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

North Terrace Campus - Semester 2 - 2020

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 basic statistical methods and data analysis techniques relevant for big data approaches, which may include regression analysis, causal inference, and quasi-experimental methods.

  • 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 Course Planner.

  • 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.

    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)
    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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    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
    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
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course uses a blended teaching approach.


    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 Australian 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 Australian 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 University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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 obtained
    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 Access Adelaide.

  • 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 ( 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
  • Policies & Guidelines
  • 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’s disciplinary procedures.

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