“Advanced” research strategies are, by virtue of their name, often complicated and require full courses of their own. Just look at the range and specificity of courses available via online learning platforms, the volumes of tutorials on building complex machine learning models, or appropriately estimating causal effects. Too often, however, analysts (and their clients) can place huge emphasis on the apparent benefits of this complexity without pausing to consider a method’s appropriateness, or limitations inherent to the data itself that may scupper valid analysis.
This course is designed to focus on the fundamentals of advanced data analytics, and in doing so to draw attention to the various theoretical, inferential, and computational issues that ultimately constrain any quantitative project. Most importantly, this course will equip you with the skills necessary to know when and why certain procedures are useful, and how to ensure the validity and appropriateness of your analysis. Along the way, we will cover a range of exciting and cutting-edge techniques across several key areas of analysis, including causal inference, machine learning, and experimentation.
This course should refine your ability to conduct advanced analysis of data across a variety of contexts, and I hope it sparks further interest in the various methods you will encounter along the way. Of course, there is only so much material eight modules can cover and, where applicable, I have highlighted extra readings you may wish to pursue beyond the confines of this course.
This course is comprised of eight, self-guided modules, two problem sets, and a final test:
Problem Set 1:
Problem Set 2:
Final Test (multiple choice)
This course aims to improve your ability to analyze data by focusing on advanced topics in data science and statistics. By the end of this course, you should be able to:
1) Participants who register to take this course and pass the final examination with a score of 70% or higher will be eligible to receive the APO certificate. Please note that the final examination can be taken only once. Therefore, you decide when you choose to take the examination. Please note that the self-assessment quizzes are for your own evaluation and have no connection with the final examination results.
2) Participants who perform well in this course and receive the APO certificate will be given preference, on a merit basis, for selection to attend the follow-up face-to-face multicountry APO project, provided their nominations are received through the concerned NPOs and slots are available.
3) Notes 1 and 2 are applicable only to participants from APO member countries. Participants from nonmember countries are welcome to take the course for self-improvement, though they will neither get APO certificate nor an opportunity to attend the follow-up face-to-face multicountry APO projects.
Public-sector productivity is an important part of the economic performance of a country. Yet measuring productivity in the sector, especially of public services, is not a simple task. It requires an appropriate framework and robust calculations of various basic inputs. As the government’s function is not to maximize profits but people’s welfare, performance measures in the public sector must therefore be addressed differently. In the long run, productivity measures for the public sector are vital in understanding the success of governments in using their resources to improve living standards and community well-being, giving warning signs to take policy action to improve productivity performance, providing feedback on the effectiveness of productivity-related measures taken, alerting policymakers to adverse productivity consequences that may result from actions taken in other areas, etc.
While determining the productivity of a specific service has proved to be challenging, the APO must increasingly recognize the important role of the public sector and examine how it can produce results of higher value to society. Hence, it must continue efforts to equip member country governments to apply the theories, concepts, and tools to improve the motivation and skill levels of public officials, strengthen management systems, enhance performance in the changing environment, and better understand productivity within their public agencies. This course will therefore provide a fundamental understanding of public-sector productivity by introducing some basic measurement techniques and applying them using examples provided in the course.
The course will cover the following modules:
Module 1: Why does measurement of public-sector productivity matter?
Module 2: The basics of productivity measurement
Module 3: Outputs and outcomes in the public sector
Module 4: How to measure output
Module 5: How to measure input
Module 6: Formulating productivity measures
Module 7: Dealing with quality
Module 8: How to interpret productivity trends
Module study, additional study material for participants, short quizzes for self-assessment, and a final examination to qualify for the APO e-certificate for eligible participants.
In recent years, we have seen the increased use of basic data analytics and how they revolutionize and revitalize not only businesses but also the public sector. They have played a vital, transformational role in improving the quality of public-sector decision- and policy making, strengthening political accountability, and delivering reforms in terms of advancing public services, monitoring budgets, and cutting waste, among other ways to enhance efficiency and effectiveness. Governmental organizations can now operate in a more data-driven, information-led manner, which was not possible previously. At the same time, this development also involves challenges since the responsible, secure use of public data must be ensured.
Aligned with the transformation initiative of the APO, this course will introduce the background to why data analytics are important in public-sector organizations by examining key concepts and trends of the big data environment and their applicability. As a capacity-building effort, this course will show how public-sector organizations can apply data analytics, process the results, and make data-based decisions for better performance and increased productivity.
Module 1: Purpose and use of data analytics in the public sector
Module 2: Fundamentals of data analytics in the public sector: sampling and measurement
Module 3: Analysis and visualization of data I: descriptive statistics
Module 4: Analysis and visualization of data II: probability distributions
Module 5: Analysis and visualization of data III: linear regression and correlation
Module 6: Application of data analytics to the public sector problem solving_final_revision
(A minimum score of 70% on the final examination is required to qualify for the APO e-certificate.)