Overview
Semester: Fall 2022
Schedule: Mondays, 9:30 AM – 12:15 PM
Location: Eggers 040
Instructor Contact Information
Saba Siddiki, PhD
Associate Professor, Public Administration and International Affairs
Center for Policy Research, 426 Eggers Hall, Syracuse
T: 315-443-4589
E: ssiddiki@syr.edu
Office Hours: Mondays, 12:30–1:30 PM
Co-Instructor
Graham Ambrose
PhD Student, Public Administration and International Affairs
Center for Policy Research, 426 Eggers Hall, Syracuse
E: grambros@syr.edu
Office Hours: Fridays, 11:00 AM – 12:00 PM
Course Description, Objectives, and Competencies
Leading and managing in any organization to address public sector challenges has always required the ability to effectively synthesize, analyze, and critically evaluate data within complex and dynamic contexts. But today’s evolving data environment requires an augmented set of data competencies for leaders and managers. What are some of the features of this evolving data environment, and what sets of competencies do they prompt? In part, this evolving environment is one in which data previously deemed intractable can now be effectively collected, stored, and analyzed. It is also one in which computational techniques are increasingly used for data analysis and decision making based on different forms of data. Sometimes these techniques are built on decision heuristics that are fundamentally different than those engaged by humans. To effectively navigate this evolving data environment, leaders and managers must be able to:
- Demonstrate understanding of complexities involved in public sector decision making
- Convey understanding of evolving data trends
- Demonstrate understanding of computational thinking and perspectives that undergird computational tools and techniques increasingly used to inform public sector decision making
- Use, at least at a basic level, computer programming software increasingly relied on to perform data tasks
- Assess and communicate the qualities and biases associated with data used in policymaking and public organizations
- Use decision frameworks and heuristics for guiding choices about the appropriate use of different kinds of data and analytical techniques
- Reference and draw upon research and practice that addresses data driven decision making
To support students’ mastery of competencies across the data analytics life cycle, this course will:
- Highlight complexities of public sector decision making
- Introduce students to computational thinking
- Introduce students to basic programming in Python
- Introduce students to data management and visualization in R
- Introduce students to data management and visualization in Tableau
- Provide students with an overview of data fundamentals, including: types of data, data bias, data quality, data measurement, data management, and data communication
- Review cases and strategies relating to data driven decision making
Course Materials
There are two required texts for this course (text information below), one of which needs to be rented or purchased by the student, and one of which is freely available (i.e., in publicly available E-book format). In addition to the course texts listed below, students will read a mix of academic journal articles, government reports, professional reports, and web-based resources relating to topics covered in the course. These additional readings are posted on the course Blackboard site or referenced in this syllabus. Reading assignments are listed in the course schedule included in this syllabus.
Required Texts
- Kettl, Donald F. 2018. Little Bites of Big Data for Public Policy. Thousand Oaks, CA: CQ Press.
- Severance, Charles. 2016. Python for Everybody: Exploring Data in Python 3. Free E-book version available at: https://www.py4e.com/book
Class Meeting Format
Class sessions will be held in person in a lecture-lab format. This means each class period will be split into two parts. The first (lecture) part of each class will involve lecture and class discussion relating to overarching topics of data driven decision-making for public administrators. The second (lab) part of each class will consist of a data and programming lab, where Python, R, and Tableau tutorials will be provided to students, and in which students will have an opportunity to apply skills covered in tutorials directly. Please note that the course meeting format may change during the fall term per the instructor’s, PAIA’s, or University’s discretion, and in light of evolving circumstances relating to COVID-19.
Assignments and Exams
Students will earn points in this course based on their performance on data and computing assignments and a final project. Data and computing assignments and the final project are generally described below. Specific assignment and project instructions will be provided by the instructor during the course term.
Data and Computing Exercises (25 points each × 10 exercises = 250 points total)
Students will complete 10 data and computing assignments throughout the semester. The assignments will focus both on data concepts and skills covered in the lecture portion of the class and programming skills covered in the lab portion of the class. The data and computing assignments are designed to prepare students to complete the final project.
Final Project (100 points total)
The final project will provide an opportunity for students to apply various concepts and skills they have learned in the class. Students will be provided with datasets, and asked to work through multiple steps of the data analytics life cycle (i.e., getting data, preparing data, exploring data) culminating in the production of a 2-page policy memo presenting and discussing the data.
Grading
Students can earn a total of 350 points in this course. The percentage of points earned by students will be used to calculate their course grades. The following grade scale will be used: 94–100 = A; 90–93 = A-; 87–89 = B+; 84–86 = B; 80–83 = B-; 77–79 = C+; 74–76 = C; 70–73 = C-; Below C- = Failing.
University Guidance and Policies
Please review the University’s guidance and policies on select matters relating to student and classroom conduct on the course Blackboard page.
Course Schedule
| Date | Class Theme | Class Topics | Class Readings | Lab Topics | Lab Readings | Due |
|---|---|---|---|---|---|---|
| 8/29 | Introduction to Data Driven Decision Making | Describing Data Driven Decision Making in the Public Policy Process | None | None | None | None |
| 9/5 | No Class – Labor Day | |||||
| 9/12 | Data and Programming Basics | Data Analytics Life Cycle and Data Types | “Six Types of Data in Statistics and Research: Key in Data Science”: https://www.intellspot.com/data-types ; “What are Data Types and Why are they Important?”: https://dataled.academy/guides/data-types/ | Introduction to Programming: Understanding Programming and Basic Building Blocks | Severance, Ch. 1–2 Baker (2017) “Code Alert.” “Python for Social Scientists”: https://realpython.com/python-for-social-scientists/ | |
| 9/19 | Computational and Algorithmic Thinking | Computational Logic and Algorithmic Government | Engin and Treleaven. 2018. “Algorithmic Government: Automating Public Services and Supporting Civil Servants in Using Data Science Technologies.” Snow. 2019. “Decision Making in the Age of the Algorithm: Three Key Principles to Help Public Sector Organizations Make the Most of AI Tools.” | Conditional Execution and Functions | Severance, Ch. 3–4 | Data and Computing Exercise 1 |
| 9/26 | Different Data Forms | Big Data; Text as Data | Gandomi & Haider (2015) “Beyond the Hype: Big Data Concepts, Methods, and Analytics.”; Benoit (2019) “Text as Data: An Overview.” | Iteration | Severance, Ch. 5 | Data and Computing Exercise 2 |
| 10/3 | Data Quality and Biases | Assessing Data Quality and Bias | Sebastian-Coleman (2013) “Data Quality and Measurement.”; Crawford (2013) “The Hidden Biases in Big Data.” | Strings | Severance, Ch. 6 | Data and Computing Exercise 3 |
| 10/10 | Data Measurement | Conceptualization to Measurement | “Basic Concepts of Measurement.” https://www.oreilly.com/library/view/statistics-in-a/9780596510497/ch01.html | Files and Lists | Severance, Ch. 7–8 | Data and Computing Exercise 4 |
| 10/17 | Data Management | Data Management Basics | Sebastian-Coleman (2013) “Data Management, Models, and Metadata.”; Strasser et al. “Primer on Data Management: What You Always Wanted to Know.” | Dictionaries | Severance, Ch. 9 | Data and Computing Exercise 5 |
| 10/24 | Database Structure and Management | Introduction to Database Types | Torfs & Brauer (2014) “A (Very) Short Introduction to R.” | R basics and the tidyverse; Packages: dplyr, tidyr | tidyverse: https://www.tidyverse.org/packages/ ; dplyr: https://dplyr.tidyverse.org/ ; cheatsheet: https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf ; tidyr: https://tidyr.tidyverse.org/ ; cheatsheet: https://github.com/rstudio/cheatsheets/blob/main/tidyr.pdf | Data and Computing Exercise 6 |
| 10/31 | Data Visualization | Best Practices in Data Visualizations | Sinar, “Data Visualization.” | R Data Visualization | ggplot2: https://ggplot2.tidyverse.org/ ; cheatsheet: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf | Data and Computing Exercise 7 |
| 11/7 | Data Visualization | Types of Data Visualizations | Netquest, “A Comprehensive Guide to Data Visualization.” | Tableau Introduction | https://www.tableau.com/academic/students | Data and Computing Exercise 8 |
| 11/14 | Data Description | Writing about Data Effectively | Kettl, 1–5 | Tableau Data Visualization | Data and Computing Exercise 9 | |
| 11/21 | No Class – Thanksgiving Break | |||||
| 11/28 | Data Communication | Communicating Data Effectively | Cairney & Kwiatkowski (2017) “How to Communicate Effectively with Policymakers: Combine Insights from Psychology and Policy Studies.”; NCES (2011) “Best Practices Brief: Stakeholder Communication Tips from the States.” | Data and Computing Exercise 10 | ||
| 12/5 | Community Engagement in Data Analytics | Stakeholder Engagement in the Data Analytics Life Cycle | Woods (2019) “A Design Thinking Mindset for Data Science.” https://towardsdatascience.com/a-design-thinking-mindset-for-data-science-f94f1e27f90 ; Slunge et al. (2017) “Stakeholder Interaction in Research Processes – A Guide for Researchers and Research Groups.”; Pham et al. (2022) “The Role of Design Thinking in Big Data Innovations.” | |||
| 12/12 | No Class – Final Project | Final Project Assignment |
Course Content
Python for Everyone Chapters 1 and 2
Course overview, goals, and how data informs public decisions.
Python for Everyone Chapters 3 and 4
This lesson works through conditional execution using Boolean expressions and conditional operators as well as programing new functions in Python.
Python for Everyone Chapters 5
This lesson works through iterations using while functions and for loops in Python.
Python for Everyone Chapters 6
This lesson dives into manipulating string objects in Python.
Python for Everyone Chapters 7 and 8
This lesson teaches students how to upload files into Colab, but largely focuses on engaging with and manipulating text files, and list operations.
Python for Everyone Chapters 9
This lesson introduces the structure of and practical us of dictionaries.
Intro to R and the tidyverse
This lesson works as a transition from python to R. Furthermore, tibble, readr, dplyr, and tidyr are introduced as tools to engage and manipulate not just data but datasets.
Extending R to ggplot2
This lesson reviews the content covered in the last section and extends it to data visualization using ggplot2.
Data Visualization using R
This lesson was developed as an in class exercise structure so the class has to think through the data visualization process.
Download Full Syllabus
📄 PAI 724 – Fall 2022 Syllabus (PDF)
Last Updated: October 2025