Course coordinator
Professor Gianluca Demartini
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 16/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
DATA2001 Fundamentals of Data Science will be discontinued and offered as COMP2011 Fundamentals of Data Science from 2025.
This course will utilize a scenario-based methodology to approach simple and complex data science problems in various data-intensive sectors and domains.
The aim of this course is to develop the students’ knowledge of the data science lifecycle and capability to ask the right questions to the data and to select the appropriate methods of analysis. The course is composed of four modules. The first one introducing the data science process in all its steps also discussing relevant use cases. The second module looks at the data science process applied to structured data. The third module looks at the data science process applied to unstructured data (i.e., text). The fourthﾠmodule looks at the data science process applied to time series data.
Course requirements
Assumed background
Basic knowledge of Python and SQL
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or ENGG1001) and INFS1200
Incompatible
You can't enrol in this course if you've already completed the following:
DATA7001
Course contact
Course staff
Lecturer
Professor Gianluca Demartini
Associate Professor Mahsa Baktashmotlagh
Professor Shazia Sadiq
Associate Professor Archie Chapman
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
To develop the students’ knowledge of the data scienceﾠlifecycle and capability to ask the right questions to the data and toﾠselect the appropriate methods of analysis.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the core techniques, tools and processes that are used in data science
LO2.
Design effective data science processes from problem formulation to persuasive storytelling with data
LO3.
Critically apply data science techniques to analyse, model and visualise different types of data
LO4.
Compare and discuss technical and ethical aspects of data science projects, and evaluate and reflect on the results
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation | A1 - Hindsight | 20% |
2/09/2024 3:00 pm |
| Paper/ Report/ Annotation | A2 - Insight | 25% |
30/09/2024 3:00 pm |
| Paper/ Report/ Annotation | A3 - Foresight | 25% |
25/10/2024 3:00 pm |
| Examination |
Final Exam
|
30% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
A1 - Hindsight
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
2/09/2024 3:00 pm
- Learning outcomes
- L02, L03, L04
Task description
Given a dataset(s), students will be required to produce and submit a complete Jupyter Notebook describing how the full data science process has been applied over the dataset(s) and effectively communicate the outcome of their analysis.
Submission guidelines
Turnitin link on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days, where the earlier time frame applies if there are no extensions
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A2 - Insight
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 25%
- Due date
30/09/2024 3:00 pm
- Learning outcomes
- L02, L03, L04
Task description
Given a dataset(s), students will be required to produce and submit a complete Jupyter Notebook describing how the full data science process has been applied over the dataset(s) and effectively communicate the outcome of their analysis.
Submission guidelines
Turnitin link on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days, where the earlier time frame applies if there are no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A3 - Foresight
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 25%
- Due date
25/10/2024 3:00 pm
- Learning outcomes
- L02, L03, L04
Task description
Given a dataset(s), students will be required to produce and submit a complete Jupyter Notebook describing how the full data science process has been applied over the dataset(s) and effectively communicate the outcome of their analysis.
Submission guidelines
Turnitin link on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days, where the earlier time frame applies if there are no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L04
Task description
In this exam students will be assessed on their understanding of Module 1-4 topics.
Hurdle requirements
You must achieve at least 40% on the final exam to pass the course. If you do not achieve at least 40% on the final exam then your overall mark will be capped at 49% and your final grade will be capped at 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Your final percentage will be rounded to the nearest whole number before your final grade is determined as per the cutoffs above.
You must achieve at least 40% on the final exam to pass the course. If you do not achieve at least 40% on the final exam then your overall mark will be capped at 49% and your final grade will be capped at 3.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Use of Artificial Intelligence (AI) and Machine Translation
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing A1-A3 assessment tasks. Students may appropriately use AI and/or MT in completing these assessment tasks. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
The final exam is to be completed in-person. The use of generative Artificial Intelligence (AI) and Machine Translation (MT) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lectorial This is a weekly 1 hour live session to reflect and discuss the online content and to provide complementary information to the online content. It is also an opportunity to clarify any aspect of the week's topics. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Online Content Students will consume online content (a mix of pre-recorded videos and readings) in preparation for the live lectorial. Learning outcomes: L01, L02, L03, L04 |
|
Multiple weeks From Week 3 To Week 13 |
Practical |
Practical in the Lab In this sessions, students will work on Jupyter Notebooks to analyse given datasets with the support of demonstrator. Learning outcomes: L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: