The Graduate Certificate (CADAN), Graduate Diploma (DADAN) and Masters of Applied Data Analytics (MADAN) and Graduate Certificate of Data Engineering (GCDE) are designed to suit working professionals studying data science and data engineering.

Semester or blended intensive learning
To support students at different career stages, we offer most of our data program courses in semester format and blended intensive format courses (aka ‘intensive’). Intensive courses are mostly remote learning with only 1 face-to-face learning week, and provide the same learning outcomes.

Transition into data science
The applied data analytics pathway to Masters trains students from foundational to deep data analytics skills and enables experts from other domains to learn the advanced data skills to become a data scientist in their domain knowledge area. We offer a unique blend of data science, statistics and social science to teach the techniques from each of those fields, and provide advanced electives in each field to allow students to specialise.

Nested degrees
The applied data analytics postgraduate programs comprise a set of nested degree programs allowing students to start their data science learning journey with a graduate certificate (24 credit units) or graduate diploma (48 credit units) and progress to the full masters program (72 credit units). Alternately, students in the higher degrees can exit earlier with a graduate certificate or Graduate Diploma as evidence of their new knowledge.

About blended intensive courses

  • Learning outcomes: blended intensive courses provide the equivalent learning outcomes and workload as the equivalent semester courses allowing either to be used as a pre-requisite for more advanced courses. Students can take a mix of semester and intensive courses, however they can take only one intensive course in each non-semester session to avoid a clash of intensive week studies.

  • Format: follows a 4+1+4 blended learning format of:
    • 4 weeks online learning
    • 1 full time week on campus learning (i.e. 9am-5pm) for labs and lectures in the middle of the course (typically week 5)
    • 4 weeks online learning

  • Take care to not slip behind as there is no mid-semester break in an intensive course.

  • Intensive week: the intensive week is when students typically put their learning into practice, often includes guest speakers, and provides a great opportunity for students to meet and work with their peers and share data analytics experiences in different industries. Attendance is required for each day of the mid-course intensive week. Make sure to take leave from other responsibilities and arrange to be in Canberra for the intensive week (see dates in Schedule).

Study patterns

MADAN and other students transferring to MADAN should take care to plan their studies to meet the pre-requisites for their intended electives. Please note, the advice here is a summary, and you should ensure your study plan meets the program rules in Programs and Courses.

Here some example recommended study patterns:

Computing electives
Students intending to study computing electives are required to study compulsory subject COMP8410 Data Mining or COMP8910 Data Mining and recommended to study STAT6039 Principles of Mathematical Statistics as both are pre-requisites for two of the computing electives COMP8600 Statistical Machine Learning and COMP8880 Computational Methods for Network Science

Statistics electives
Students intending to study statistics electives are required to study STAT6039 Principles of Mathematical Statistics and STAT6038 Regression Modelling which are pre-requisites for all of the Statistics electives.

Social science electives
Students intending to study the social science electives are required to study the compulsory courses SOCR8201 Introduction to Social Science Methods and Types of Data and SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy which are pre-requisites for social science electives SOCR8203 Advanced Techniques in the Creation of Social Science Data and SOCR8204 Causal Inference, respectively.

2023 Course schedule

This schedule shows both semester and blended intensive courses of GCDE, CADAN, DADAN and MADAN for the current academic year, including the dates of the intensive week dates for blended courses.

Choosing your courses
Several recommended study patterns will be added soon to aid in planning your studies. MADAN students should choose your elective studies and check their pre-requisites on Programs and Courses to ensure you have the required pre-requisites completed.

Recent changes

  1. Some courses have new course codes: STAT6014 Introduction to Bayesian Statistics, STAT6026 Graphical Data Analysis, STAT6030 Generalised Linear Modelling, STAT6040 Statistical Learning, COMP8910 Data Mining (intensive version of COMP8410), COMP8930 (intensive version of COMP8410).
  2. Some intensive week dates are adjusted: The start date of the autumn intensive week is adjusted for public holidays. SOCR8201 intensive week will be in week 6
  3. STAT6026 Graphical Data Analysis course dates on Programs and Courses are incorrect and will be updated soon - see correct dates below.
Course Convener Course
Dates		 Intensive
Week		 Enrolment
Deadline		 Census
Date
Summer
STAT6030 Generalised Linear Models Dr Luca Maestrini 10 January–
11 March		 6–10
February		 20 January 20 January
SOCR8204 Causal Inference Prof Nicholas Biddle 9 January–
10 March		 6–10
February		 20 January 20 January
COMP8930 Data Wrangling Prof Peter Christen 9 January–
10 March		 6–10
February		 9 January 20 January
Semester 1
COMP6730 Programming for Scientists> Brian Parker 20 February–
26 May		 NA 31 March 31 March
COMP8410 Data Mining Prof Kerry Taylor 20 February–
26 May		 NA 31 March 31 March
COMP8600 Statistical Machine Learning Prof Lexing Xie 20 February–
26 May		 NA 31 March 31 March
SOCR8201 Introduction to Social Science Methods and Types of Data Assoc Prof Steve McEachern 20 February–
26 May		 NA 31 March 31 March
STAT6038 Regression Modelling Abhinav Mehta 20 February–
26 May		 NA 31 March 31 March
STAT6039 Principles of Mathematical Statistics Dr Le Chang 20 February–
26 May		 NA 31 March 31 March
STAT6040 Statistical Learning Dr Yanrong Yang 20 February–
26 May		 NA 31 March 31 March
STA7055 Introductory Statistics for Business and Finance Dr Francis Hui 20 February–
26 May		 NA 31 March 31 March
STAT8002 Applied Time Series Dr Xuan Liang 20 February–
26 May		 NA 31 March 31 March
Autumn
STAT6026 Graphical Data Analysis Dr Priya Dev 13 March–
19 May		 11–17
April		 7 April 7 April
SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy Prof Matthew Gray 13 March–
19 May		 11–17
April		 7 April 7 April
COMP7240 Introduction to Database Concepts Assoc Prof Qing Wang 13 March–
19 May		 11–17
April		 13 March
Autumn (late)
STAT6038 Regression Modelling Dr Francis Hui 29 May–
28 July		 26–30
June
Semester 2
COMP6490 Document Analysis Dr Dawei Chen 24 July–
27 October		 NA 31 August 31 August
COMP8420 Neural Networks, Deep Learning and Bio-inspired Computing Dr Sabrina Caldwell 24 July–
27 October		 NA 31 August 31 August
COMP8430 Data Wrangling Prof Graham Williams & Dr Anushka Vidanage 24 July–
27 October		 NA 31 August 31 August
COMP8880 Computational Methods for Network Science Prof Lexing Xie 24 July–
27 October		 NA 31 August 31 August
SOCR8006 Online Research Methods Prof Robert Ackland 24 July–
27 October		 NA 31 August 31 August
SOCR8082 Social Research Practice TBA 24 July–
27 October		 NA 31 August 31 August
SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy Prof Matt Gray 24 July–
27 October		 NA 31 August 31 August
STAT6016 Introduction to Bayesian Data Analysis Dr Bronwyn Loong 24 July–
27 October		 NA 31 August 31 August
STAT6026 Graphical Data Analysis Prof Michael Martin 24 July–
27 October		 NA 31 August 31 August
STAT6030 Generalised Linear Modelling Dr Tao Zou 24 July–
27 October		 NA 31 August 31 August
STAT6038 Regression Modelling Dr Insha Ullah 24 July–
27 October		 NA 31 August 31 August
STAT6039 Principles of Mathematical Statistics Dr Xuan Liang 24 July–
27 October		 NA 31 August 31 August
STAT7055 Introductory Statistics for Business and Finance Dr Gen Nowak 24 July–
27 October		 NA 31 August 31 August
Winter
COMP7230 Introduction to Programming for Data Scientists Dr Michael McCulloch 7 August–
6 October		 4–8
September		 7 August 18 August
SOCR8203 Advanced Techniques in the Creation of Social Science Data Dr Markus Hahn 7 August–
6 October		 4–8
September
COMP8910 Data Mining Prof Kerry Taylor 7 August–
6 October		 4–8
September		 7 August 18 August
Spring
STAT7055 Introductory Statistics for Business and Finance Dr Priya Dev 2 October–
1 December		 30 October–3
November		 20 October 20 October
SOCR8201 Introduction to Social Science Methods and Types of Data Assoc Prof Steve McEachern 2 October–
1 December		 6–10
November		 20 October 20 October

Frequently asked questions

  1. Who can enrol in an intensive courses?
    The blended courses are specifically designed for students in the postgraduate Applied Data Analytics programs (Graduate Certificate, Graduate Diploma, and Masters), and the Graduate Certificate in Data Engineering, who are typically transitioning into data fields from other professions, or deepening their skills in data professions. Enrolment in these blended courses is limited to students from the ADAN and GCDE programs. Students from other programs are only admitted under special circumstances, by application through their program convenor.

  2. Do I need a permission code to enrol in an intensive course?
    • COMP intensives: For COMP intensive courses, permission codes are only required if you have not met the pre-requisites of the course.
    • SOCR and STAT intensives: these still require permission codes.
  3. How can I get a permission code to enrol in a course?
    A permission code to enrol in a CECS course can be obtained by completing an online Permission Code Request Form obtained from the respective school Student admin service to request a code.
  4. Can I take more than one intensive course in the same term? No. Attendance is required for each day of the intensive week, consequently to avoid clashing intensive week studies, students can only take 1 intensive in any term.

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