complex algorithms and techniques. It generates various insights that can be used for several business purposes. Python, being a programming language known to have simple syntax and to integrate well in environments of production systems of organizations, is a powerful tool in performing data science and machine learning. This course provides immediately applicable skills in using Python to implement data science and machine learning algorithms. The focus is on the application of the most actionable analytical techniques in addressing the most common business problems.

Take note that the course will end on November 10, 2024, 11:59 PM. 

SQL is a dominant data analysis language since data are usually available in a structured, database format. Most analysis involve a lot of filtering, grouping, sorting, and aggregation, for which SQL is quite handy to use. Python, on the other hand, has well-known libraries, specially designed for data analysis and statistical modeling. This course teaches how to perform descriptive, diagnostic, and predictive analytics using functions, procedures, and best practices in both SQL and Python. Combining the use of SQL to retrieve and process the essential data for analysis, together with the use of specialized Python libraries for more complex data manipulation, analysis, and modeling is also discussed.

Take note that this course will end on November 10, 2024, 11:59 PM

Statistics transforms data into meaningful information, enabling organizations to make better decisions and predictions. Hence, it is a valuable skill to learn in business or academia. This course will teach participants how to perform the most important and commonly used analytics and modeling techniques in Excel. Topics covered include descriptive, diagnostic, predictive, and prescriptive analytics. Participants will be trained using a case-based approach with either real or simulated data sets, to solidify their knowledge so they can apply the techniques that they have learned to their own work.

Take note that this course will end on November 10, 2024, at 11:59 PM.