ECON 1013 - Using Big Data for Economic and Social Problems I

North Terrace Campus - Semester 2 - 2024

This course will show how "big data" can be used to understand and solve some of the most important social and economic problems of our time. The course will give students an introduction to important relevant economic concepts and frontier research in applied economics and social science related to policy making. Topics may include equality of opportunity, discrimination, education, health care, and climate change besides others. The course will also provide an introduction to data visualisation, data management, and basic statistical methods and data analysis techniques relevant for big data approaches, which may include regression analysis, causal inference, and quasi-experimental methods.

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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 assignment

Course Staff

Course Coordinator: Dr Florian Ploeckl

Course Timetable

The full timetable of all activities for this course can be accessed from .

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.

5. Analyze, visualize, and communicate economic and social data individually and in group settings.

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

University Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	1,3
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	2,3,4,5
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	2,4,5
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	2,3,4,5
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	1,2,5
Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.	1,2,5
Attribute 7: Digital capabilities Graduates are well prepared for living, learning and working in a digital society.	3,4,5
Attribute 8: Self-awareness and emotional intelligence Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.	2,4,5

Learning Resources

Recommended Resources

This course makes use of the materials of the “Big Data Course” by Opportunity Insight, available at:

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 the workshop and tutorials, students are expected to spend about 9 hours per week for reading, practicing, watching online material, preparing projects and studying. The time required may vary across students and topics.

Learning Activities Summary

Weekly learning activities consist of workshops, tutorials, and engagement with online materials. Workshops expect prior preparation with online material and utilize active participation components in a blended teaching approach. Tutorials focus on practical data exercises in software-based data analysis and visualization. This will be done by introducing students to the PowerBI software package.

The intention is to focus the activity of week 8 on real-world examples through either a guest lecture or case study seminar that illustrates real world aspects of the application.

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	Racial Disparities	Racial Disparities in Economic opportunities	Dynamic Models
6-7	Education	Education and social mobility, Effects of schools and teachers	Regression Discontinuity, etc
8	Application	Guest lecture / Case Study / etc
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

The University'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%	1-5
2	Data Project	Individual	TBC	30%	2,3,4,5
3	Midterm	Individual	Weeks 8-9	30%	1,2,3,4
4	Data Assignment	Individual	Week 12/13	30%	2,3,4,5

Assessment Detail

1) Participation
    a.)   Active Participation is assessed in the weekly seminar through participation in quizzes, polls and surveys.
    b.)    There will be a systematic replacement activity for students that cannot attend the seminar
    c.)    Overall marking will make use of a "best of" approach to take into account extenuating circumstances

2) Data Project
    a.)   This is an assignment where students are tasked to design, create, and explain a written and visual exploration of an empirical, data-oriented economic topic

3) Midterm Exam
    a.) This will be conducted online in the form of an exam quiz
    b.) A variety of question formats will be used, potentially including multiple answers and standard multiple choice questions.
     c.) It will be a timed quiz with students able to freely choose when to complete it during an extended period of time.

4) Data Assignment
   a.) This is a written assignment that may include practical data analysis and visualization tasks
   b.)   This is the final assignment of the course. If students are unable to complete it due to extenuating circumstances a case-by-case decision will be made with regard to a replacement through an additional assignment or reweighting of other assessment components.

An overall mark of 50% is needed to gain a pass.

Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process, and may affect marks. Marks cannot be awarded for answers that cannot be read or understood.

Submission

1) Submission of projects is to be done online through MyUni. Failure to submit an assignment on time will lead to a zero mark unless
a different arrangement is specifically stated on MyUni.

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 .

Student Feedback

The University 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 University 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.

Based on student feedback changes to the seminar structure, integration of online material and linkage between tutorials and other course elements have been made.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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