Postgraduate Programs in Applied Data Analytics

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ANU is pleased to offer new postgraduate study opportunities for professionals interested in developing skills – or upskilling – in the area of data analytics. These programs are designed to address a global shortage of graduates with skills in data analytics, which is vital to the development of high-quality, data-informed decision-making. The program has wide-ranging applications for the Australian government, Australian businesses and the broader community, all of which are facing the challenge of how to use public data effectively and informatively.

The rapid expansion of the digital environment has increased the opportunity for data-driven innovation, but also the dangers of it. Being able to understand and anticipate developments in the manipulation and use of data will result in a workforce with a diverse skillset that ranges over computational, statistical and methodological approaches. These areas of expertise can be applied in a range of professional settings, from public health to national security, from education to consumer industry.

The data analytics program focuses on equipping students to apply their skills in solving problems that reflect ‘real’ conditions, as well as giving students familiarity with the underlying principles of ‘big data’ software systems. Real-world case studies are embedded in several key courses spread throughout the curriculum. Students undertaking the data analytics program should expect:

  • Exposure to best-practice in data analytics.
  • Cutting-edge courses in areas of relevance to data analysts.
  • Opportunities to develop detailed knowledge of one of three academic specialisations – computation, statistics, or social science.
  • Professional development for students already working in the data analytics field.
  • To pursue research of professional relevance.
  • Pathways and exit points which reflect students’ differing educational requirements, as well as varying degrees of technical proficiency and real-world expertise.

The ANU’s ‘T-shaped’ programs are intended to develop interdisciplinary knowledge across three foundational academic areas: computing, statistics and social science. Students will also develop a specialisation in one of these three areas. The program is designed to have flexible entry and exit points, reflecting the diverse student cohort with different professional backgrounds and varying levels of technical expertise. The Data Analytics program is comprised of three different exit awards:

Students can apply directly to either the Graduate Diploma or Masters awards, and can also transfer between award programs. For example, a student enrolled in the Graduate Diploma can subsequently transfer to the Masters award, with credit for courses already completed. Equally, a student enrolled in the Masters who decides not to complete the full award program can request to exit with a Graduate Diploma, which requires fewer courses. The Graduate Certificate is an exit-only qualification, meaning that it is only available to students enrolled in the Graduate Diploma who wish to exit the program early.

I'm interested! Now what?

Applications for the Applied Data Analytics program are lodged through the Universities Admissions Centre (UAC).

The application and enrolment process has 5 key stages:

Step 1

Students lodge an application through the Universities Admissions Centre: http://www.uac.edu.au/postgraduate/

Step 2

Students receive an email from the ANU’s Domestic Admissions team, containing an offer for study.

Step 3

Students complete the online acceptance process, and will then receive their ANU logon and enrolment instructions from the Data Analytics team.

Step 4

Students log on to ISIS, the ANU’s Interactive Student Information System, to enrol themselves in their chosen courses.

Step 5

Students log on to Wattle, the ANU’s online learning portal, to access their course materials

Application process

Applications for the Applied Data Analytics program are available through the Universities Admissions Centre (UAC): http://www.uac.edu.au/postgraduate/

There are different application closing dates for each academic session (Autumn, Winter, Spring etc.). These dates are accessible through the ‘course search’ function on the UAC postgraduate website.  Please note that applications for study in 2018 will be open from 5 September 2017.

The application and enrolment process has 5 key stages:

  • Students lodge an application form, academic transcripts and curriculum vitae through UAC.
  • Students will receive an email to their nominated address from the ANU’s domestic admissions team. This email will contain an offer for study. Students must follow the instructions contained in the offer letter in order to accept their place at the ANU.
  • Students who have completed the online acceptance process will receive an email from the Data Analytics team. The email will contain the student’s ANU ID number, student password, and enrolment instructions.
  • Students must log on to the ANU’s Interactive Student Information System (ISIS) with their ID and password, and follow the instructions to enrol themselves in their chosen courses.
  • Online course material will be available to all enrolled students through the ANU’s Wattle portal: https://wattle.anu.edu.au/. Wattle requires the same ID number and password as ISIS. Course material will become visible about a week before the commencement date of the course.

 

Delivery mode

All courses listed on the Data Analytics class schedule are online intensive courses.

These are delivered in 4+1+4 mode – 4 weeks of online teaching and assessment, followed by one full-time week on campus at the ANU, followed by another 4 weeks of online teaching and assessment. Students will only be required to be on campus in Canberra during the one-week intensive period for each course.

Some of the courses which form part of the Data Analytics program are also run in semester-long mode for other ANU students. For example: STAT7055 is run in Spring session 2017 for the Data Analytics cohort, and in Semester 2 2017 for students enrolled in Economics and Accounting programs.

Data Analytics students should always take the versions of courses which are specified on the Data Analytics class schedule, as these are the only courses taught in online mode. Semester-long courses require students to attend classes on campus over 12 weeks, with no online component.

Credit

Applicants who have completed a degree in a discipline related to Data Analytics may be eligible to receive credit towards their degree. For example, a student with a background in databases may be given credit for COMP7240 – Introductory Databases. In this case, the student’s degree program is shortened by one course, because the student is regarded as having completed COMP7240.

The ANU’s credit policy for coursework students states that credit cannot be given for study which predates the course for which credit is being sought by more than 7 years. See the ‘Coursework Award Rules 2016’, section 3.3.1: https://www.legislation.gov.au/Details/F2016L00992

Students whose studies fall outside this 7-year window may be granted an exemption instead of credit. For example, a student whose database studies were completed in 2006 may be granted an exemption for COMP7240. In this case, the student’s degree is not shortened, but they can choose a different course with which to replace COMP7240.

Program structure

Stage 1: Foundational courses

Stage 1 comprises four courses which are designed to introduce students to the key concepts of the Data Analytics program. The courses cover two technical disciplines – Statistics and Computer Science – and act as a re-skilling opportunity for students with some experience in these areas. Students coming from a technical background are highly likely to be granted recognition of prior learning for some or all of Stage 1. Students may choose to leave the program at the end of Stage 1 with a Graduate Certificate of Applied Data Analytics.

Stage 2: Core material

Stage 2 builds on the foundational courses in Stage 1, developing students’ knowledge across the program’s three main areas of Statistics, Computer Science and Social Science. In Stage 2, students will be given regular opportunities to apply their skills in solving problems that reflect real-world conditions. Students will also participate in teamwork as part of this integrated approach to learning. Students with extensive experience in one of the three disciplines may be granted recognition of prior learning for some of the courses in Stage 2, at the discretion of the program convenor. Students may choose to leave the program with a Graduate Diploma of Applied Data Analytics after completing four courses from Stage 2 – eight courses in total, combined with those from Stage 1.

Stage 3: Specialisation

Stage 3 continues to offer students the opportunity to upskill, by facilitating specialisation in one of the program’s three disciplines – Statistics, Computer Science or Social Science. Students completing Stage 3 will take a further two courses after Stage 2, enabling them to apply their broad-based skills whilst refining their expertise in their chosen area. Students who complete this stage of the program will be awarded a Master of Applied Data Analytics.

 

The following tables illustrate the relevant patterns of study for each of the three Data Analytics qualifications.

Applicants should note that the program rules have been updated since the ANU's first intake in 2016. The below rules will take effect from January 2018.

Students who commenced in 2016 or 2017 may be able to graduate under these rules, with the approval of the program convenor. Students wishing to view the program rules for their own year of commencement should follow the below links for the relevant award.

 

Graduate Certificate Applied Data Analytics

Students undertaking the Graduate Certificate must complete four courses from the following list, comprising a minimum of one COMP course, a minimum of one STAT course and a maximum of one SOCR course:

COMP7230 (Introductory Programming)

STAT7055 (Introductory Statistics)

SOCR8201 (Introductory Social Science)

COMP7240 (Introductory Databases)

STAT7001 (Applied Statistics)

SOCR8202 (Using Data to Answer Policy Questions)

 

Graduate Diploma Applied Data Analytics

Students undertaking the Graduate Diploma must complete the following eight courses:

COMP7230 (Introductory Programming)

STAT7055 (Introductory Statistics)

SOCR8201 (Introductory Social Science)

COMP7240 (Introductory Databases)

STAT7001 (Applied Statistics)

SOCR8202 (Using Data to Answer Policy Questions)

COMP8410 (Data Mining)

STAT6039 (Mathematical Statistics)

 

 

Master of Applied Data Analytics

Students undertaking the Master Applied Data Analytics must complete 12 courses in total – the following 10 courses, plus any two from the choice of Stage 3 courses:

COMP7230 (Introductory Programming)

STAT7055 (Introductory Statistics)

SOCR8201 (Introductory Social Science)

COMP7240 (Introductory Databases)

STAT7001 (Applied Statistics)

SOCR8202 (Using Data to Answer Policy Questions)

COMP8410 (Data Mining)

STAT6039 (Mathematical Statistics)

 

COMP8430 (Data Wrangling)

STAT7026 (Graphical Data Analysis)

 

Stage 3 courses:

COMP6490 (Document Analysis)

STAT7016 (Bayesian Data Analysis)

SOCR8203 (Adv. Social Science Techniques)

COMP8420 (Bio-inspired Computing)

STAT7030 (Linear Models)

SOCR8204 (Policy Development & Service Delivery)

COMP8600 (Statistical Machine Learning)

STAT7040 (Statistical Learning)

SOCR8006 (Online Research Methods)

 

STAT8002 (Applied Time Series Analysis)

DEMO/SOCR8082 (Social Research Practice)

 

Stage 1

C1 - Introduction to Programming for Data Scientists (COMP7230)

This course teaches introductory programming within a problem-solving framework applicable to data science. The course emphasises technical programming, data processing, and data manipulation. There is an emphasis on designing and writing correct code. Testing and debugging are seen as integral to the programming enterprise. The course will also teach how to effectively use computational tools for data analysis.

C2- Introduction to Database Concepts (COMP7240)

This course is an introduction to database concepts and the general skills for designing and using databases. The topics include the relational data model, SQL, entity-relationship model, dependencies, query processing and optimisation, and database transactions and security. To deepen the understanding of relational databases, the current industry development of database systems such as NoSQL databases will be introduced.

STAT1 - Introductory Statistics for Business and Finance (STAT7055)

This course will introduce students to basic statistical methods, with a focus on applying these methods to the business world. This course assumes no statistics background.

STAT2 - Applied Statistics (STAT7001)

This course builds on STAT1 (STAT7055) and provides an introduction to common applied techniques for carrying out statistical analysis. This course assumes knowledge of STAT7055.

 

Stage 2

C3 - Data Mining (COMP8410)

Large amounts of data are increasingly being collected by public and private organisations, and research projects. Additionally, the Internet provides a very large source of information about almost every aspect of human life and society. This course provides a practical focus on the technology and research in the area of data mining. It focuses on algorithms and techniques, and less on mathematical and statistical foundations.

C4 - Data Wrangling (COMP8430)

Real-world data are commonly messy, distributed, and heterogeneous. This course introduces core concepts of data cleaning, standardisation and integration, and discusses data quality, management, and storage issues as relevant to data analytics.

SS1 - Introduction to social science methods and types of data (SOCR8201)

This course provides an introduction to the main empirical social science methods, types of data, and techniques for collecting social science data.

SS2 - Using data to answer policy questions and evaluate policy (SOCR8202)

This course will provide students with a range of analytical techniques that can be used to answer policy and service delivery questions and measure the impact of policy.

STAT3 - Principles of Mathematical Statistics (STAT6039)

This course builds on STAT1 (STAT7055) and STAT2 (STAT7001) and provides an introduction to mathematical statistics with applications.

STAT4 - Graphical Data Analysis (STAT7026)

This course introduces the principles of data representation, summarisation and presentation with particular emphasis on the use of graphics.

 

Stage 3

C5 & C6 – Two courses from a choice of:

Statistical Machine Learning (COMP8600)

This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. The course covers a broad range of topics including the major techniques used in machine learning.

Document Analysis (COMP6490)

Processing of semi-structured documents such as Internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the 'document' and its various genres as a fundamental object for business, government and community.

Bio-inspired Computing: Applications and Interfaces (COMP8420)

Bio-inspired Computing is the combination of computational intelligence and collective intelligence. This course introduces the fundamental topics in bio-inspired computing, and builds proficiency in the application of various algorithms to real-world problems.

SS3 & SS4 – Two courses from a choice of:

Advanced techniques in the creation of social science data (SOCR8203)

This course will provide students with a detailed understanding of the main techniques for the collection of policy relevant social science data. Students will be well placed to design and undertake their own research and to commission others to undertake design, fieldwork and analysis.

Advanced social science approaches to inform policy development and service delivery (SOCR8204)

This course will provide a more advanced treatment of how social science approaches can be used to inform policy development and service delivery approaches.

Online Research Methods (SOCR8006)

This course will provide students with thorough training in online research methods for social science Internet research.  While "obtrusive" social research methods (e.g. online surveys and focus groups) will be covered, there will be greater emphasis on unobtrusive research methods, e.g. quantitative (statistical) analysis of Internet trace data from emails, websites, blogsites and social networking sites such as Facebook.

Social Research Practice (DEMO/SOCR8082)

This course aims to provide students with a solid understanding of each phase in the life of a research project­ (conception, scoping, planning, doing, and finalisation)­ and the way in which the components within each phase fit together.

STAT5 & STAT6 – Two courses from a choice of:

Statistical Learning (STAT7040)

This course provides an introduction to statistical learning and aims to develop skills in modern statistical data analysis.

Introduction to Bayesian Data Analysis (STAT7016)

The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.

Generalised Linear Models (STAT7030)

This course is intended to introduce students to generalised linear modelling methods, with emphasis on, but not limited to, common methods for analysing categorical data. 

Applied Time Series Analysis (STAT8002)

This course considers statistical techniques to evaluate processes occurring through time. It introduces students to time series methods and the applications of these methods to different types of data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk-management and sociology).

 

Course schedule

**N.B.: These tables show only the intensive (i.e. online) iterations of these courses. For details of semester-long, on-campus versions of the same courses, please follow the course links available in the 'program structure' tab immediately above.

2017 intensive-mode courses:

Convenor

Course dates

Intensive week

Last day to enrol

Census date

Academic session

SOCR8201 – Introduction to Social Science

Steve McEachern

29/5 – 28/7

26 -30 June

2/6

16/6

Autumn

COMP8410 – Data Mining

Kerry Taylor

COMP7230 – Programming for Data Scientists

Armin Haller

7/8 – 6/10

4 -8 Sept

11/8

25/8

Winter

STAT7001 – Applied Statistics

Grace Chiu

STAT7055 – Introductory Stats for Business and Finance

Gen Nowak

2/10 – 1/12

30 Oct – 3 Nov

6/10

20/10

Spring

SOCR8202 – Using Data to Answer Policy Questions

Nick Biddle

 

2018 intensive-mode courses:

Convenor

Course dates

Intensive week

Last day to enrol

Census date

Academic session

STAT6039 – Mathematical Statistics

TBA

15/1 – 16/3

12 – 16 Feb

19/1

2/2

Summer

COMP7240 – Introductory Databases

Qing Wang

12/3 – 11/5

9 – 13 April

16/3

30/3

Summer

STAT7026 – Graphical Data Analysis

Francis Hui

STAT7001 – Applied Statistics

Grace Chiu

28/5 – 27/7

25 -29 June

1/6

15/6

Autumn

SOCR8201 – Introduction to Social Science

Steve McEachern

COMP8420 – Bio-inspired Computing

Tom Gedeon

COMP7230 – Programming for Data Scientists

Armin Haller

6/8 – 5/10

3 -7 Sept

10/8

24/8

Winter

COMP8410 – Data Mining

Kerry Taylor

SOCR8204 – Advanced Social Science Approaches

TBA

STAT7055 – Introductory Stats for Business and Finance

Gen Nowak

1/10 – 30/11

29 Oct – 2 Nov

5/10

19/10

Spring

SOCR8202 – Using Data to Answer Policy Questions

Nick Biddle

COMP8600 – Statistical Machine Learning

TBA

Application deadlines for each of these academic sessions are available through the ‘course search’ function on the UAC postgraduate website.

 

Frequently asked questions

Click here to download a PDF of these FAQs.

Admission:

What makes the Applied Data Analytics program different to similar degrees elsewhere?

The ANU’s Applied Data Analytics program differs from other similar programs in that it offers both a broad-ranging curriculum and an opportunity to develop specialised skills in one particular area. Students enrolling in the Graduate Diploma or Masters are required to complete courses across three different academic disciplines – computer science, social science and statistics. Students cannot complete the program without attaining a certain level of proficiency in all three areas, resulting in graduates with an integrated and well-rounded understanding of up-to-date data analysis. Masters students are also able to specialise in a discipline of their choosing, taking higher-level courses to give them the expertise they need in their professional fields. The ANU’s program also focuses on real-world applications of data analytics techniques and toolsets, equipping graduates with the academic background to tackle practical problems.

 

Is the program open to international students?

The program is not currently available to international students. The ANU is in the process of securing CRICOS accreditation from the Australian government Department of Education, which will enable us to enrol international students. However, the program will not be open for international enrolment before January 2018. International applicants should monitor the Applied Data Analytics website, where an announcement will be posted once a decision is reached later this year.

 

What are the admission requirements for the non-award, Graduate Diploma and Masters programs?

Applicants for the Graduate Diploma must hold either an Honours degree (AQF8) or a Bachelor degree (AQF7) plus one year’s relevant work experience. Applicants for the Masters must hold a Bachelor degree plus three years’ relevant work experience.

Applicants with a completed higher qualification (e.g. Masters, PhD) are admissible to either program. Applicants to the non-award program must meet the entry requirements of the Graduate Diploma.

Please note that there is no discipline-specific requirement for this program – applicants are eligible with a Bachelor degree in any field. For information on the Australian Qualifications Framework, see here.

 

What is the difference between ‘award’ and ‘non-award’ programs?

Both the Graduate Diploma and the Masters are ‘award’ programs – this means that they are programs of study which enable the student to graduate with a formal academic award, once all requirements are met.

The non-award program enables students to enrol in any course which interests them and for which they meet the pre-requisites. The completion of non-award courses does not enable students to graduate with a Graduate Diploma or a Masters, regardless of how many courses they complete. However, students who have completed non-award courses which are part of the Applied Data Analytics program are able to request academic credit for these courses, should they subsequently enrol in the Graduate Diploma or Masters programs.

 

Am I admissible if I don’t have a Bachelor degree?

Applicants who do not have a Bachelor degree may be eligible for admission to the program on the basis of professional equivalency. This means that the applicant has been assessed by the relevant delegated authorities as meeting the academic entry requirements without completing formal university study.

Applicants wishing to discuss whether they are eligible for admission without a degree should consult the program convenor, Assoc. Prof. Kerry Taylor (Kerry.Taylor@anu.edu.au).

 

What is the ANU’s English language policy, and to whom does it apply?

The ANU’s English language policy applies to all students of the ANU, whether domestic or international. The policy is available here: https://policies.anu.edu.au/ppl/document/ANUP_000408

Australian citizens and permanent residents whose university studies were conducted in countries other than those listed under ‘Group A’ in the above policy will be required to provide evidence of English language competency. This policy applies regardless of whether you have separately had to meet these requirements for the Department of Immigration and Border Protection.

Holding Australian citizenship or being employed by an Australian organisation does not constitute grounds for admission under the English language policy – applicants must provide an approved form of evidence, as outlined in the policy above.

 

How do I apply for the non-award program?

Applications for the non-award program are lodged directly through the ANU, at the following link: https://apollo.anu.edu.au/apollo/default.asp?pid=9347&script=true

Applicants must ensure that they submit all required documentation to the email address dataanalytics.cecs@anu.edu.au as soon as they have submitted their application. Required documentation is described below, and on the application form.

 

How do I apply for the Graduate Diploma or Masters program?

Applications for the Grad. Dip. and Masters programs are lodged through the Universities Admissions Centre (UAC): http://www.uac.edu.au/postgraduate/

Applicants should use the ‘course search’ function to identify the ANU’s Data Analytics programs, which have the following UAC numerical codes:

  • Graduate Diploma – UAC ID: 830813
  • Masters – UAC ID: 830812

 

What are the application closing dates for each academic session?

There is a different application closing date for each academic session of the year (Winter, Spring etc.). The closing dates for all 2017 sessions are available under the ‘course search’ function on the UAC website: http://www.uac.edu.au/postgraduate/

 

What documents do I need to include with my application?

You will need to include colour scans of original academic transcripts, as well as an up-to-date copy of your curriculum vitae and of any change-of-name documentation (e.g. marriage certificate). Please note that greyscale scans of academic transcripts are not acceptable, nor are colour photographs of original transcripts.

 

How do I know if my application has been successful?

You will receive an email from the ANU’s Admissions team within three weeks of your application date, to the email address nominated on your application. Depending on the information you provided, you may receive either a full offer for study or a conditional offer for study. Conditional offers for study require you to meet certain conditions before you can accept the offer.

 

Teaching and assessment:

Why doesn’t the Applied Data Analytics program teach in normal university semesters?

Because the Applied Data Analytics program was designed to be taught in online intensive mode for working professionals, normal university semesters aren’t suitable for this teaching format. Instead of being constrained by fixed dates for Semesters 1 and 2, the Data Analytics program teaches courses staggered throughout the year in the ANU’s non-standard teaching sessions – Autumn, Winter, Spring etc. This model is also used by other areas in the ANU which teach specialised professionally-focussed courses, including the College of Law and the Crawford School of Public Policy.

 

What is an ‘academic session’?

An academic session is the specific period of the academic year to which a course belongs. The Data Analytics program teaches in non-standard academic sessions, which are seasonal:

  • Summer – January to March
  • Autumn – April to June
  • Winter – July to September
  • Spring – October to December

These sessions run in parallel with Semesters 1 and 2. Semester 1 runs from January to June, at the same time as the Summer and Autumn sessions; Semester 2 runs from July to December, at the same time as the Winter and Spring sessions.

Students need to know in which academic session their course is being taught, because this is how you make sure you’re enrolling in the right course in your ISIS account. Once you’ve successfully added a course to your enrolment, the course will display on the ISIS home screen under the relevant academic session.

 

How do online intensive courses work?

As described in the tab above labelled ‘Delivery mode’, online intensive course in the Applied Data Analytics program are run over nine weeks. The first four weeks comprise online learning and assessment through the ANU’s Wattle portal; the fifth week is spent full-time on campus at the ANU; and the final four weeks are a further period of online teaching and assessment. We call this mode 4+1+4. Therefore, eight weeks of each course can be completed online from anywhere, and one week must be spent on campus in Canberra.

 

Can I take two online intensive courses which are running simultaneously?

Students cannot take two intensive courses with same start and finish dates. This is because they will have the same on-campus intensive week, and students can only attend one of these at a time. Where courses are offered concurrently (e.g. Spring 2017 – STAT7055 / SOCR8202), students will be required to choose one or the other.

 

Can students complete the program in full-time mode?

At present, it is not possible for students to undertake the online intensive program in full-time mode. The maximum number of online intensive courses available is five per annum – this is slightly more than a 50% study load.

Students who reside in Canberra and who are prepared to enrol in a mixture of online courses and traditional on-campus semester-long courses are able to complete the program in full-time mode. For advice about how to mix on-campus and online courses, please contact dataanalytics.cecs@anu.edu.au.

 

Is the on-campus intensive week compulsory?

Yes, the on-campus intensive week is compulsory for all students. Exceptions will be made only in the case of serious and unforeseen circumstances (e.g. sudden illness, accident or bereavement). Otherwise, all students are expected to participate in all aspects of the on-campus intensive week, and students will not be given exemption on the grounds of professional workload. The intensives are a crucial part of learning development and cohort-building for all courses, and students should fully consider this expectation when choosing to undertake the program.

 

What is the timetable for the 2017/2018 online intensive courses?

The online intensive timetable is available at the above tab labelled ‘course schedule’.

Students wishing to consult the timetable for courses taught in on-campus semester-long mode should consult the relevant Programs and Courses entry for their course.

 

What is the assessment structure for each course?

The assessment structure varies from course to course, depending on the individual convenor and on the material being taught. However, all ANU courses include multiple forms of assessment, both formative (identifying areas of academic strength and weakness) and summative (finding out how well you’ve achieved the learning outcomes of the course). You can expect to be assessed in different ways throughout the course, not just at the end. Many courses will include some form of group assessment, reflecting the real-world concerns of the program, because large-scale data analysis is often performed in a team environment.

 

How do I find out if a particular course is suitable for my skill-set?

You should consult the relevant Programs and Courses entry for the course in question, which will provide you with a summary of the course material, and of the learning outcomes for the course. You may also consult the convenor for the relevant course, who will be able to answer specific questions and to provide you with a course outline.

 

How do I know what the pre-requisites are for any particular course?

Pre-requisites and any assumed knowledge are detailed on the Programs and Courses entry for each course. Below is a table of the pre-requisites for the courses included in the Applied Data Analytics program.

 

Please note that these pre-requisites only apply to students enrolled in the Applied Data Analytics program; students enrolled in other programs may be subject to other pre-requisites, as detailed on Programs and Courses.

 

Course

Pre-requisites

Stage One

STAT7055 Intro Stats for Business

 

STAT7001 Applied Statistics

STAT7055

COMP7230 Programming for Data Scientists

 

COMP7240 Introductory Databases

 

Stage Two

SOCR8201 Introductory Social Science

 

SOCR8202 Using Data to Answer Policy Questions

 

STAT6039 Mathematical Statistics

STAT7055 or STAT7001

STAT7026 Graphical Data Analysis

STAT7055

COMP8410 Data Mining

COMP7240

COMP8430 Data Wrangling

COMP7230 and COMP7240

Stage Three

SOCR8203 Advanced Social Science Techniques

SOCR8201 or SOCR8202

SOCR8204 Policy Development and Service Delivery

SOCR8201 or SOCR8202

SOCR8006 Online Research Methods

SOCR8201 or SOCR8202

SOCR/DEMO8082 Social Research Practice

SOCR8201 or SOCR8202

STAT7016 Bayesian Data Analysis

STAT7001 and STAT6039

STAT7030 Linear Models

STAT7001 and STAT6039

STAT7040 Statistical Learning

STAT7001 and STAT6039

STAT8002 Applied Time Series Analysis

STAT7001 and STAT6039

COMP6490 Document Analysis

COMP7230 and COMP7240 and COMP8410 and STAT6039

COMP8420 Bio-Inspired Computing

COMP7230 and COMP7240 and STAT6039 and either COMP8410 or COMP8430

COMP8600 Machine Learning

COMP7230 and COMP7240 and COMP8410 and STAT6039

 

Fees:

Is this program eligible for a FEE-Help loan?

Australian citizens enrolled in this program are eligible to defer their tuition fees through a FEE-Help loan. New Zealand citizens and Australian permanent residents can access FEE-Help in certain specific situations, which are outlined here.

Australian citizens who wish to defer their tuition through FEE-Help must complete an electronic Commonwealth Assistance Form (eCAF). This form is available to students through their ISIS account (the ANU’s student enrolment and information portal) once they have accepted their offer for study.

 

What is the ‘census date’ for each course?

The census date for a course is the date at which students are regarded as liable for their tuition fees, regardless of whether they complete the academic requirements of the course. Up to census date, students can ‘drop’ a course online through their ISIS account without incurring any financial penalty. Students dropping a course after census date will remain liable for the tuition. The only exception to this is students who are approved for late withdrawal from a course. Late withdrawals apply to students who encounter sudden and unexpected circumstances, which prevent them from completing the course (e.g. serious illness, accident or bereavement).

The census date for a course is determined by its starts date – the census date occurs roughly 1/3 of the way through the course. The census dates for all of the 2017 and 2018 courses are listed under the above tab labelled ‘course schedule’.

For courses taught in typical semester-long on-campus mode, census dates remain the same every year: 31 March for Semester 1 courses, and 31 August for Semester 2 courses.

 

Are there any Commonwealth Supported Places (CSPs) for this program and how do I apply?

In 2017, the College of Engineering and Computer Science has only one Commonwealth Supported Place (CSP) for the whole of the Applied Data Analytics program. This place is awarded strictly on the basis of academic merit, and applicants should be aware that competition is fierce. A GPA of 6.5 or above is recommended to have a realistic chance of securing a CSP.

From Semester 2, 2017, the situation with CSPs may change, due to reforms announced by the Australian government in the 2017/2018 federal budget. The ANU is yet to establish precisely what effect the changes will have on the number and allocation of CSPs for individual academic programs, but students should expect that CSPs will become more difficult to get from 2017 onwards, possibly incorporating time-limits for students to complete their programs.

Applications for CSPs are considered once per semester. Applicants must complete and submit the form available at the following link in order to be considered for a CSP: https://cecs.anu.edu.au/study/commonwealth-supported-places

Please note that applicants must have received an unconditional offer for study to the Applied Data Analytics program, or be already enrolled, before they can be considered for a CSP.

 

What is the difference between the Services and Amenities Fee (SAF) and Domestic Tuition Fees (DTF)?

The Services and Amenities Fee (SAF) is incurred by every student enrolled at the ANU. This fee is a contribution by students towards the cost of the ANU facilities which are available to students: physical buildings as well as information services and other online study supports. Students are charged SAF at a rate of AUD $73.50 per seasonal academic session, up to a maximum of AUD $147 per annum for part-time students or AUD $294 per annum for full-time students (2017 rate).

Domestic Tuition Fees (DTF) are the cost of individual academic courses – these are the fees students incur by being enrolled in a particular course. Students will see two different items on their tuition invoices: SAF is the service and administration component, and DTF is the academic tuition component. Neither component is negotiable, although both can be deferred under the FEE-Help program by Australian citizens and eligible permanent residents and New Zealand citizens.

 

How can I organise for my employer to pay my tuition fees?

Students whose employers wish to pay their tuition invoices need to set up a tuition sponsorship through the ANU’s Student Finance team. Sponsored students will not receive a tuition invoice through their ISIS accounts – the invoice goes straight from ANU Student Finance to the nominated contact at the sponsoring organisation. Students who have a tuition sponsorship will not be charged late fees if their sponsor does not pay by the due date on the invoice – the ANU’s Student Finance team negotiates directly with the sponsor for payment.

Students who choose to download their invoices from ISIS and to provide these to their employers for payment should note that the student will be charged late fees if payment does not arrive by the due date. This is because the above arrangement does not constitute a tuition sponsorship, even if the student has provided the invoice to their employer. From the ANU’s point of view, students are considered to be self-funded unless they have an official ANU tuition sponsorship in place.

Students who do not wish to set up a tuition sponsorship are recommended to pay their own tuition by BPay or credit card through their ISIS account, and then to seek retroactive reimbursement from their employer. This arrangement minimises the chance that students will be left with additional fees if an employer does not pay the invoice on time.

 

How do I view and pay my tuition fee invoice?

From the home screen in ISIS, students should select ‘check your invoice’ to see their most recent outstanding invoice. Students wishing to consult previous invoices should click ‘invoice history’ for a full list.

 

What are the indicative tuition fees for each course?

Indicative tuition fees for each academic year are available under the relevant course entry on the Programs and Courses website.

Students should expect that tuition will increase by roughly 5% per annum. For 2017, indicative fees are AUD $3660 per course.

 

What does DTF cover?

Domestic Tuition Fees (DTF) are the cost payable for enrolling in the course. DTF does not include the cost of any textbooks (which not all courses will require), or the cost of travelling to and attending the on-campus intensive component.

 

Am I Centrelink-eligible while studying in the Applied Data Analytics program?

The Applied Data Analytics program is not considered an approved coursework Masters program for the purposes of student-support payments from Centrelink. However, students who believe they qualify for Austudy or for Youth Allowance (Student) should contact the Department of Human Services directly for clarification.

 

General questions:

Do I need a student card?

Students who do not reside in the ACT and who do not intend to use ANU infrastructure during their degree may not need a student card. However, we recommend that all students acquire a student card just in case – these can be obtained in person during one of your on-campus intensive weeks. Student cards are available over the counter with approved photo identification (e.g. passport, driver’s licence) from the ANU Student Central services counter at 121 Marcus Clarke Street.

Please note that students must have accepted their offers for study and be enrolled in a course before they are eligible to hold a student card. Interstate students can request that a student card be mailed to them, subject to certain restrictions.

Students who reside in the ACT and who wish to use ANU library or computer laboratory facilities will need a student card to access the relevant buildings.

 

What are the arrangements for the on-campus intensive week for each course?

Arrangements for the on-campus intensive week vary from course to course, but students should expect to be on campus every day (Monday to Friday) from 0900 to 1700. Details of the building locations and timetables for the intensive weeks will be emailed to all enrolled students one week in advance, along with maps of the ANU campus and other information to assist those new to Canberra and to the ANU.

 

Can I get recognition of prior learning (RPL) for some of my previous university study?

You may be eligible for recognition of prior learning (RPL) if you have completed related studies in a previous university degree. At the ANU, RPL is called academic status and takes two forms – credit and exemption.

As described in the above tab labelled ‘credit’, students who are awarded credit will have their total academic program shortened by the number of course for which they are granted credit. In other words, they are regarded as having completed the courses in question.

Students who are granted exemption do not have to take the course for which they have been granted exemption, and are permitted to replace the exempted course with another course of their choosing, from an approved list of program inclosures. A student who receives exemption does not have their program shortened.

For more information about the restrictions on the awarding of academic status, please see section 3.1 of the Coursework Awards Rule: https://www.legislation.gov.au/Details/F2016L01980

Some examples of credit and exemption scenarios:

  • Student A has worked in the Australian Public Service for 15 years but has no formal qualifications. He has had significant experience in programming and is able to demonstrate this. On the basis of his work experience, he is admitted to the Master of Applied Data Analytics. He does not receive any credit, thus the duration of his program remains the same. Due to his experience in programming, he is able to get an exemption for COMP7230, a compulsory course, which he chooses to replace with another course under the ‘Data Science’ category.

 

  • Student B has worked in the Australian Public Service for 5 years and has completed a Bachelor of Economics within the last 10 years. After submitting a credit application – including his transcript and course outlines – he is granted credit for STAT7055. He is not required to replace this course with another and so his program is shortened by one course.

 

  • Student C applies to study three courses included in the Master of Applied Data Analytics as a non-award student. She has completed a Master of Computing within the last 10 years. She is not able to receive credit as she is studying as a non-award student. However, as she is considering applying for the Master of Data Analytics later, she seeks advice regarding the credit she would be eligible for. She is told that she would receive credit for COMP7230 and COMP7240 and so does not have to complete these courses. When she transfers into the Masters program, the duration of her course will be shortened by two courses, plus whatever courses she has undertaken as a non-award student.

 

 

Does the ANU consider MOOCs for admission and/or recognition of prior learning?

If the MOOC for which the student is seeking credit is determined to be equivalent in learning and assessment to a 6-unit ANU course, then the ANU may choose to grant credit, at the discretion of the program convenor. For a MOOC to be credit-eligible, it must be taught by a reputable university, as defined by the Australian government Department of Education and Training’s policy on the recognition of overseas qualifications.

 

Can I appeal a decision about academic credit?

If you are unhappy with the decision of the College regarding your application for academic status, you can appeal to the Associate Dean (Education) for the relevant College. The Data Analytics program teaches across three ANU Colleges – Business and Economics, Engineering and Computer Science, and Arts and Social Sciences – and each College has its own AD(E).

Applications to appeal a credit decision must be submitted in writing to dataanalytics.cecs@anu.edu.au, and the Data Analytics team will identify the correct College for the resolution of your appeal.

 

Information for members of the Australian Public Service:

Do I need departmental support/endorsement to enrol in this program?

You do not need departmental support to enrol in the Data Analytics program – if you meet the admission requirements, then you are eligible.

However, if you wish to access study leave or other flexible arrangements through your employer, then the ANU recommends consulting your professional development or learning support contacts before enrolling in the program.

 

If my department has encouraged me to apply for this program, and has coordinated my application process, does this mean my tuition is fully funded?

Students should not assume that their tuition is employer-funded unless they have specifically consulted their employer about tuition arrangements, and have received written assurance that the employer has entered into a tuition sponsorship with the ANU. The ANU will not solicit tuition sponsorships on behalf of students. It is the student’s responsibility to secure the employer’s financial support, at which point the Data Analytics team (dataanalytics.cecs@anu.edu.au) is happy to assist with the logistics of setting up the sponsorship.

Students should check their ISIS account regularly to see whether they have any invoices owing. Even if you are a sponsored student, it’s a good idea to keep an eye out for invoices, because an unexpected account is a sign that something may not be functioning correctly with your sponsorship.

 

Can APS students access FEE-Help?

APS students can access FEE-Help, provided that they are Australian citizens. Permanent residents and New Zealand citizens are able to access FEE-Help in certain limited situations, as explained above.

 

Glossary of useful ANU terms:

Award A qualification conferred by the University and certified by a testamur.
  • Award names and relevant specialisations appear on a graduate's testamur.
  • Different plans may lead to different awards though some lead to the same award.
Program In an academic sense , a program is a structured sequence of study - normally leading to the Award of one or more degrees, diplomas or certificates. In a system sense, a program is a grouping of one or more academic plans around a particular theme, Awards, or set of admission requirements.
Course A subject of scholarly study taught:
  • In a connected series of lectures or demonstrations
  • By means of practical work including the production by students of essays or theses or case studies, or the attendance and participation by students in seminars or workshops
Each course requires a course outline.
A four character alphabetic subject area code and a four digit numeric catalogue number identify each course. The first digit denotes the state/year of the program in which the course is normally taken. Each course is normally assigned a unit value that is a measure of the proportion of the academic progress that a course represents within the total credit for the program
Unit This is an indicator of the value of the course within the total program. Most courses are valued at 6 units. Units are used to track progress towards completing a plan. Full -time students normally undertake 24 units of courses each semester.
Non - award study Study that does not lead to the award of a degree, diploma, or certificate, but consists of a course or work requirement that may be at undergraduate or graduate coursework level. [Not e: non- award study does not include studies undertaken on a non- award basis within the meaning of HES Act.]
Credit The granting of credit is an evaluation process that assesses the individual's prior formal, non- formal, and informal learning to determine the extent to which the individual has achieved the required learning outcomes, competency outcomes, or standards for entry to, and/or partial or total completion of, a qualification.
Exemption Some students may be exempt from undertaking a compulsory c ourse for the program on the basis of previous completion of the course, or an equivalent course. However, a course of equivalent unit value must be substituted. An exempted course counts towards program requirements and satisfied pre- requisite requirement s for other courses but the unit value of the exempted course does not count towards the units taken towards the program.

Other terms can be found in the Student policies and procedures glossary

Updated:  8 September 2015/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing