The University of Sydney provides licences to some commercial software packages for staff and students. Please visit the list of software available through the university to see what software you are eligible to access, and for information on how to obtain access to the available packages.
Tools specific to data analysis that are available at the University are listed and described below. Keep in mind that analysis and visualisation are often overlapping activities, so be sure to check both the analysis and visualisation sections to ensure that you don’t miss the ideal tool for your data!
NVivo – Allows you to handle rich text based information, where deep levels of analysis on both small and large volumes of data are required. It removes many of the manual tasks associated with analysis, like classifying, sorting and arranging information, so you have more time to explore trends, build and test theories and ultimately arrive at answers to questions. Get NVivo from ICT.
Microsoft Excel – A spreadsheet application that is part of the Microsoft Office package made available by the University. Excel uses a grid of cells to organise data manipulations and arithmetic, and offers graphing tools, pivot tables and a macro programming language. Get Excel from ICT.
GraphPad Prism – Combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organisation. While it won't replace a heavy-duty statistics program, Prism lets you easily perform basic statistical tests commonly used by laboratory and clinical researchers. Get GraphPad Prism from ICT.
GenStat – One of the statistics packages made available by the University, Gen Stat is a data analysis tool used to manage and illustrate your data, summarize and compare, model relationships, design investigations and of analyse your experiments from the simplest of ANOVA’s right through to the most complex REML. Get GenStat from ICT.
Mathematica – One of the statistics packages made available by the University, Mathematica is a computational software program used in scientific, engineering, and mathematical fields and other areas of technical computing. Get Mathematica from ICT.
SAS – One of the statistics packages made available by the University, SAS (Statistical Analysis System) is an analytical, data manipulation application with reporting capabilities. It provides tools to master the four data-driven tasks common to virtually any application: data access, management, analysis and presentation—all within a powerful applications development environment. Get SAS from ICT.
SPSS and AMOS – One of the statistics packages made available by the University, SPSS provides you with a broad range of capabilities for the entire analytical process. With SPSS, you can generate decision-making information quickly using powerful statistics, understand and effectively present your results with high-quality tabular and graphical output, and share your results with others using a variety of reporting methods, including secure Web publishing. Results from your data analysis enable you to make smarter decisions more quickly by uncovering key facts, patterns, and trends. The AMOS application provides you with structural equation modelling (SEM) software, which allows you to create more realistic models than if you used standard multivariate statistics or multiple regression models alone. Get SPSS and AMOS from ICT if you are using a university owned or leased computer, or access SPSS through Citrix Receiver if you are using your own computer.
MATLAB – A numerical computing environment that uses its own MATLAB scripting language to manipulate, analyse and visualise data. A large number of toolboxes exist that extend its functionality. The University licence includes approximately 70% of the toolboxes available in the MATLAB suite. Get MATLAB from ICT.
ArcGIS – ArcGIS is a geographic information system that allows you to analyse and visualise geospatial data. It allows you to create maps, georeference data, analyse mapped data, visualise geospatial data, and manage geographic information in a database. Get ArcGIS from ICT.
Cambridge Structural Database System – The CSDS is a powerful suite of software tools that allow you to explore, utilise, analyse and visualise the data in the Cambridge Structural Database, a repository of small molecule crystal structures. Get the CSDS from ICT.
ChemBioDraw – A chemistry and biology drawing and analysis suite. ChemBioDraw provides you with a collection of applications for chemical structure drawing and analysis combined with biological pathway drawing. Get ChemBioDraw from ICT.
Launchpod – A tool to deploy virtual machines on the NeCTAR Research Cloud with one of a number of preconfigured research-based software applications. The software applications that you can build using Launchpod include: Twitter Scraper, DIVER, Omeka, LimeSurvey, MATLAB, Alveo, CSIRO Workspace, and RStudio. Launchpod can be accessed by any researcher from an organisation that participates in the Australian Access Federation (AAF). All Australian universities are members of AAF.
In addition to commercial software, a host of open-source and/or freely available tools exist for data analysis. We have collected a short list of some of the more widely used, or easy to use tools that are available. Keep in mind that analysis and visualisation are often overlapping activities, so be sure to check both the analysis and visualisation sections to ensure that you don’t miss the ideal tool for your data!
Voyant Tools – A web application for performing simple text mining and analysis. You can upload, link to, or copy and paste text documents directly into the web browser. The basic analysis includes a word cloud, word counts, word frequencies, and word trends.
Lexos – A web-based tool designed for transforming, analyzing, and visualizing texts, designed for use primarily with small to medium-sized text collections, and especially for use with ancient languages and languages that do not employ the Latin alphabet. Lexos was created as an entry-level platform for Humanities scholars and students new to computational techniques while providing tools and techniques sophisticated enough for advanced research.
Jupyter Notebook – The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualisations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, machine learning and more. The Notebook has support for over 40 programming languages, including those popular in Data Science such as Python, R, Julia and Scala.
OpenRefine – A powerful tool for working with messy data: cleaning it, transforming it from one format into another, and extending it with web services and external data.
CARTO – A geographic information system cloud computing platform that enables you to perform data analysis and data visualisation operations on geospatial data. It provides you with a set of tools and APIs for analysis and visualisation in a web browser, and also supplies some publically available geospatial datasets. Formerly CartoDB.
QGIS – An open source geographic information system that allows you to visualise, manage, edit, analyse geospatial data, and compose printable maps.
R – A programming language and software environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques. A large number of packages that extend the functionality of R for data analysis beyond the basics are available. R does have a steep learning curve, and so may take some time to pick up for those who are new to programming.
RStudio – RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
Python – A general purpose programming language with a focus on simplicity and readability that make it relatively easy to learn for those with little programming experience. Python requires additional libraries to be usable for data analysis and visualisation (e.g. NumPy and pandas for analysis, and Seaborn or Bokeh for visualisation). Installation of Python through the Anaconda distribution is recommended, as it includes many of the most commonly used Python packages.
If you know of other freely available tools that you think other researchers would benefit from knowing about, then get in touch with email@example.com.