DATA1901: Foundations of Data Science (Adv)
DATA1901 is an advanced level unit (matching DATA1001) that is foundational to the new major in Data Science. The unit focuses on developing critical and statistical thinking skills for all students. Does mobile phone usage increase the incidence of brain tumours? What is the public's attitude to shark baiting following a fatal attack? Statistics is the science of decision making, essential in every industry and undergirds all research that relies on data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem solving skills in a team setting. Taught interactively with embedded technology and masterclasses, DATA1901 develops critical thinking and skills to problem-solve with data at an advanced level. By completing this unit you will have an excellent foundation for pursuing data science, whether directly through the data science major, or indirectly in whatever field you major in. The advanced unit has the same overall concepts as the regular unit but material is discussed in a manner that offers a greater level of challenge and academic rigour.
Unit details and rules
Unit code
DATA1901
Academic unit
Mathematics and Statistics Academic Operations
Credit points
6
Prohibitions
?
MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021
Prerequisites
?
None
Corequisites
?
None
Available to study abroad and exchange students
Yes
Assessment summary
-
Masterclass: You will attend research masterclasses (Sydney Data Stories) as part of your Lab classes, and then submit a short scholarly reflection on what you have learnt, through Canvas. Masterclasses are eligible for for special consideration, and if successful, will be given a mark adjustment for the final exam.
-
Projects: The data projects are designed to develop your statistical thinking and computational skills. They must be submitted electronically, as an HTML file via the DATA1001 Canvas site by the deadline. It is your responsibility to check that your project has been submitted correctly, otherwise it will not be marked.
-
Final exam: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.
-
Participation mark: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the labs. It is 0.25 marks per lab class up to 8 labs (there are 12 labs).
Late submission
In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:
-
Deduction of 5% of the maximum mark for each calendar day after the due date.
-
After ten calendar days late, a mark of zero will be awarded.
Academic integrity
The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.
We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.
You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.
Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.
Simple extensions
If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension. The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.
Special consideration
If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.
Special consideration applications will not be affected by a simple extension application.
Using AI responsibly
Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.
WK
Topic
Learning activity
Learning outcomes
Week 01
Design of experiments
Lecture and tutorial (5 hr)
LO1 LO2 LO9 LO10
Week 02
Data & graphical summaries
Lecture and tutorial (5 hr)
LO3
Week 03
Numerical summaries
Lecture and tutorial (5 hr)
LO3
Week 04
Normal model
Lecture and tutorial (5 hr)
LO4
Week 05
Linear model
Lecture and tutorial (5 hr)
LO5
Week 06
Project preparation week
Project (5 hr)
LO5
Week 07
Understanding chance
Lecture and tutorial (5 hr)
LO6
Week 08
Chance variability (The Box Model)
Lecture and tutorial (5 hr)
LO6
Week 09
Sample surveys
Lecture and tutorial (5 hr)
LO6
Week 10
Hypothesis testing
Lecture and tutorial (5 hr)
LO7 LO8
Week 11
Tests for a mean
Lecture and tutorial (5 hr)
LO7 LO8
Week 12
Tests for a relationship
Lecture and tutorial (5 hr)
LO7 LO8
Attendance and class requirements
-
Lecture attendance: You are expected to attend lectures, either face-face or livestream, or by catching up, in a timely manner, through the recordings in Canvas.
-
Lab attendance: Labs (one x 2 hours per week) start in Week 1. You must attend the Lab given on your personal timetable. Attendance at labs and participation will be recorded to determine the participation mark. Your attendance will not be recorded unless you attend the Lab in which you are enrolled. We strongly recommend you attend Labs regularly to keep up with the material and to engage with the Lab questions.
Study commitment
Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.
Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.
At the completion of this unit, you should be able to:
-
LO1. assess the importance of statistics in a data-rich world, including current challenges such as ethics, privacy and big data
-
LO2. analyse the study design behind a dataset, seeing additional evidence from literature, and evaluate how the study design affects context specific outcomes
-
LO3. design, produce, interpret and compare graphical and numerical summaries of data from multiple sources in R, using the use of interactive tools
-
LO4. apply the Normal approximation to data, with consideration of measurement error
-
LO5. model the relationship between 2 variables using linear regression, and explain linear regression in terms of projection
-
LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
-
LO7. formulate an appropriate hypothesis and perform a range of hypothesis tests on given real multivariate data and a problem
-
LO8. interpret the p-value, conscious of the various pitfalls associated with testing
-
LO9. critique the use of statistics in media and research papers in a wide variety of data contexts, with attention to confounding and bias
-
LO10. perform data analysis in a team, on data requiring multiple preprocessing steps, and communicate the findings via oral and written reproducible reports, with extensive interrogation.
-
Lectures: The Monday Intro Lecture is face-face and streamed live. The Friday Revision Lecture is on Zoom, as it involves demonstration of computation. Links are found in Canvas.
-
Labs: Labs start in week 1.
-
Unit material: All learning activities are found in Canvas.
-
Ed Discussion Board: https://edstem.org
Work, health and safety
We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.
DATA1901: Foundations of Data Science (Adv)
DATA1901 is an advanced level unit (matching DATA1001) that is foundational to the new major in Data Science. The unit focuses on developing critical and statistical thinking skills for all students. Does mobile phone usage increase the incidence of brain tumours? What is the public's attitude to shark baiting following a fatal attack? Statistics is the science of decision making, essential in every industry and undergirds all research that relies on data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem solving skills in a team setting. Taught interactively with embedded technology and masterclasses, DATA1901 develops critical thinking and skills to problem-solve with data at an advanced level. By completing this unit you will have an excellent foundation for pursuing data science, whether directly through the data science major, or indirectly in whatever field you major in. The advanced unit has the same overall concepts as the regular unit but material is discussed in a manner that offers a greater level of challenge and academic rigour.
Unit details and rules
| Unit code | DATA1901 |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
|
Prohibitions
?
|
MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021 |
|
Prerequisites
?
|
None |
|
Corequisites
?
|
None |
| Available to study abroad and exchange students |
Yes |
Assessment summary
- Masterclass: You will attend research masterclasses (Sydney Data Stories) as part of your Lab classes, and then submit a short scholarly reflection on what you have learnt, through Canvas. Masterclasses are eligible for for special consideration, and if successful, will be given a mark adjustment for the final exam.
- Projects: The data projects are designed to develop your statistical thinking and computational skills. They must be submitted electronically, as an HTML file via the DATA1001 Canvas site by the deadline. It is your responsibility to check that your project has been submitted correctly, otherwise it will not be marked.
- Final exam: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.
- Participation mark: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the labs. It is 0.25 marks per lab class up to 8 labs (there are 12 labs).
Late submission
In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:
- Deduction of 5% of the maximum mark for each calendar day after the due date.
- After ten calendar days late, a mark of zero will be awarded.
Academic integrity
The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.
We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.
You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.
Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.
Simple extensions
If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension. The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.
Special consideration
If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.
Special consideration applications will not be affected by a simple extension application.
Using AI responsibly
Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.
| WK | Topic | Learning activity | Learning outcomes |
|---|---|---|---|
| Week 01 | Design of experiments | Lecture and tutorial (5 hr) | LO1 LO2 LO9 LO10 |
| Week 02 | Data & graphical summaries | Lecture and tutorial (5 hr) | LO3 |
| Week 03 | Numerical summaries | Lecture and tutorial (5 hr) | LO3 |
| Week 04 | Normal model | Lecture and tutorial (5 hr) | LO4 |
| Week 05 | Linear model | Lecture and tutorial (5 hr) | LO5 |
| Week 06 | Project preparation week | Project (5 hr) | LO5 |
| Week 07 | Understanding chance | Lecture and tutorial (5 hr) | LO6 |
| Week 08 | Chance variability (The Box Model) | Lecture and tutorial (5 hr) | LO6 |
| Week 09 | Sample surveys | Lecture and tutorial (5 hr) | LO6 |
| Week 10 | Hypothesis testing | Lecture and tutorial (5 hr) | LO7 LO8 |
| Week 11 | Tests for a mean | Lecture and tutorial (5 hr) | LO7 LO8 |
| Week 12 | Tests for a relationship | Lecture and tutorial (5 hr) | LO7 LO8 |
Attendance and class requirements
- Lecture attendance: You are expected to attend lectures, either face-face or livestream, or by catching up, in a timely manner, through the recordings in Canvas.
- Lab attendance: Labs (one x 2 hours per week) start in Week 1. You must attend the Lab given on your personal timetable. Attendance at labs and participation will be recorded to determine the participation mark. Your attendance will not be recorded unless you attend the Lab in which you are enrolled. We strongly recommend you attend Labs regularly to keep up with the material and to engage with the Lab questions.
Study commitment
Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.
Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.
At the completion of this unit, you should be able to:
- LO1. assess the importance of statistics in a data-rich world, including current challenges such as ethics, privacy and big data
- LO2. analyse the study design behind a dataset, seeing additional evidence from literature, and evaluate how the study design affects context specific outcomes
- LO3. design, produce, interpret and compare graphical and numerical summaries of data from multiple sources in R, using the use of interactive tools
- LO4. apply the Normal approximation to data, with consideration of measurement error
- LO5. model the relationship between 2 variables using linear regression, and explain linear regression in terms of projection
- LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
- LO7. formulate an appropriate hypothesis and perform a range of hypothesis tests on given real multivariate data and a problem
- LO8. interpret the p-value, conscious of the various pitfalls associated with testing
- LO9. critique the use of statistics in media and research papers in a wide variety of data contexts, with attention to confounding and bias
- LO10. perform data analysis in a team, on data requiring multiple preprocessing steps, and communicate the findings via oral and written reproducible reports, with extensive interrogation.
- Lectures: The Monday Intro Lecture is face-face and streamed live. The Friday Revision Lecture is on Zoom, as it involves demonstration of computation. Links are found in Canvas.
- Labs: Labs start in week 1.
- Unit material: All learning activities are found in Canvas.
- Ed Discussion Board: https://edstem.org
Work, health and safety
We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.