SCIENCE 7030 - Applied Data Science with Python
North Terrace Campus - Semester 1 - 2020
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General Course Information
Course Details
Course Code SCIENCE 7030 Course Applied Data Science with Python Coordinating Unit School of Physical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Assessment Literature reviews, project report and tests Course Staff
Course Coordinator: Professor Martin White
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1 understand and apply different classes of data science algorithms and critically interpret their output 2 compare and contrast different algorithms to identify the algorithm type appropriate for a particular problem from business, science or health science 3 confidently discuss data science problems and display critical and logical thinking 4 demonstrate an understanding of data science problems in the abstract, in addition to their discipline-specific content 最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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)
1,2,3,4 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
2,3,4 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
2,3 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3,4 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
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Learning & Teaching Activities
Learning & Teaching Modes
Learning activities
• 12 x Online lectures
• 12 x 2 hour computer workshops
• 2 x 2 hour tutorialsWorkload
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
Online lectures will be used to present discussions of data science and how it works. This will include short lecture
segments that introduce and develop an understanding of different data science methods (e.g. k-means clustering, neural networks). Sample output of data science algorithms will be provided for a range of problems from business, health science, geology, chemistry and physics to aid discussion. This will facilitate an understanding of the similarities and differences between naively disparate problems.
Each week’s computer laboratory session will involve hands-on computer work, in which an intuitive knowledge of data
science algorithms will be developed by using the Python programming language. Here, students will apply the techniques learnt in the online lectures. These sessions will involve assessment via question sheets.
In addition to these workshops, two literature comprehension exercises will be run to encourage students to engage with the research literature (assessed by written report).
Finally, a project will be developed by students in the last two weeks of the computer lab sessions, with assessment via written report.
Specific Course Requirements
The computer laboratory sessions are compulsory. -
Assessment
The 最新糖心Vlog'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 Hurdle Yes or No Weighting Learning Outcome Due PC lab in-class exercises Formative & summative No
30% 1,2 Weeks 1-10 Literature exercise 1 Formative & summative No 15% 1,3,4 Week 4 Literature exercise 2 Formative & summative No 15% 1,3,4 In-semester tests Formative & summative No 10% 1,2 Week 6 & 10 Final project Formative & summative No 30% 2,3,4 Week 12 Assessment Related Requirements
Computer laboratory sessions are compulsory. This includes attendance and conduct of the required computer work, which will be
assessed during the session.Assessment Detail
In-semester tests (total of 10%)
Students will complete a total of 2 in-semester tests (worth 5% each). These are designed to refresh knowledge of a topic and indicate the major points of learning required for the final project. Tests will consist of short answer, discursive questions. They will be held as online quizzes. Students receive feedback one week later.
Final project report (30%)
Students will prepare a 2000 word report on a data analysis project undertaken during the final two weeks of computer lab sessions. Students will work individually, and will be assigned a problem using real-world data. Students will be assessed on their problem-solving ability, analysis, understanding of data science techniques and communication skills.
Literature review exercises (30%)
Students will complete two literature comprehension and analysis exercises during the course. Examples of data science applications will be taken from the research literature. Students will be required to read the paper, then attend a 2 hour tutorial at which they ask questions. They will then prepare a written report of 1000 words detailing their understanding, critical analysis of the research, and present suggested improvements for the research paper. Two of these will be completed in total (worth 15% each).
Computer lab in-class tests (30%)
During the first ten weeks of computer lab sessions, students will complete a short questionnaire testing their knowledge of the algorithm introduced that week. This will include questions relating to changing the Python example and documenting the changes (via either multiple choice questions, or short written answers). Students will receive feedback one week later.Submission
Submission of Assigned Work
Coversheets must be completed and attached to all submitted work. Coversheets can be obtained from the School Office (room G33 Physics) or from MyUNI. Work should be submitted via the assignment drop box at the School Office.
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 Coordinator 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:
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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
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- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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