ECON 1008 - Data Analytics

North Terrace Campus - Summer - 2019

In today's world, good decision making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data, and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers. By the end of this course, students should understand and know how to use statistics. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics. Students will work in small groups in this course; this will develop the skills required to work effectively and inclusively in groups, as in a real work environment. Typically, one component of the assessment requires students to work in teams and collect and analyse data in order to answer a real-world problem of their own choosing.

  • General Course Information
    Course Details
    Course Code ECON 1008
    Course Data Analytics
    Coordinating Unit School of Economics
    Term Summer
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week. Intensive in Summer Semester.
    Available for Study Abroad and Exchange Y
    Incompatible ECON 1008 Business and Economic Statistics I, ECON 1011, WINEMKTG 1015EX, STATS 1000, STATS 1005, STATS 1004, STATS 1504
    Restrictions Cannot be counted towards BCompSc, BCompGr, BMath,BMath Adv, BMathComp Sci or BEng(Software Engineering) students
    Quota A quota may apply
    Course Description In today's world, good decision making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data, and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers.

    By the end of this course, students should understand and know how to use statistics. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics. Students will work in small groups in this course; this will develop the skills required to work effectively and inclusively in groups, as in a real work environment. Typically, one component of the assessment requires students to work in teams and collect and analyse data in order to answer a real-world problem of their own choosing.
    Course Staff

    Course Coordinator: Ms Ye Han

    These are the Course Coordinators for this course for the three semesters of 2019. They will provide further contact details, such as office locations and office hours, at the start of the semester.

    Summer Semester
    Course Coordinator:  Dr Ye Han
    Office location:  Nexus 10, Room 4.22
    Contact details: Email ye.han@adelaide.edu.au

    Ye Han will be conducting both the lectures and tutorials for this course in Summer Semester.

    Semester 1 
    Course Coordinator: Prof Giulio Zanella
    Office Location: Nexus 10, Room 4.36
    Contact details: Email giulio.zanella@adelaide.edu.au 

    Semester 2
    Course Coordinator:  Dr Florian Ploeckl
    Office Location: Nexus 10, Room 4.43
    Contact details: Email florian.ploeckl@adelaide.edu.au
    Course Timetable

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

    Students in this course are expected to attend two 2-hour lectures each week starting in Week 1; and two 2-hour practical classes each week starting in Week 1.
  • Learning Outcomes
    Course Learning Outcomes

    On successful completion of this course, students will be able to:

    1. Apply correctly a variety of statistical techniques, both descriptive and inferential.
    2. Interpret, in plain language, the application and outcomes of statistical techniques.
    3. Interpret computer output and use it to solve problems.
    4. Recognize inappropriate use or interpretation of statistics in other courses, in the media and in life in general and comment critically on the appropriateness of this use of statistics.
    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)
    1-4
    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
    1-4
    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
    1-4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1-4
    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
    1-4
    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
    1-4
  • Learning Resources
    Required Resources
    Text book

    Selvanathan S, Selvanathan S and Keller G,  Business Statistics: Australia New Zealand Edition 7
    ISBN 9780170369466

    We will be using an online platform called APLIA to solve the weekly assignments. Buying the textbook is not mandatory but access to APLIA is required. More details regarding APLIA will be communicated through MyUni and in the first week of lectures.

    Calculator
    Students will need a calculator; a basic one that can take squares, square roots etc is sufficient.
    Recommended Resources
    The lecture slides, practical questions and other information will be available for students on Canvas and can be downloaded or printed from there.

    The lecture notes are NOT complete – they indicate what is to be covered in the lecture; you need to attend the lecture and write your own notes.

    In Semester 1, 2 and Summer Semester, it is intended that the lectures be recorded and a recording of each lecture put on Canvas for students who miss a lecture – but be aware that sometimes recordings fail. In that case, a note will be put on MyUni but the lecture may not always be re-recorded and students may need to make other arrangements, such as obtain notes from other students or read the book.

    NOTE: Dictionaries are not allowed in School of Economics exams

    Online Learning
    Extensive use is made of MyUni; please check the announcements regularly. Lecture notes, practical questions, and past exam paper solutions will be made available on MyUni. 

    There is a discussion board on MyUni; this is the preferred way for students to ask questions because this way all students have the same information and any of the staff can reply, allowing for quicker responses.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course uses lectures plus practicals. The lectures provide an overview of the course content but
    students can expect that they will need to study the textbook in order to understand the work. The practicals will comprise discussion, problem solving activities, individual and group work, student questions and student participation. These practicals provide the opportunity for students to practice; they are vital for success in this course. Often students think they can follow what we do in the lectures but when they come to do the work themselves, they realise it’s not that easy! Students are expected to have done the required reading before the practical and may ask questions about it during the practical.

    Help

    If you need some information or help, here is what you can do:
    • Ask your tutor in the practical
    • Post your query on the Discussion Board
    • Go to the Maths Learning Centre (Hub Central) They offer help with mathematics and statistics for students from all faculties.  They run a FREE drop in centre. Check times and location details on their web site, http://www.adelaide.edu.au/mathslearning/
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The workload for this course should consist of:

    Attend lectures 4 hours per week
    Attend tutorials 4 hours per week
    Study text book 4 hours per week
    Prepare tutorial answer 4 hours per week
    Learning Activities Summary

    The following timetable shows the topics to be covered each week and the associated chapter from the book. We will endeavour to keep to this but this may not be possible.

    Topic Reading Practical
    Week 1 Data and Sample Surveys Chapters 1 and 8 1
    Week 1 Describing Categorical Data
    Describing Quantitative Data
    Chapter 2
    Start Chapter 3
    2
    Week 2 Describing Quantitative Data
    Correlation & Regression
    Finish Chapter 3
    Start Chapter 4
    3
    Week 2 Correlation & Regression
    Randomness & Probability
    Finish Chapter 4
    Chapter 5
    4
    Week 3 Random Variables
    The Normal Model
    Chapter 7.1-7.3 5
    Week 3 Sampling Distributions
    Confidence Intervals for 1 proportion
    Chapter 9

    6
    Week 4 Confidence Intervals for 1 mean
    Hypothesis tests for 1 proportion and mean
    Chapter 11
    Chapter 10,11,12
    7
    Week 4 Hypothesis tests continued and tests of
    Independence
    Finish Chapter 12; part of
    Chapter 14.1-3,6
    8
    Week 5 Inference in Regression
    Residuals
    Chapter 15
    Chapter 16
    Test
    Week 5 Multiple Regression
    Dummy Variables
    Chapter 17
    Part of Chapter 18.1
    9
    Week 6 Time Series
    Index Numbers
    Chapter 19.1, 2, 3, 6
    Lecture Notes
    10
    Week 6 Revision 11
    Specific Course Requirements
    None.
    Small Group Discovery Experience
    In this course, students work in small teams and undertake a project investigating a real-world problem that involves statistics in the world around them.
  • 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 Due date/Week Weighting Length (Time)
    Homework Weekly 30% TBA
    Mid-Semester test Advised on MyUni 30% 2 hours
    Final Exam Summer exam period 40% 3 hours
     Total   100%  
    For due dates, see MyUni
    Assessment Related Requirements

     There are NO hurdle requirements.

    Assessment Detail

    Final exam (40%)

    • This is of 3 hours duration, plus 10 minutes reading time. 
    • Statistical tables are provided.
    • This exam covers the whole semester.
    • Please note that, following University policy, dictionaries are not allowed in School of Economics exams.
    • Graphics calculators are not allowed.
    • Legible hand-writing and the quality of English expression are integral parts of the assessment process. Marks will not be awarded in the final examination for answers that cannot be read.

    Individual Mid-Semester test (30%)

    • More details will be provided during the semester.
    • Medical and compassionate extensions:
      • Unfortunately, late attempts at the test are not possible, even for valid medical or compassionate reasons. Instead, the weight on the final exam will be increased to take the place of this assessment.

    Homework assignments (30%)

    • During the summer semester, there will be a weekly assignment for each week, so overall there are 6 weekly assignments.
    • The best 4 out of these 6 assignments will be counted for the assessment. Because not all of the 6 weekly assignments count for assessment, no special consideration will be given to students who miss a assignment for medical, compassionate or any other reason.

    Redemption

    There is no extra work that can be done to redeem individual components of assessment during the semester, no matter the reason.

    Assessment marks prior to the final exam may be displayed on the course website.  Students are encouraged to check their marks and notify the course coordinator of any discrepancies.

    Submission

    Midterm wriitten test can only be done during the student's Lecture on the date advised on MyUni.

    Weekly homeworks can only be submitted online, as instructed in tutorials and on myUni. No late assignments will be accepted.  

    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.

    The policy of the School of Economics is not to return final exam scripts to students. However, they are made available for students to read under the supervision of the Course Coordinator, at a time and place to be announced.

    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 (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.

    Many students have commented on the team-based approach to learning used in this course. Whilst many students said they enjoyed it and thought it helped them study (some even said they thought they'd only pass because their team was so helpful and contributed so much to their learning), many thought the allocation of marks was unfair in that free-riders could earn marks too easily. Also, anecdotally, we felt that students who received marks because their team did the work for them were at a disadvantage because they did not realise they couldn't do the work until the exam, which meant they failed. To rectify this, we have a system in place so that only team members that contribute to team work will be allocated marks. Furthermore, we have the individual written test component so that students can see how they understand the topics.
  • 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.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.