Event_
Sydney Hacky Hour
For researchers who code or analyse data
Come to Sydney Hacky Hour to swap notes, get help, or learn new techniques in programming and data science.
Hacky Hour is a regular meetup where all researchers – students, staff and university affiliates – gather in a social environment to collaborate and get research support.
Experts and mentors from Sydney Informatics Hub and across the University will be available to advise and answer questions on coding, data analytics or digital tools.
| Next session | 3–4pm, 18 May 3–4pm, 15 June 3–4pm, 20 July Third Wednesday of every month |
| Location | Join virtually via Zoom: https://uni-sydney.zoom.us/j/89080313908 |
| What to bring | If you have a laptop or tablet, bring it along so you can show what you’re working on. |
What happens at Hacky Hour?
At our Sydney Hacky Hour sessions you can gain:
- advice on how to best collect and manage your data
- practical help on fixing a frustrating bug
- an understanding of the basics of R
- an understanding of spatial data on a map
- help using tool 'X' after attending an Intersect training course
- assistance in using version control
- advice on how to access a large dataset from overseas quickly and easily
Even if you don't have a data problem to solve, come along to network with like-minded researchers, find a collaborator or hack away at your scripts in a friendly environment.
If you have a particular topic you'd like us to cover, please fill out our survey.
Meet the mentors
Each Hacky Hour will be staffed by experts from a variety of backgrounds.
Darya Vanichkina
Data Science & machine learning, R, python & HPC, Bioinformatics & genomics, HH founder
Nathaniel Butterworth
Visualisation, Python, Artemis HPC, Argus Research Desktops, astrophysics, geophysics, cloud, Matlab
Tracy Chew
DNA/RNA sequence analysis, single cell RNA-seq, association analysis, animal genomics, veterinary science
Gordon McDonald
Simulation, machine learning, Bayesian statistics, physics, chemistry, R, Matlab
Jazmin Ozsvar
R, data science, visualisation, machine learning, molecular dynamics, biochemistry, bioinformatics
Sergio Pintaldi
Python, Linux, Data Engineering, Database (SQL, NoSQL), Web Development, Dashboard and Data Viz, Data Pipelines
Marius Mather
R, Python, Statistics, Machine Learning, Stata, SPSS, Linux/Ubuntu
Jianzhou Zhao
Qualtrics, REDCap, Excel, SPSS, Programming, Research Data Management
Henry Lydecker
Machine learning, R, Python, public health, ecology
Cali Willet
Bioinformatics (Genomics, SNP and Indel Variant Detection, Structural Variation, Transcriptomics, RNA sequencing, GWAS, Metagenomics), High Performance Computing, Artemis HPC, NCI Gadi, Linux/Ubuntu, Perl, Scaling and Parallelization of Workflows
Jim Matthews
Statistical methods: Experimental Design, Power Analysis, Linear Models, Meta-analysis and Statistical Inference. Programming: Excel, SPSS, Prism, R and SAS. Applications: Medicine and Health, Engineering and Materials Science.
Alex Shaw
Statistical methods: Experimental Design, Power Analysis, Linear Models, PCA, Clinical Diagnostic Accuracy + Agreement, Meta-analysis and Statistical Inference. Programming: R tidyverse. Applications: Medicine and Health, Molecular Biology and Genetics.
