| Semester 1, 2020 Online | |
| Short Description: | Intro Data Science Visual |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Sciences |
| Student contribution band : | Band 2 |
| ASCED code : | 020199 - Computer Science not elsewhere |
| Grading basis : | Graded |
Staffing
Examiner:
Other requisites
While this course does provide an accelerated introduction to fundamental programming knowledge, it is the student鈥檚 responsibility to ensure their introductory knowledge of computing is consistent with that found in CSC1401 Foundation Programming. Students without the requisite knowledge may wish to enrol in other courses prior to this course or at least co-enrol to ensure that their introductory knowledge of computing meets this requirement.
Rationale
Government, private enterprise and science have always been data-driven, what is changing dramatically is the sheer amount of data now generated. Data Science, sometimes also referred to as Big Data, is a rapidly evolving field which studies how to organize, analyse and communicate relevant data through appropriate data visualisations as well as written and oral communications. While data science鈥檚 technical foundations arise from Mathematics, Statistics and Computer Science, the area is fundamentally both multi and interdisciplinary. It is most often performed in collaborations across disciplines to bring together the necessary skills and relevant application knowledge. Those with a technical background related to data science need an understanding of the data relevant to the particular problem application area. Those with expertise in the application area must acquire the relevant technical knowledge in order to effectively and accurately make use of data science tools and methodologies.
Synopsis
This course covers foundational data science concepts, tools, methodologies and visualisation. Students will learn how to extract knowledge from data through hands-on experience with common data science programming tools and methodologies. They will create data visualisations to conduct exploratory and confirmatory data analysis. And will gain an appreciation of the breadth of data science applications and their potential value across disciplines.
Objectives
On successful completion of this course students will be able to:
- differentiate between data science algorithms and interpret their appropriate application across disciplines.
- apply data visualisations and written communication, tailored to specific discipline audiences, to report a data science project鈥檚 central problem, data analysis, reasoning and conclusions.
- identify and apply the appropriate technical processes and problem-solving skills for the successful completion of a data science project.
- plan and execute a data science project.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Basic data science algorithms and their applications, such as recommender systems, online advertising, and others depending on selected case studies | 20.00 |
| 2. | Common tools for programming, development and data management | 20.00 |
| 3. | Creating data visualisations for exploratory and confirmatory analysis | 20.00 |
| 4. | Data wrangling | 15.00 |
| 5. | Creating and presenting visualisation models | 15.00 |
| 6. | Mining text from the social web | 10.00 |
Text and materials required to be purchased or accessed
ALL textbooks and materials available to be purchased can be sourced from (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=01&subject1=CSC8001)
Please for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
Reference materials
Student workload expectations
| Activity | Hours |
|---|---|
| Assessments | 56.00 |
| Examinations | 2.00 |
| Private Study | 112.00 |
Assessment details
| Description | Marks out of | Wtg (%) | Due Date | Notes |
|---|---|---|---|---|
| Assignment 1 | 100 | 20 | 07 Apr 2020 | |
| Data Science Project Report | 100 | 30 | 19 May 2020 | |
| Online Exam | 100 | 50 | End S1 | (see note 1) |
Notes
- This will be an open examination. Students will be provided further instruction regarding the exam by their course examiner via StudyDesk. The examination date will be available via UConnect when the official examination timetable has been released.
Important assessment information
-
Attendance requirements:
There are no attendance requirements for this course. However, it is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration. -
Requirements for students to complete each assessment item satisfactorily:
Due to COVID-19 the requirements for S1 2020 are: To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.
Requirements after S1 2020:
To complete each of the assessment items satisfactorily, students must obtain at least 50% of the marks available for each assessment item. -
Penalties for late submission of required work:
Students should refer to the Assessment Procedure (point 4.2.4). -
Requirements for student to be awarded a passing grade in the course:
Due to COVID-19 the requirements for S1 2020 are: To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.
Requirements after S1 2020:
To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.
Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.
To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure (point 4.4.2). -
Method used to combine assessment results to attain final grade:
The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course. -
Examination information:
Due to COVID-19 the requirements for S1 2020 are: An Open Examination is one in which candidates may have access to any printed or written material and a calculator during the examination
Requirements after S1 2020:
This is a Closed examination. Candidates are allowed to bring only writing and drawing instruments into a closed examination. -
Examination period when Deferred/Supplementary examinations will be held:
Due to COVID-19 the requirements for S1 2020 are: The details regarding deferred/supplementary examinations will be communicated at a later date
Requirements after S1 2020:
Any Deferred or Supplementary examinations for this course will be held during the next examination period. -
University Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at .