- Class Number 2798
- Term Code 3330
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Prof Matthew Hole
- Michael Purcell
- Dr Quanling Deng
- Class Dates
- Class Start Date 20/02/2023
- Class End Date 26/05/2023
- Census Date 31/03/2023
- Last Date to Enrol 27/02/2023
Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels - scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms - span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management, basic statistical modeling (e.g., descriptive statistics, linear and non-linear regression), algorithms for machine learning and optimization, and fundamentals of knowledge representation and search. Learn key concepts in security and the use of cryptographic techniques in securing data.
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
- Define, query and manipulate a relational database
- Demonstrate basic knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation.
- Formulate and extract descriptive and predictive statistics from data
- Analyse and interpret results from descriptive and predictive data analysis
- Apply their knowledge to a given problem domain and articulate potential data analysis problems
- Identify potential pitfalls, and social and ethical implications of data science
- Explain key security concepts and the use of cryptographic techniques, digital signatures and PKI in security
Examination Material or equipment
A single A4 sized cheat sheet with notes on both sides
Staff FeedbackStudents will be given feedback in the following forms in this course:
- Written comments
- Verbal comments
- Feedback to the whole class, to groups, to individuals, focus groups
Student FeedbackANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.
|Summary of Activities
|Introduction to course and data science
|Data Visualisation Lab 1: Python 101
|Data Analysis Lab 2: Data Analysis 1
|Machine Learning 1 Lab 3: Data Visualisation
|Machine Learning 2 Lab 4: Data Analysis 2
|Machine Learning 3 Lab 5: Machine Learning 1
|Mid-semester exam Assignment 1 due
|Data Management 1
|Data Management 2 Lab 6: Machine Learning 2
|Data Management 3 Lab 7: Machine Learning 3
|Security 1 Lab 8: Databases
|Assignment 2 due
|Security 3 and Course Revision Lab 9: Security
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
PoliciesANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
Assessment RequirementsThe ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.
Moderation of AssessmentMarks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Where possible, exams will be held on campus and in person.
Assessment Task 1
Learning Outcomes: 1-8
The assignments consists of the following components:
Assignment 1 - Individual Work - 15% (Due Week 6)
Assignment 2 - Individual Work - 15% (Due Week 11)
|NUMERICAL MARK (%)
Work of exceptional quality, as demonstrated in the attainment of learning outcomes at or above the relevant qualification level
Work of superior quality, as demonstrated in the attainment of learning outcomes at or above the relevant qualification level
Work of good quality, as demonstrated in the attainment of learning outcomes at or above the relevant qualification level
Work of satisfactory quality, as demonstrated in the attainment of learning outcomes at or above the relevant qualification level
Work in which the attainment of learning outcomes at or above the relevant qualification level has not been demonstrated
Assessment Task 2
Learning Outcomes: 3-4
There will be assessments in every even numbered lab (2, 4, 6, and 8) and in lab 9. Each of these will be worth 1 mark, adding up to a total of 5 marks for the labs.
The lab marks are redeemable against the final exam.
Assessment Task 3
Learning Outcomes: 3
Assessment Task 4
Learning Outcomes: 1-8
The mid-semester exam will be a lab exam. You will work in a restricted environment with no access to the external network. There will be a directory for each main question, and files in each such directory that you will need to use to answer particular sub-questions. You will need to create appropriate program files for the programming related tasks. Make sure that you save your files regularly. There will be around 3 main questions in the exam.
Assessment Task 5
Learning Outcomes: 1-8
The final exam will be worth 50% and will be held during the examination period.
Academic IntegrityAcademic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education), assignment submission must be through Turnitin. Github and other plagiarism checking tools may be used.
Hardcopy SubmissionFor some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.
Late submission of assignments is not accepted. If you submit an assignment after the assignment deadline, then you will get a mark of zero for that assignment.
Extensions will only be granted in extraordinary circumstances and can only be given by the course convenor. You have to approach the course convenor as soon as possible, and bring any documentary evidence with you. The course convenor may grant an extension, may vary the specifications of your assignment, or (in truly exceptional cases) may vary your assessment in some other appropriate way.
Referencing RequirementsAccepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.
Extensions and PenaltiesExtensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Distribution of grades policyAcademic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for studentsThe University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Fusion Energy Science, Plasma Physics, Computational Science
Prof Matthew Hole