ECON 1013 - Using Big Data for Economic and Social Problems I
North Terrace Campus - Semester 2 - 2020
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General Course Information
Course Details
Course Code ECON 1013 Course Using Big Data for Economic and Social Problems I Coordinating Unit Economics Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact up to 3 hours per week Available for Study Abroad and Exchange Y Assessment Typically, active participation, group project, mid-term exam and final exam Course Staff
Course Coordinator: Dr Florian Ploeckl
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1. Recognize suitable economic models and concepts to address major contemporary economic and social issues.
2. Explain the relevance of causality in addressing policy questions.
3. Identify suitable and appropriate empirical and statistical analysis approaches.
4. Interpret and explain the application and outcomes of big data statistical techniques.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,3 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
2,3 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
2,4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
3,4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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Learning & Teaching Activities
Learning & Teaching Modes
The course uses a blended teaching approach.
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
On average beyond attending lectures and tutorials, students are expected to spend about 10 hours per week for reading, watching online material, preparing projects and studying. The time required may vary across students and topics.
Learning Activities Summary
Learning activities for weeks 1-7 and 9-12 consist of weekly class lectures and tutorials. Lectures require prior preparation with online material and utilize active participation components in a blended teaching approach. Tutorials focus on practical exercises in software-based data analysis and visualization. The course will provide an introduction to the software package Tableau.
The activity of week 8 focuses on practical examples through either a guest lecture or review / data source lecture that illustrates certain aspects of the preceding topics by introducing students to a range of relevant 最新糖心Vlogn data sources.
The following table provides a tentative overview about the economic topics and statistical methods covered in the weekly lectures and tutorials.
The schedule and topic selection are tentative and might be adjusted during the semester Week Topic Lecture Topic Statistical Methods 1-4 Equality of Opportunity Geography of mobility, Neighbourhood effects, Innovation, etc Correlation, Regression, Experiments, etc 5-6 Education Education and social mobility, Effects of schools and teachers Bayes Rule, Regression Discontinuity, etc 7 Racial Disparities Racial Disparities in Economic opportunities Dynamic Models 8 最新糖心Vlogn Data sources / Guest / Review lecture 9-10 Health Economics of Health Care, Improving Health Outcomes Hazard Models, Adverse Selection 11 Climate Change Impact of pollution, Mitigation policies Difference-in-Difference Externalities, etc 12 Tax Policy Taxation, Behavioural Economics Supply & Demand -
Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Task Type Due Weighting Learning Outcome 1 Participation Individual weekly 10% 1-4 2 Group Projects Group TBC 30% 3-4 3 Midterm Individual During Midterm period 20% 1-4 4 Quizzes Individual weekly 20% 1-4 5 Data Assignment Individual Week 12/13 20% 1-4 Assessment Detail
The assessment components are as follows:
1) Active participation is assessed during the weekly lectures through quizzes, polls and surveys.
2) Group projects involve explaining and analysing economic and social problems using data visualization and statistical methods.
3) The mid-semester test will be available online to be completed between Weeks 8 and 9. It will focus mostly on economic and social issues and practical data analysis, and will be using a combination of multiple choice and other format types, such short paragraph questions..
2) Weeklyquizzes are used to assess the engagement with course materials coveringeconomic and social questions, as well as causality and statistical methodology
5) The Final Data Assignment is a practical data analysis, interpretation and visualization project.
6) To gain a pass for this course, a mark of at least 50% overall needs to be obtainedSubmission
1) Submission of projects is to be done online through MyUni. Failure to submit an assignment on time will lead to a zero mark.
2) Extensions and alternative assessment conditions: It is your responsibility to contact the lecturer in the first 2 weeks of the semester to discuss extension or alternative assessment options. This applies to ALL students, included but not limited to those registered with the disability centre or the elite athletes program. Exceptional circumstances will be evaluated by your lecturer on a case-by-case basis and should be discussed whenever possible at least 48 hours before the due date
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
This is a new course, no prior feedback is available. -
Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
