General Information
This subject is an introduction to programming. There is a focus on writing computer code to solve problems in business, which promotes the development of problem-solving skills. The necessary foundation concepts are covered, including expressions, variables, data structures, control structures, functions, commenting and debugging. Although it can be taken as a stand-alone subject, it is specifically designed for students interested in future study in data science and big data analytics. Two widely popular programming languages for data science, R and Python, will be used as vehicles for learning programming. Cutting-edge R and Python packages used by data scientists will be covered in this subject.
Prior coding knowledge and experience is not a requirement for this subject
-
Details
Academic unit: Bond Business School Subject code: DTSC11-100 Subject title: Business Analytics Coding Subject level: Undergraduate Semester/Year: January 2021 Credit points: -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Online Workload items: - Directed Online Activity: x12 (Total hours: 24) - 2 X one-hour online video conferences per week
- Directed Online Activity: x12 (Total hours: 24) - Pre-recorded content
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all scheduled sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: No Prescribed resources.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.iLearn@Bond & Email: iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | DTSC11-100 |
| Subject title: | Business Analytics Coding |
| Subject level: | Undergraduate |
| Semester/Year: | January 2021 |
| Credit points: |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Online |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all scheduled sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List. |
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
|
| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Draft working computer programs that use variables, assignment and expressions, control structures and functions.
- Identify and apply the appropriate procedures to document computer code according to common-use standards.
- Develop computer programs in R and Python to solve business problems.
- Decompose a complex problem into the constituent parts necessary to develop a modular software solution.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
-
Assessment details
Type Task % Timing* Outcomes assessed Computer-Aided Examination (Open) Mid-semester examination. Covers R only. 25% Week 7 (Mid-Semester Examination Period) 1,3,4 Skills Assignment Develop code in R to solve a given problem. 20% Week 6 1,2,3,4 Skills Assignment Develop code in Python to solve a given problem 40% Week 12 1,2,4 *Homework Exercise Basic skills exercise 15% Ongoing 1 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
-
Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-Aided Examination (Open) | Mid-semester examination. Covers R only. | 25% | Week 7 (Mid-Semester Examination Period) | 1,3,4 |
| Skills Assignment | Develop code in R to solve a given problem. | 20% | Week 6 | 1,2,3,4 |
| Skills Assignment | Develop code in Python to solve a given problem | 40% | Week 12 | 1,2,4 |
| *Homework Exercise | Basic skills exercise | 15% | Ongoing | 1 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
The University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Bond University utilises Originality Reporting software to inform academic integrity.Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
Accessibility and Inclusion Support
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
Additional subject information
As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
-
Elements of Programming and Introduction to R
An introduction to programming is provided in the context of R, a popular programming language for data science. This topic also covers the installation of R and associated development software.
-
Data Structures in R
Key programming concepts are covered, including data types, declarations, expressions, operators and data structures. Specific data structures in R are also discussed, including vectors, factors, lists, matrices and data frames.
-
Conditional Statements in R
The concept of Boolean logic is first introduced. If, if-else and ifelse statements are then discussed. The important concept of debugging is introduced in this topic and reinforced in all future topics.
-
Loops and Apply Functions in R
The concept and advantages of loops in programming are presented. While and for loops are then discussed. Finally, the vectorisation in R is introduced and exploited with the use of apply functions.
-
Functions in R
This topic covers how to develop user-defined functions and the advantages of them. The notion of variable scope is also covered along with modular software development.
-
New Packages in R
This topic will cover R packages that are currently being heavily used in the programming community, particularly for data science.
-
Introduction to Python
The Python programming language will be introduced. Python is a populator alternative to R and the two will be compared. This topic covers installation of Python (in the lab), and data structures and control structures in Python. The debugger available in Python is also presented and contrasted with debugging in R.
-
Data Manipulation and Analysis in Python
This topic covers two very popular packages in Python, Pandas and Numpy. These packages can be used to perform scientific computations and real-world data manipulation and analysis.
-
Visualisation in Python
The vast area of data visualisation is introduced. Specific visualisations in Python are presented; the Matplotlib package is a focal point.
-
Advanced Programming Concepts in Python
This topic discusses advanced programming concepts, such as object-orientated programming and exception handling. It also ties together previous topics by sharing some practical tips for data analysis in Python.