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 some of our data program courses in semester format and blended intensive format courses. Intensive courses are mostly remote learning with only 1 face-to-face learning week, and provide the same learning outcomes. Due to small enrolments in blended intensive classes, computing classes are offered in semester mode only in 2024. More information about blended intensive courses and frequently asked questions can be found below.
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.
Study plans
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.
Master of Applied Data Analytics
Computing/Statistics focus for students commencing their studies in Semester 1
STAT6039 is an allowed alternative to STAT8130 (providing approval has been granted by your program convenor) and is required pre-requisite course for the more quantitative computer science and statistics electives.
Students planning to study STAT6039 should email their program convenor at MADAN.convener.comp@anu.edu.au to apply for approval to swap STAT8130 with STAT6039 in the compulsory studes list.
| Year 1, Semester 1 |
|---|
| COMP6730 Programming for Scientists |
| COMP7240 Introduction to Database Concepts |
| STAT7055 Introductory Statistics for Business and Finance |
| SOCR8201 Introduction to Social Science Methods and Types of Data |
| Year 1, Semester 2 |
|---|
| COMP8410 Data Mining (Winter) |
| COMP8430 Data Wrangling |
| STAT6026 Graphical Data Analysis |
| STAT6039 Principles of Mathematical Statistics |
| Year 2, Semester 1 |
|---|
| STAT7038 Regression Modelling |
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy (Autumn) |
| Elective: e.g., COMP8600 Statistical Machine Learning |
| Elective: e.g., STAT8002 Applied Time Series Analysis |
Social Science focus for students commencing their studies in Semester 1
| Year 1, Semester 1 |
|---|
| COMP6730 Programming for Scientists |
| COMP7240 Introduction to Database Concepts |
| STAT7055 Introductory Statistics for Business and Finance |
| SOCR8201 Introduction to Social Science Methods and Types of Data |
| Year 1, Semester 2 |
|---|
| COMP8430 Data Wrangling |
| STAT7038 Regression Modelling |
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy |
| Elective: e.g., SOCR8006 Online Research Methods |
| Year 2, Semester 1 |
|---|
| STAT8130 Generalised Linear Modelling (Summer) |
| COMP8410 Data Mining |
| STAT6026 Graphical Data Analysis (Autumn) |
| Elective: COMP6990 Document Analysis (Late Autumn) or STAT6039 Principles of Mathematical Statistics) |
Graduate Diploma of Applied Data Analytics
For students commencing their studies in Semester 1
Alternative course: STAT6039 Principles of Mathematical Statistics
There is an alternative course available, STAT6039, which is not on the DADAN Study Requirements. STAT6039 is an allowed alternative to STAT8130 (providing approval has been granted by your program convenor) and is recommended for students going on to MADAN, especially those with strong maths skills, as it is required for some of the MADAN electives in computer science and statistics.
Students planning to study STAT6039 should email MADAN.convener.comp@anu.edu.au to apply for approval to swap STAT8130 with STAT6039 from their program convenor
| Year 1, Semester 1 |
|---|
| COMP6730 Programming for Scientists |
| COMP7240 Introduction to Database Concepts |
| STAT7055 Introductory Statistics for Business and Finance |
| SOCR8201 Introduction to Social Science Methods and Types of Data |
| Year 1, Semester 2 |
|---|
| COMP8410 Data Mining (Winter) |
| STAT7038 Regression Modelling |
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy |
| STAT6039 Principles of Mathematical Statistics |
Graduate Certificate of Applied Data Analytics
Students can study either full time using a mix of semester and intensive classes, or part time with either intensive and/or semester classes.
We recommend that students do NOT take any other course at the same time as our Python course COMP6730/COMP7230, if they are new to programming or find it difficult. COMP6730/COMP7230 will provide a strong foundation that requires some students to devote more hours than others.
For students commencing their studies in Semester 1
As many students study part time while working, below is a study plan for part time study with intensive classes starting in Semester 1.
The CADAN program is easist if students take 1.25 years to complete the program. Alternatively students can change their program in various ways to complete within 1 year. For example, studying SOCR8201 in Semester 1 or SOCR8202 in Semester 2.
| Year 1, Autumn |
|---|
| COMP7240 Introduction to Database Concepts |
| Year 1, Winter |
|---|
| COMP7230 Introduction to Programming for Data Scientists |
| Year 1, Spring |
|---|
| STAT7055 Introductory Statistics for Business and Finance |
| Year 2, Autumn |
|---|
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy |
For students commencing their studies in Semester 2
As many students study part time while working, below is a study plan for part time study with intensive classes starting in Semester 2.
For students starting in Semester 2, the CADAN program is easist for part time students to combine with other commitments if they take 1.5 years to complete the program. Alternatively students can change their program in various ways to complete within 1 year. For example, studying SOCR8201 in Semester 1 which will not clash with COMP7240.
| Year 1, Winter |
|---|
| COMP7230 Introduction to Programming for Data Scientists |
| Year 1, Spring |
|---|
| STAT7055 Introductory Statistics for Business and Finance |
| Year 1, Autumn |
|---|
| COMP7240 Introduction to Database Concepts |
| Year 2, Spring |
|---|
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy |
Electives
Computing
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
Students intending to study statistics electives are required to study STAT6039 Principles of Mathematical Statistics and STAT7038 Regression Modelling which are pre-requisites for all of the Statistics electives.
Social Science
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.
2024 Course schedule
This schedule shows blended mode courses of GCDE, CADAN, DADAN and MADAN for the 2024 current academic year, including the dates of the intensive week.
| Course | Convener | Course Dates |
Intensive Week |
|---|---|---|---|
| Summer | |||
| STAT8130 Generalised Linear Models | Dr Francis Hui | 15 January– 15 March |
12–16 February |
| SOCR8204 Causal Inference | Prof Nicholas Biddle | 8 January– 8 March |
5–9 February |
| Autumn | |||
| STAT6026 Graphical Data Analysis | Dr Priya Dev | 11 March– 10 May |
8–12 April |
| SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy | Prof Matthew Gray | 11 March– 10 May |
8–12 April |
| Autumn (late) | |||
| STAT7038 Regression Modelling | Dr Francis Hui | 27 May– 26 July |
24–28 June |
| Winter | |||
| SOCR8203 Advanced Techniques in the Creation of Social Science Data | Dr Markus Hahn | 5 August– 4 October |
2–6 September |
| Spring | |||
| STAT7055 Introductory Statistics for Business and Finance | Dr Priya Dev | 30 September– 29 November |
28 October–1 November |
| SOCR8201 Introduction to Social Science Methods and Types of Data | Assoc Prof Steve McEachern | 30 September– 29 November |
28 October–1 November |
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 enrolling in two different classes with the same intensive week.
Format
Blended intensive courses follow 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 not to 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, may include 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).
Frequently asked questions
Who can enrol in an intensive courses?
The blended courses are specifically designed for students in the postgraduate Applied Data Analytics and Social Research programs, who are typically transitioning into data analysis from other professions, or deepening their data skills. Students from other programs are only admitted under special circumstances, by application through their program convenor.
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.
How can I get a permission code to enrol in a course?
A permission code to enrol in a course can be obtained by completing an online Permission Code Request Form from the respective College Student Service teams:
- COMP intensives: CECS—Student Enquiries studentadmin.cecs@anu.edu.au
- STAT intensives: CBE—Student Enquiries info.cbe@anu.edu.au
- SOCR intensives: CASS—Student Enquiries students.cass@anu.edu.au
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.