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.
3–4pm, 18 August
3–4pm, 15 September
3–4pm, 20 October
Third Wednesday of every month
Join virtually via Zoom: https://uni-sydney.zoom.us/j/597499126
|What to bring||If you have a laptop or tablet, bring it along so you can show what you’re working on.|
At our Sydney Hacky Hour sessions you can gain:
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.
Each Hacky Hour will be staffed by experts from a variety of backgrounds.
Data Science & machine learning, R, python & HPC, Bioinformatics & genomics, HH founder
Visualisation, Python, Artemis HPC, Argus Research Desktops, astrophysics, geophysics, cloud, Matlab
DNA/RNA sequence analysis, single cell RNA-seq, association analysis, animal genomics, veterinary science
Simulation, machine learning, Bayesian statistics, physics, chemistry, R, Matlab
R, data science, visualisation, machine learning, molecular dynamics, biochemistry, bioinformatics
Python, Linux, Data Engineering, Database (SQL, NoSQL), Web Development, Dashboard and Data Viz, Data Pipelines
R, Python, Statistics, Machine Learning, Stata, SPSS, Linux/Ubuntu
Qualtrics, REDCap, Excel, SPSS, Programming, Research Data Management
Machine learning, R, Python, public health, ecology
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
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.
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.