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?

The ANU is currently changing its admissions process for Data Analytics. In 2016, students applied directly to the ANU; from 2017, students will apply 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

Students applying for admission in 2017 will need to lodge an application through UAC: http://www.uac.edu.au/postgraduate/. Applications for 2017 programs open on 6 September 2016.

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 2016 for the Data Analytics cohort, and in Semester 2 2016 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.

Course schedule

Class Schedule

2016 Class Schedule:

Course Application Deadline Course Start Intensive Week Course Finish Course Census Academic Session
COMP7230 – Introduction to Programming for Data Scientists 18 July 8 August 5 -9 September 7 October 26 August 2650 - Winter
STAT7055 – Introductory Statistics for Business and Finance 12 September 3 October 31 October - 4 November 2 December 21 October 2670 - Spring

2017 Class Schedule:

Course Application Deadline Course Start Intensive Week Course Finish Course Census Academic Session
COMP7240 – Intro to Databases 19 December 16 January 13 -17 February 17 March 3 February 2720 - Summer
COMP8430 – Data Wrangling
or
STAT7026 – Graphical Data Analysis
13 February 6 March 3 -7 April 5 May 24 March 2720 – Summer
COMP8410 – Data Mining
or
SOCR8201 – Intro to Social Science
8 May 29 May 26 -30 June 28 July 16 June 2740 - Autumn
STAT7001 – Applied Statistics
or
COMP7230 – Programming for Data Scientists
17 July 7 August 4 -8 September 6 October 25 August 2750 – Winter
STAT7055 – Intro Statistics
or
SOCR8202 – Using Data to Answer Policy Questions
11 September 2 October 30 October - 3 November 1 December 20 October 2770 – Spring

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 Statistics 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 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 in real-world problems.

SS3 & SS4 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.

STAT5 & STAT6 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.

Big Data Statistics (STAT7017)

This course provides an overview of recent statistical theory that addresses topics such as high-dimensionality, large sample sizes, sequential prediction, incremental and parallel statistical learning. The goal of this is course is to build on the knowledge developed in Statistical Learning (STAT7040) in order to understand new and effective methods for analysing Big Data.

For more information about the courses in these programs, please download the Postgraduate Programs in Applied Data Analytics flyer.

Frequently asked questions

If you'd like to learn more about these programs or have some unanswered questions please feel free to explore our frequently asked questions.

Downloadable PDF of Glossary and FAQ

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

FREQUENTLY ASKED QUESTIONS

Application and Admission:

Are applications open for both the Applied Data Analytics 'award' and 'non -award' programs? Application s for the Masters, Graduate Diploma and non- award courses are open now. All three programs are running concurrently.

What if I don’t want to enrol in the full program but am interested in some courses?

A student can enrol as a non- award student in courses of interest to them, rather than in a formal award. The most significant issue with this is that the student will not have access to FEE -HELP, and so will need to pay the entirety of their tuition fees upfront. Students are able to enrol in a formal award (Masters o r Gradu ate Diploma) and then discontinue once they have completed the courses they are interested in.

When do I need to apply and accept by? Applications are now open and will close four weeks before the commencement of the individual courses.

For 2016, the dates are:

Course Application Deadline Acceptance Deadline
COMP7230: Introduction to Programming for Data Scientists 18 July 2016 1 August 2016
STAT7055: Introductory Statistics for Business and Finance 12 September 2016 26 September 2016

For 2017, the anticipated dates are:

Course Application Deadline Acceptance Deadline
COMP7240: Introduction to Database Concepts 19 December 2016 9 January 2017
COMP8430: Data Wrangling and
STAT7026: Graphical Data Analysis
13 February 2017 27 February 2017
COMP8410: Data Mining and
SOCR8201: Introduction to Social Science Methods and Types of Data
8 May 2017 22 May 2017
STAT7001: Applied Statistics and
COMP7230: Introduction to Programming for Data Scientists
17 July 2017 31 July 2017
STAT7055: Introductory Statistics for Business and Finance and
SOCR8202: Using Data to Answer Policy Questions
11 September 2017 25 September 2017
What are the entry requirements for each program, including pre-requisites and/or pre-admission requirements?
  • Masters program - Students should hold either an Honours degree or a Bachelor degree plus three years of work experience. Professional equivalency will be considered on a case- by- case basis.
  • Graduate Diploma - Students should hold either an Honours degree or a Bachelor degree plus one year of work experience. An equivalent combination of qualifications and experience will be considered .
  • Graduate Certificate - this is an exit -only qualification. Exit -only means that students cannot apply directly for a Graduate Certificate. They must instead register for a Grad. Dip. or Masters program, and can transfer into the Graduate Certificate if they choose to exit their program after completing four courses.
  • Non- Aw ard - There are no mandatory requirement s for entry to non- award courses. However, it is expected that students will have a similar background to those seeking entry to the Graduate Diploma.
What if I don't have a degree but I have significant relevant experience?

A combination of work experience and certification, or significant relevant experience can be used for admission to the Graduate Diploma. There must be documentary evidence of attainment of the learning outcomes of the Award for which admission is sought. Employment -based experience can be treated in conjunction with non- degree qualifications or certification. This may only be used towards Admission and exemptions, and not for course credit.

Do I need to meet English Language Requirements?

Yes. All applicants, whether domestic or international, must provide evidence that their English language ability meets the minimum requirements for admission. The English Language Requirements for Students policy outlines approved methods for meeting the English requirements . If you believe you do not meet the English language requirements please email dataanalytics@cecs.anu.edu.au for further advice.

How do I apply to the Data Analytics program ?

Applications for the Masters and Graduate Diploma are done online via a link on the top right of the following pages :

Masters: http://programsandcourses.anu.edu.au/2016/program/MADAN

Graduate Diploma: http://programsandcourses.anu.edu.au/2016/program/DADAN

Non- award applications can be submitted online at: https://apollo.anu.edu.au/default.asp?pid=8952

What do I need to provide with my application?

In order for us to make an accurately assess your application, please provide:

  • A copy of your current CV .
  • Academic transcripts and g raduation certificates (testamurs) all formal tertiary -level study .
  • Other documents which may support your application. For example, documents certifying a change of name, or providing explanations of any fail grades or academic exclusion.
How often will the courses be open to new students?

There is no fixed application an d/or admission period for this program. Non award students can apply on an ongoing basis for the course/s in which they are interested. The Master and Graduate Diploma programs will be available for any session in which there is a course offered.

This means that applications are accepted on a rolling basis, with students automatically considered for admission to the next available academic session. Alternatively, students can nominate a specific academic session in which they wish to begin their st udies.

When will applicants find out if they're successful - as applications are made, or at a set time prior to the course(s)?

Students will be notified if they have been successf ul for admission to the program as soon as their application has been as sessed by the ANU. Usually, this process takes a week or two; at very busy times of year (e.g. January, February, July) it may take three weeks.

Students will receive an email regarding the decision about their application, and a formal letter of offer w ill follow. This will be approximately one week after the email advice for Graduate Diploma and Masters students and approximately one week following the application deadline for Non- Award students.

What is the approach, process and cost for Recognition of Prior Learning (RPL)?

At ANU, RPL is called 'credit' and is governed by the ANU Credit Policy

ANU Credit policy extract:

Acceptable Credit applications must include evidence of attainment of the relevant learning outcomes and are categorised as follows:

  1. Formal learning, i.e., partial or total completion of an Australian Qualificati ons Framework qualification at Level 7 or higher, or an AQF Level 6 Associate Degree; and/or total completion of an AQF recognised RTO certificate, diploma, or advanced diploma.
  2. Non- formal learning, i.e., a successfully completed unit of learning that takes place through a structured program but does not lead to a formally recognised qualification (Could include non- accredited but assessed workplace courses run by a tertiary institution, or tertiary courses taken on a non- award basis).
  3. Informal learning, i.e., specific employment experience, volunteering, internship or workplace based training for which there is documentary evidence of attainment of the learning outcomes of the Award for which credit is sought. Employment based experience could be in conjunction with one or more of the learning categories above and/or secondary school certification. Informal learning may only be used towards Credit for Admission and exemptions, and not for course credit unless specified in the admission requirements of a professionally oriented Award.

As stated above, course credit can only be granted on the basis of previous Formal or Non- formal learning, not on the basis of informal learning, including work experience. However, relevant work experience can be used as a basis for admission into the Master or Graduate Diploma, or as a basis for course exemptions.

Only students enrolled in the Graduate Diploma and Masters programs will be eligible for credit, as students are unable to receive credit when undertaking non- award study. However , if current non- award students are considering undertaking the Graduate Diploma or Masters program in the future and believe they may be eligible for credit, they may wish to seek advice from the Data Analytics team .

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 Austra lian Public Service for 5 years and has completed a Bachelor of E conomics 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 cours e.
  • 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 t hese 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.

In order to assess the likelihood of receiving credit for previous studies, students should look at the content and learning outcomes of individual courses in the degree. Students should consider whether their previous studies have covered these topics; if so, credit may be sought.

The total amount of credit granted cannot exceed 50% of the program units , and can only be given where the previous study was undertaken within 7 years of admission to the program. Up to 25% of the program units can be credited on the basis of study in a B achelor degree, whereas up to 50% of the program units can be credited on the basis of study in an Honours degree.

There is no cost to students for RPL application.

Students must complete a credit application form and submit the relevant documents . This process can be done at the same time as applying for admission to the degree.

Students may send a general enquiry prior to application seeking an assessment of their likely credit , but this will only be formalised upon receipt of an official application.

How will applicants who have pa ssed MOOC courses be assessed for intake into the courses, and will these students be granted either credit or exemption from subjects?

Where the MOOC is from a reputable university ( see the Australian NOOSR Guidelines , section 1), and where the volume of learning and assessment is equivalent to that in an ANU 6 unit course, and where the university 'delivering' the MOOC would provide credit to students, the College may consider awarding either credit or exemption.

I feel my prior learning and work experience has not been assessed correctly. Can I appeal a decision for credit?

Yes, if you are unhappy with the decision of the College in relation to credit, you may appeal to the Associate Dean. The Data Analytics administration team . The following website also contains useful information: http://www.anu.edu.au/students/program- administration/program- management/get -course- credit -or-exemption

Special Application Information for staff of the Australian Public Service :

Will departmental endorsement of the each participant be required?

No. Departmental endorsement will only be required when an applicant for admission, is seeking financial sponsorship from their Departmen t for payment of course fees. In these cases, Departmental endorsement will be required befor e applicants will be assessed for admittance.

Participants paying for their own course fees do not require departmental endorsement for admission or enrolment.

Most APS agencies have an established nominations process in place for staff who want to undertake the courses while accessing financial assistance from their agency. This is a matter for the individual and their relevant agency.

found information about these courses through my Department. Does this mean my Department will fund my tu ition?

No. Tuition sponsorship is an internal process for your department/agency and you should take this up with your departmental contact prior to applying for admission or enrolment.

Enrolment , Delivery Mode and Tuition Fees :

Is the website live for enrolments now?

Students who have accepted their offers and received their University ID and passwords can enrol already through ISIS, the AN U's Interac tive Student Information System.

Isis login: https://isis.anu.edu.au/psp/sscsprod/?cmd=login

What is the delivery mode and what is the workload?

Data Analytics courses are designed to be delivered largely online, with only one week in each academic session requiring students to be present on the ANU campus in Canberra.

Typically, 6 -unit courses have an overall workload of 120- 130 hours . Data Analytics courses will be delivered in a 4+1+4 mode as follows:

  • 4 weeks of online pre -reading and preliminary study: online material, verification quizzes etc. The expected workload during this period is 10 hours per week .
  • 1 week of i ntensive on -campus sessions: tutorials, laboratory work, lectures, practical assessments. In this on- campus week, students can expect a workload of 40 hours .
  • 4 weeks of online assessments: designed to consolidate students' learning. As with the earlier online period, the expected workload in these four weeks is 10 hours per week .

Can I complete these courses via correspondence?

There is a one- week intensive component for each course that is compulsory. The balance of the course can be done by distance - there is no other face- to-face or on- campus attendance requirement.

Are the on-campus intensive periods compulsory ? And what happens if I am unable to attend?

Attendance is compulsory at all i ntensive on- campus components of Data Analytics courses . If yo u are in any doubt about your ability to attend, you should consider withdrawing from the relevant course prior to the census date for that course. The census date is the date after which enrolled students are deemed financially liable for a course, regardless of whether the student completes the course satisfactorily.

Information about census dates is available under 'Course Schedule ' on the Data Analytics website: https://cecs.anu.edu.au/study/dataanalytics

The actual content of the intensive week will differ from course to course, but is likely be a combination of practical tutorials, laboratory exercises, discussions, guest speakers etc.

What are the assessment components of each course?

Assessments for each course are currently under development, and will differ from course to course. Therefore, we cannot provide specific details.

The Programs and Courses entries for individual courses provide Learning Outcomes and Assessment criteria. Students wishing to enquire in more detail should contact the relevant course convenor by email. The name of the course convenor can be found under the relevant entry for your course on the Programs and Courses website: http://programsandcourses.anu.edu.au/Catalogue

When is the exam period for each course?

There will not be any specific exam period. Assessment will be ongoing throughout the course.

What are the average total costs for each program?

2016 tuition fees for courses undertaken as a student in the Masters, Graduate Diploma, and Graduate Certificate are as follows. Please note that fees are likely to increase by approx. 5% pa:

  • COMP and STAT courses: $3480 per course
  • SOCR8201: $3,480
  • SOCR8202: $3,054
  • SOCR8203: $3,480
  • SOCR8204: $3,054

Non- award fees are the same as the above but it should be noted that FEE HELP cannot be used to cover the cost of non- award fees. I t can only be accessed if students are enrolled in a formal award.

What do the fees cover?

The fees quoted are for tuition only , and do not cover the cost of travel to attend classes or other costs such as reference books , meal or travel costs, stationer y etc.

Can I access Centrelink benefits when undertaking the Master of Applied Data Analytics?

No, this program has not received approval to be added to the list of Masters Programs supported by Centrelink. We will explore this possibility in the future.

However, all Graduate Certificates and Graduate Diplomas are eligible for student income payments, including the Graduate Diploma of Applied Data Analytics.

Please contact Centrelink directly for further information.

I want to study full-time in 2016 and am happy to do some additional courses in on-campus non-intensive semester mode.

There are a small number of courses that are equivalent to those in the Applied Data Analytics degree and that can be taken in Semester 2, 2016 in a more traditional mode of delivery:

Semester 2, 2016 (July- Nov)

Course Requisite Information
COMP6240: Relational Databases Equivalent to COMP7240 - requires some intr oductory programming knowledge,
COMP6730: Programming for Scientists Equivalent to COMP7230 - note th is is being delivered in 2016 in the Applied Data Analytics programs
STAT6039: Principles of Mathematical Statistics has prerequisites
STAT7001: Applied Statistics has prerequisites
STAT7026: Graphical Data Analysis has prerequisites
STAT7040: Statistical Learning has prerequisites
STAT7016: Introduction to Bayesian Data Analysis has prerequisites
STAT7017: Big Data Statistics has prerequisites

When will classes be run?

The course schedule for the remainder of 2016 and for 2017 is appended below:

2016 Course Course Start Intensive Start Intensive Finish Course Finish Census Date Last day to enrol Academic Session
COMP7230: Introduction to programming for data scientists 8 August 5 September 9 September 7 October 26 August 19 August 2650 - Winter
STAT7055: Introductory statistics for business and finance 3 October 31 October 4 November 2 December 21 October 14 October 2670 - Spring
2017 Course Course Start Intensive Start Intensive Finish Course Finish Census date Last day to enrol Academic Session
COMP7240: Introduction to Database Concepts 16 January 13 February 17 February 17 March 3 Feb 27 Jan 2720 - Summer
COMP8430: Data Wrangling and
STAT7026: Graphical Data Analysis
6 March 3 April 7 April 5 May 24 March 20 March 2720 - Summer
COMP8410: Data Mining and
SOCR8201: Introduction to Social Science Methods and Types of Data
29 May 26 June 30 June 28 July 16 June 9 June 2740 - Autumn
STAT7001: Applied Statistics and
COMP7230: Introduction to Programming for Data Scientists
7 August 4 September 8 September 6 October 25 August 18 August 2750 - Winter
STAT7055: Introductory Statistics for Business and Finance and
SOCR8202: Using Data to Answer Policy Questions
2 October 30 October 3 November 1 December 20 October 13 October 2770 - Spring

Updated:  8 September 2015/Responsible Officer:  Head of School/Page Contact:  CECS Marketing