## ECON 1008 - Data Analytics I

### North Terrace Campus - Semester 2 - 2023

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, though these tools are also useful in many other real-world settings. The focus is on concepts, reasoning, interpretation, and thinking that build upon computation, formulae and theory. Students will be required to clearly and effectively communicate and visualize their ideas, analyses, and results. The course covers two main branches of statistical data analysis: descriptive statistics and inferential statistics. Descriptive statistics includes data collection, exploration, and interpretation through numerical and graphical techniques such as charts and visual representations. Inferential statistics includes the selection and application of correct and suitable statistical techniques in order to make estimates or test claims about data based on a sample. By the end of this course, students should understand and know how to use statistics in real-world settings. Students will also develop some understanding of the limitations and misuse of statistical inference as well as the ethics of data analysis and statistics.

• General Course Information
##### Course Details
Course Code ECON 1008 Data Analytics I Economics Semester 2 Undergraduate North Terrace Campus 3 Up to 3 hours per week. Intensive in Summer Semester Y ECON 1008UAC, WINEMKTG 1015EX, STATS 1000, STATS 1005, STATS 1004, STATS 1504. Not permitted after ECON 1011. Cannot be counted towards BCompSc, BCompSc Adv, BCompGr, BMath, BMath Adv, BMathComp Sci or BEng(Software Engineering) A quota may apply Typically tutorial participation and/or exercises, assignments, tests and final exam
##### Course Staff

Course Coordinator: Dr Florian Ploeckl

 Adelaide Semester 1 Melbourne Semester 1 Name: Dr Virginie Masson Name: Chris Stewart Email: virginie.masson@adelaide.edu.au Email: christopher.stewart@adelaide.edu.au Adelaide Semester 2 Melbourne Semester 2 Name: Email:
##### 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 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.

Melbourne Campus students

- Students in this course are expected to attend two 1-hour lectures and one 2-hour practical (tutorial) class each week.
- PRACTICALS (tutorials) commence in WEEK 2 and ASSESSMENT in practicals BEGINS in WEEK 2.
- Melbourne Campus students are asked to refer to MyUni for applicable timetable and assessment information.

• 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.

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.

1,2,4

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.

1,2,3,4

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

Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

1,2,3,4

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.

2,3,4

Attribute 7: Digital capabilities

Graduates are well prepared for living, learning and working in a digital society.

1,2,3,4

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
• Learning Resources
##### Required Resources
Text book
Selvanathan S, Selvanathan S and Keller G,  Business Statistics: Australia New Zealand Edition 8
ISBN 9780170439527

Calculator
Students will need a calculator; a basic one that can take squares, square roots etc is sufficient.
##### Recommended Resources

In Semester 1, and 2 it is intended that live lectures be recorded and a recording of each lecture put on MyUni for students offshore or those needing to quarantaine and who cannot attend the face-to-face lectures.

NOTE: Dictionaries are not allowed in exams

##### Online Learning
Extensive use is made of MyUni, so please check the announcements regularly. Lecture notes, tutorial questions, and other relevant material will be made available on MyUni.

There are discussion boards on MyUni. This is the preferred way for students to ask questions so that all students have the same information and any of the staff can reply, allowing for quicker response time.
• Learning & Teaching Activities
##### Learning & Teaching Modes
This course uses lectures plus tutorials. The lectures provide an overview of the course content but students must expect that they
will need to study the textbook in order to understand the work.

The tutorials may incorporate team based learning, discussions, problem solving activities, individual and group work, student questions and student participation. These tutorials provide the opportunity for students to practice; they are vital for success in this course. Before
the tutorials, students are expected to have attended or watched and understood the lectures and to have read the relevant chapter(s) from the text book.

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 and Lecture Material 4 Hours per week Prepare Quizzes and Assignment Answers 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 (subject to changes) are:

MODULE 1  Introduction to Statistics & Analytics
What is Statistics?
Types of Data, Data Colleciton and Sampling
MODULE 2  Analysing Data
Graphical Descriptive Techniques - Nominal Data
Graphical Descriptive Techniques - Numerical Data
Measures of Central Locations
Measures of Variability
Measures of Relative Standings
MODULE 3   Probability & Chance
Probability
Random Variables and Discrete Probability Distributions
Random Variables and Continuous Probability Distributions
MODULE 4  Estimation & Hypothesis Testing
Statistical Inference and Sampling Distribution
Estimation - Single Population
Hypothesis Testing
Estimation - Two Populations
MODULE 5  Correlation and Regression
Covariance and Correlation
Linear Regression Model

##### Specific Course Requirements
None
• 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 Weight Length(Time) Learning Outcomes Engagement Activities* Weekly 20% varying 1 - 4 Assignments TBA 30% varying 1 - 4 Final Examination Exam Period 50% 2 hours 1 - 4 Total 100%
Engagement Activities consist of weekly quizzes, and active participation in tutorials.
##### Assessment Related Requirements

There are NO hurdle requirements

##### Assessment Detail

No information currently available.

##### Submission
All activities to be submitted online through MyUni, with the exception of tutorial participation.

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
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.

To be anounced on MyUni.
• 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.

The revisions to this course, based on student feedback, include a clearer structure of topics, more opportunities to practice questions, a reduction in expenses for online materials and a change in weighting towards continuous assessment.
• 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 studentâ€™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.

```
```