ECON 1008 - Data Analytics
North Terrace Campus - Semester 2 - 2019
General Course Information
Course Code ECON 1008 Course Data Analytics Coordinating Unit School of Economics Term Semester 2 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 Coordinator: Dr Florian PloecklThese 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.
Course Coordinator: Dr Ye Han
Office location: Nexus 10, Room 4.22
Contact details: Email email@example.com
Ye Han will be conducting both the lectures and tutorials for this course in Summer Semester.
Course Coordinator: Prof Giulio Zanella
Office Location: Nexus 10, Room 4.36
Contact details: Email firstname.lastname@example.org
Course Coordinator: Dr Florian Ploeckl
Office Location: Nexus 10, Room 4.43
Contact details: Email email@example.com
The full timetable of all activities for this course can be accessed from Course Planner.Students in this course are expected to attend two 1-hour lectures and one 1-hour practical (tutorial) class each week.
Lectures begin in Week 1. Practicals and ASSESSMENT in practicals (tutorial) begin in WEEK 2.
Course Learning Outcomes
On successful completion of this course, students will be able to:
- Apply correctly a variety of statistical techniques, both descriptive and inferential.
- Interpret, in plain language, the application and outcomes of statistical techniques.
- Interpret computer output and use it to solve problems.
- 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
Required ResourcesText book
Selvanathan S, Selvanathan S and Keller G, Business Statistics: Australia New Zealand Edition 7
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.
Students will need a calculator; a basic one that can take squares, square roots etc is sufficient.
Recommended ResourcesThe 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 LearningExtensive 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 ModesThis course uses lectures plus practicals (tutorials).
The lectures provide an overview of the course content and in most cases would provide sufficient material. Students are encouraged to consult the textbook in the instances where they feel they need more practice for specific topics.
Tutors will hold weekly tutorials where they will summarize past weeks lectures and help students with that week's assignments. All assignments will proceed with a lag, i.e. they will be based on the previous weeks lecture materials. Students are encouraged to attend the tutorials to stay on top of the course content
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 2 Hours per week Attend Tutorials 1 Hour per week Study Textbook 4 Hours per week Prepare Tutorial Answer 4 Hours per week
Learning Activities Summary
Teaching & Learning Activities Related Learning Outcomes Lectures (1 hr) 1 - 4 Tutorials/ practicals (1 x 1 hr) 1 - 4
The topics to be covered may include
o Introduction and data
o Descriptive statistics
o Correlation and regression
o Randomness, random variables and probability
o Sampling and sampling distributions
o Inference—estimation and hypothesis tests for one proportion and one mean
o Inference in regression
o Topics in multiple regression
o Time series analysis
o Index numbers
Specific Course RequirementsNone
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 Task Due Date/ Week Weight Length(Time) Learning Outcomes Homework Weekly 30% 2-3 hours per week 1 - 4 Individual Mid-Semester Test Advised on myUni 30% 1 hour 1 - 3 Final Exam Week 14 40% 2 hours 1 - 4 Total 100%
Assessment Related Requirements
There are NO hurdle requirements
Assessment DetailFinal exam (40%)
Individual Mid-Semester Test (30%)
- This is of 2 hours duration, plus 10 minutes reading time.
- Statistical tables are provided.
- This exam covers the whole semester i.e. will be cumulative in nature.
- Please note that, following University policy, dictionaries are not allowed in School of Economics exams.
- Graphics calculators are NOT allowed, students should bring a simple or non-graphics calculator to the exams.
- Legible hand-writing and the quality of English expression are considered to be integral parts of the assessment process. If we can’t read what you have written, we can’t give marks for it.
- Students are advised to use a black pen in the exam; write clearly, BEGIN each question ON A NEW PAGE and do not use white out or pencil.
- Details about this test will be announced during the semester on myUni. Given the cumulative nature of the final exam, the mid-term will be designed to gauge the understanding of students till that in point in time to ensure that students are not lagging behind in key statistical concepts.
- As of week 2 to week 11, there will be weekly Homework, so a total of 10 Homeworks over the semester.
- We will use an online platform called APLIA for the weekly assignments. Detailed instructions on signing up for the service will be announced in class and communicated through MyUni.
- The best 8 of these 10 assignments will be counted for assessment.
- Because not all of these marks count for assessment, no special consideration will be given to students who do not submit homework or miss tutorials for medical, compassionate or any other reason.
There is no extra work that can be done to redeem individual components of assessment during the semester, except in light of medical emergencies and other unforeseen events. However, assessment components will be made redeemable on a case by case basis after the approval of the course coordinator.
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.
Assessment Detail Graduate Attribute Homework Deep discipline knowledge
Critical thinking and problem solving
Teamwork and communication skills
Career and leadership readiness
Midterm and Final Deep discipline knowledge
Critical thinking and problem solving
SubmissionMidterm 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.
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.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.
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.
- Academic Support with Maths
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- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
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- Coursework Academic Programs Policy
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- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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