APP DATA 2010 - Data Handling and Visualisation II

North Terrace Campus - Semester 1 - 2023

This course covers the basics of how large data sets are managed to extract meaningful information. Concepts of data storage and access are developed in a hands-on learning environment. Visualisation of large data sets allows us to understand subtle patterns that are not otherwise obvious. The course will use Python as its core programming environment using real example data sets drawn from a variety of different disciplines.

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Course Details

Course Code	APP DATA 2010
Course	Data Handling and Visualisation II
Coordinating Unit	School of Physical Sciences
Term	Semester 1
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 7 hours per week
Available for Study Abroad and Exchange	Y
Incompatible	COMP SCI 7210
Assumed Knowledge	SCIENCE 1500 or MATHS 1004 or ECON 1008
Assessment	Programming practical reports, quizzes, research project

Course Staff

Course Coordinator: Professor Graham Heinson

Course Timetable

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

Course Learning Outcomes

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

1	Understand how data are organised and managed to extract significant information.
2.	Demonstrate fundamental approaches to data visualisation.
3	Program using Python
4	Understand the basics of SQL
5	Interpret data sets from different disciplines in a major project

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)
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
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.	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.	5
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	5
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.	5
Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.	1,2,3,4,5
Attribute 7: Digital capabilities Graduates are well prepared for living, learning and working in a digital society.	1,2,3,4,5
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.	5

Learning Resources

Required Resources

Zhang, Y., (2015), An Introduction to Python and Computer Programming Links to an external site.(Links to an external site.),,(1st ed. Lecture Notes in Engineering 353), Springer, London.

Lee, K., & Mackie, I., (2014), Python Programming Fundamentals (Links to an external site.) Links to an external site.,(2nd ed. Undergraduate Topics in Computer Science), Springer, London.

Recommended Resources

Nelli, F., (2018), Python Data Analytics With Pandas, NumPy, and Matplotlib Links to an external site., (2nd ed.), Springer, New York.

VanderPlas, J., (2016), Python Data Science Handbook: Essential Tools for Working with Data Links to an external site.(1st ed.), Sebastapol CA, O'Reilly Media, Inc.
Learning & Teaching Activities
Learning & Teaching Modes
This course consists of:
- Lectures: 12 x 1 hr per week
- Computer Practicals: 12 x 4 hrs per week
- Workshops: 12 x 2 hrs per week
Workload

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

A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision)

Learning Activities Summary

This course is split into five main modules and a project. Each module takes two weeks and includes an assignment and an online quiz.

Module 1: By successfully completing this module, you should be able to:

use Python to construct basic programs
use Anaconda and Jupyter Notebook with basic programs
recognise different types of data and their use in data science
select real-world data from different sources
evaluate real-world data from different sources.

Module 2: By successfully completing this module, you should be able to:

construct a program for a repeated task using an iterative structure
construct a program that includes a decision.

Module 3: By successfully completing this module, you should be able to:

Translate real-world data to computer representation.
Identify the most appropriate structure for grouping together given real-world data.
Construct a program for data grouping.
Recognise the benefits of using functions in programs.
Create a function using Python.

Module 4: By successfully completing this module, you should be able to:

Distinguish the difference between objects and other structures.
Create an object using Python.
Distinguish the difference between lists and arrays.
Source an appropriate NumPy method for a Python program.
Integrate an appropriate NumPy method in a Python program.

Module 5: By successfully completing this module, you should be able to:

Source an appropriate Pandas method for a Python program.
Integrate an appropriate Pandas method in a Python program.
Source an appropriate Matplotlib method for a Python program.
Integrate an appropriate Matplotlib method in a Python program.

Module 6: Project

Create a solution for a real-world problem.
Evaluate outcomes of a real-world problem.
Report outcomes of a real-world problem to peers.

Specific Course Requirements

Compulsory attendance of the Computer practicals and Workshops is required as they address the key course learning objectives 1-4

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 Summary

Assessment Task	Task Type	Weighting	Hurdle Yes/No	Due	Learning Outcome
Programming practical (Five biweekly)	Summative	30%	No	Weeks 2,4,6,8,10	4
Concept quizzes (Five biweekly)	Summative	20%	No	Weeks 1,3,5,7,9	1,2,3
Discipline-specific research project	Summative	50%	No	Weeks 11 or 12	1,2,4

Assessment Related Requirements

Attendance at practicals and workshops is compulsory. The learning outcomes for this course are substantially dependent on
this hands-on experience and practice. Therefore, missing any practicals or workshops without an allowed absence will result in a grade of FAIL being recorded for the course. Students are able to apply for an allowed absence to the Course Coordinator.

Assessment Item	Requirement for Hurdle	Is additional assessment available if student does not reach hurdle requirement?	Details of additional assessment if known
Practicals and workhops are compulsory	Satisfactory completion of all practicals, including attendance of ALL practical and workshop sessions and reasonable attempt at ALL practical assessments	Yes	Missing any practical/workshop class or failing to submit a reasonable attempt at any practical report in a semester will result in a grade of FAIL being recorded for the course. Students with medical or compassionate reasons for non-attendance will be given an opportunity to compensate for missed practical/workshop sessions.

Assessment Detail

Programming practical (Five biweekly): Total of 30% of course grades
In this assessment, students will be required to demonstrate their ability to create Python programs to solve a specific problem. Online-upload on Python scripts.

Concept quizzes (Five biweekly): Total of 20% of course grades
In this assessment, students will be required to demonstrate their understanding of concepts of data management and visualisation. Online upload of short written answers though MyUni, and embed figures and Python scripts. We will not use multiple choice.

Discipline specific research project: Total of 50% of course grades
In this assessment, students will be required to identify a data set to work with (typically from their area of discipline) and build a Python program to extract and visualise information about the data set. The purpose of this assessment is for the student to demonstrate their ability to apply what they have learned throughout the course in the creation of a document (about 2000 words and figures) including programs (as an online appendix) to answer questions about discipline-specific data, along with a short video (10 minutes) explaining their work. Emphasis is given to creative use of visualisation.

Submission

Submission of Assigned Work
Instructions on submission of work will be available on MyUn

Extensions for Assessment Tasks
Extensions of deadlines for assessment tasks may be allowed for reasonable causes. Such situations would include compassionate and medical grounds of the severity that would justify the awarding of a supplementary examination. Evidence for the grounds must be provided when an extension is requested. Students are required to apply for an extension to the Course Co-ordinator before the assessment task is due. Extensions will not be provided on the grounds of poor prioritising of time.
The assessment extension application form can be obtained from: http://www.sciences.adelaide.edu.au/current/

Late submission of assessments
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.

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 (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.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator