• 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
SELT Survey Results

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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
  2. Define, query and manipulate a relational database
  3. Demonstrate basic knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation.
  4. Formulate and extract descriptive and predictive statistics from data
  5. Analyse and interpret results from descriptive and predictive data analysis
  6. Apply their knowledge to a given problem domain and articulate potential data analysis problems
  7. Identify potential pitfalls, and social and ethical implications of data science
  8. 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 Feedback

Students 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 Feedback

ANU 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.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to course and data science
2 Data Visualisation Lab 1: Python 101
3 Data Analysis Lab 2: Data Analysis 1
4 Machine Learning 1 Lab 3: Data Visualisation
5 Machine Learning 2 Lab 4: Data Analysis 2
6 Machine Learning 3 Lab 5: Machine Learning 1 Mid-semester exam Assignment 1 due
7 Data Management 1
8 Data Management 2 Lab 6: Machine Learning 2
9 Data Management 3 Lab 7: Machine Learning 3
10 Security 1 Lab 8: Databases
11 Security 2 Assignment 2 due
12 Security 3 and Course Revision Lab 9: Security
13 Examination Period Final Exam

Assessment Summary

Assessment task Value Learning Outcomes
Assignments 30 % 1-8
Lab Marks 4 % 3-4
Lab test 2 % 3
Mid-semester exam 14 % 1-8
Final Exam 50 % 1-8

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU 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 Requirements

The 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 Assessment

Marks 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

Value: 30 %
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)



High Distinction



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

Value: 4 %
Learning Outcomes: 3-4

Lab Marks

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

Value: 2 %
Learning Outcomes: 3

Lab test

Assessment Task 4

Value: 14 %
Learning Outcomes: 1-8

Mid-semester exam

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

Value: 50 %
Learning Outcomes: 1-8

Final Exam

The final exam will be worth 50% and will be held during the examination period.

Academic Integrity

Academic 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.

Online Submission

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 Submission

For 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

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 Requirements

Accepted 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 Penalties

Extensions 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.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic 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 students

The 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).
Prof Matthew Hole

Research Interests

Fusion Energy Science, Plasma Physics, Computational Science

Prof Matthew Hole

Michael Purcell

Research Interests

Michael Purcell

Dr Quanling Deng

Research Interests

Dr Quanling Deng

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions