Master of Data Science Academic Experts Computer Science Anders - - PowerPoint PPT Presentation

Master of Data Science

Academic Experts

• Computer Science

– Anders Eriksson – Sen Wang

• Maths/Stats

– Yoni Nazarathy – Ian Wood

You can ask questions on chat

The computational, statistical and mathematical methods of solving “big data” problems define Data Science.

What is Data Science?

• Enormous opportunities for data scientists to revolutionise

the way we work, live and communicate.

• Skills shortage and marked increase in demand for competent

data scientists.

uq.edu.au

Why Study Data Science?

First in Australia to offer an advanced level of computing, statistics, mathematics and business knowledge applied in industry, government, social and scientific contexts. Emphasis on high level of graduate attributes through cross-disciplinary curriculum that includes ethical use of data, legal considerations for data science, and business communication. Hands-on experience with big data tools and technologies, industry projects and placements, leading to job-ready graduates.

UQ’s approach …

The program is taught by UQ’s world leading researchers in Statistics and Information Systems. Both ranked 5 - “well above world standard” in the 2015 Excellence in Research Round. UQ ranks 50-60 amongst the top 100 universities in the world. Our vast industry and alumni networks open global employment opportunities

The UQ advantage

1. A core covering the fundamental ideas in Data Science, including a large industry focused project. 2. Preparatory courses in computer science or mathematics and statistics. 3. Specialist courses in advanced aspects of data science. 4. Electives in fields where Data Science is applied.

What will you study?

Programs and Courses Basics

• Course refers to an individual subject. A

typical course is 2 units (#2) and presents a typical workload of 10-12 hours per week. Master of Data Science courses are grouped into four parts:

– Compulsory - Part A – Bridging - Part B1 – Specialist - Part B2 – Electives - Part C

• Program refers to degree. Master of Data

Science has two program offerings: #24 and #32. Difference lies in entry background, not in outcome

PART A (#16)

Course Code Units Course Title DATA7001 2 Introduction to Data Science DATA7002 2 Responsible Data Science DATA7201 2 Data Analytics at Scale DATA7202 2 Statistical Methods for Data Science DATA7703 2 Machine Learning DATA7901 2 Data Science Capstone Project 1 DATA7902/3 4/2 Data Science Capstone Project 2

PART B1

Course Code Units Course Title MATH7501 2 Mathematics for Data Science 1 MATH7502 2 Mathematics for Data Science 2 STAT7203 2 Applied Probability & Statistics INFS7907 2 Advanced Database Systems INFS7901 2 Database Principles CSSE7030 2 Introduction to Software Engineering

At most #10 (5 courses) units for #32 program; At most #4 (2 courses) units for #24 program

PART B2

At least #4 (2 courses) units

Course Code Units Course Title INFS7205 2 Advanced Techniques for High Dimensional Data INFS7410 2 Information Retrieval and Web Search DATA7203 2 Computational Models for Data Science INFS7203 2 Data Mining MATH7232 2 Operations Research & Mathematical Planning STAT7502 2 Advanced Statistics I MATH7202 2 Advanced Topics in Operations Research MATH7406 2 Control Theory STAT3006 2 Statistical Learning

Electives which explore fields where data science is used include:

PART C (Balance from ….)

• Concepts in Bioinformatics
• Applications of Computational

Statistics

• Advanced Bioinformatics
• Advanced Genome Informatics
• Epidemiology for

Biostatisticians

• Longitudinal and Correlated

Data

• Portfolio Management
• Fundamentals of Marketing
• Consumer and Buyer

Behaviour

• Market and Consumer

Research

• Principles of Econometrics
• Macro-econometrics for

Economics and Finance

• Financial Econometrics

Make a Study Plan

Entry path 1A:at least two first year university level calculus and linear algebra courses Entry path 1B: at least two first year university level programming and database courses Entry path 2: both of 1A and 1B (#24 unit students)

Entry path 1A (#32)

*students who have existing knowledge relating to these courses are encouraged to take DATA7703 in their first semester

Sem 2 Sem 1 Sem 2 Sem 1 DATA7001 - Introduction to Data Science DATA7202 – Statistical methods for Data Science DATA7901 - Capstone 1 DATA7902/3 – Capstone 2 CSSE7030 Introduction to Software Engineering* DATA7703 – Machine Learning DATA7002 – Responsible Data Science DATA7201 – Data Analytics at Scale STAT7203 Applied probability & statistics INFS7901 – Database Principles* INFS7907 – Advanced Database Systems Course from B2 or C MATH7502– Mathematics for Data Science 2 Course from B2 or C Course from B2 or C (extra course if DATA7903 taken)

Entry path 1B (#32)

*students who have existing knowledge relating to these courses are encouraged to take DATA7703 in their first semester

Sem 2 Sem 1 Sem 2 Sem 1 DATA7001 - Introduction to Data Science DATA7201 – Data Analytics at Scale DATA7901 - Capstone 1 DATA7902/3 - Capstone 2 INFS7907 – Advanced Database Systems MATH7501– Mathematics for Data Science 1* DATA7002 – Responsible Data Science DATA7202 – Statistical methods for Data Science Course from B2 or C Course from B2 or C STAT7203 Applied probability & statistics DATA7703 – Machine Learning Course from B2 or C Course from B2 or C MATH7502– Mathematics for Data Science 2 (extra course if DATA7903 taken)

Entry path 2 (#24)

Sem 2 Sem 1 Sem 2 DATA7001 - Introduction to Data Science DATA7901 - Capstone 1 DATA7902/3 - Capstone 2 INFS7907 – Advanced Database Systems DATA7201 – Data Analytics at Scale DATA7002 – Responsible Data Science STAT7203 Applied probability & statistics DATA7202 – Statistical methods for Data Science Course from B2 or C Course from B2 or C DATA7703 – Machine Learning (extra course if DATA7903 taken)

Ignorance is not a defense!

Get familiar with Academic Integrity at UQ

Don’t risk getting on the academic misconduct register

https://www.uq.edu.au/integrity/

Image source: https://www.pinterest.com.au/wassef87/academic-dishonesty-and-integrity/

Take Home

• Make a study plan now that gives you graduation eligibility in the

semester you plan to finish

• Note program rules and conditions on the different parts
• Consider your background (prior knowledge and/or course pre-requisites)

and timetable. Okay to check out courses and/or make an appointment with course coordinator

• Pay special attention if you are part time (International students need to

maintain full time student status i.e. 4 courses per semester) Advising sessions will be organized in the first 2-3 weeks of semester Sign up for ALLCOHORTS Piazza at piazza.com/uq.edu.au/other/allcohorts

Questions?