class: center, middle, inverse, title-slide .title[ # PADP 7120 Data Applications in PA ] .subtitle[ ## Intro & Overview ] .author[ ### Alex Combs ] .institute[ ### UGA | SPIA | PADP ] .date[ ### Last updated: January 07, 2025 ] --- # Outline 1. Why this course? 2. Course objectives 3. Why R? 4. Topics 5. Course materials 6. Assignments 7. Other course policies --- # Why this course - Data is the raw material of knowledge - Statistics is the most powerful tool we have to convert raw information into something useful --- # MPA Objectives [Network of Schools of Public Policy, Affairs, and Administration](https://www.naspaa.org) promotes five core competencies -- 1. To Lead and Manage in Public Governance 2. To Participate in the Public Policy Process 3. **To analyze, synthesize, think critically, solve problems and make decisions** 4. To articulate and apply a public service perspective 5. To Communicate with a Diverse Workforce and Citizenry --- # Why R - Adams, et al. (2013) Statistical Software for Curriculum and Careers. Journal of Public Affairs Education. - 80% of MPA schools use stats software other than Excel - 30% of public sector jobs asked for (at least) familiarity with stats software other than Excel - Reproducibility and Automation - R is popular and free - Encourages deeper thinking about data and statistics than point-and-click --- # Why learn R  <br> - Both sides need to communicate - We are constantly consuming statistics or information drawn from a statistical analysis - Producing helps you be a more knowledgeable consumer --- # Topics .pull-left[ **Description** - Data - Measurement - Descriptive measures - Visualization - Regression ] -- .pull-right[ **Inference** - Causality - Sampling - Hypothesis tests - Significance - Assumptions **Bonus?** - Forecasting ] --- # Complementary Data App Skills - Workflow - Import data - Export data and reports - Preparing data - Visualizations - Automating tables --- # Balancing Concepts and R - How to use R is initial focus - Statistical concepts relatively simple at first, then complexity increases around Week 6 with regression - Struggles with R, especially the first few weeks, is part of the process. Try not to feel discouraged. --- # Course Materials & Assignments - All materials are available on eLC - 8 R Chapters (10% of grade) - 9 R Labs (10% of grade) - 3 Problem sets (40% of grade) - 2 Exams (40% of grade) - 15 DataCamp Chapters for up to 4.0 points of extra credit --- # R Chapters - Provide regular practice using R - Due before the class each chapter corresponds to on the course schedule. - Graded complete/incomplete conditional on good faith effort - Answers available on eLC once you upload your answers - You can compare your work to the answers. Send questions via the eLC comments or bring to class. --- # R Labs - Supervised R practice - Graded complete/incomplete if you work on R Lab in class - Missing one R Lab is automatically excused. - Missing additional R Labs will require a valid excuse for the opportunity to avoid a grade deduction. - If excused, late submissions will be graded based on accuracy and completeness. - Answers will be posted by Friday each week. Availability will be conditional on submitting your R Lab to eLC. --- # Problem sets - 3 problem sets - Combination of concepts (readings and lectures) and applied skills using R (R chapters and labs) - Each week will cover concepts and skills that apply to the current or next problem set - I will let you know when we have covered everything that applies to the current problem set - We will cover everything applicable to the current problem set at least a week before it is due --- # Working on problem sets - Designed to be worked on some each week, not all at once - Questions do not necessarily progress in the same order as our weekly topics. - If you reach a question we haven't covered, there may be questions that follow it that we have covered. - Review the problem set after each class and make note of which questions you should be able to answer. --- # Working on problem sets: Groups - Due to class size, I encourage you to form a group to work on problem sets - Maximum size for groups is three - Groups are encouraged to work together simultaneously rather than independently on separate parts of the problem set. - Let me know if your schedule or circumstances make group work unfeasible and need to work on problem sets individually. --- # Problem set groups - If you have established a group on your own, someone email me the names of the members by our next class meeting. - All remaining students who have not requested to work individually will be grouped together or added to an existing group. - Groups will be added to eLC. - Group members are expected to contribute in a way that satisfies all other members of the group. Please reach out to me if difficulties arise with group dynamics. --- # Exams - Midterm and final administered via eLC - Focus on concepts and interpretation of statistical analysis - Exams do not require you to use R --- # DataCamp Chapters - Optional - Recommended order provided on course schedule - Opportunity for extra credit closes after April 4th - Invite link on eLC. Use your uga.edu email address to join - Be sure the chapters you complete are the ones that count toward extra credit --- # Attendance & Office Hours - Attendance not required; no explanation needed unless pertains to missing an assignment deadline - Link to schedule office hours is on eLC.