Health data research does not have to involve the use of expensive proprietary software. While GDPR does place restrictions on access to and use of health related data, once suitable legal and ethical approval has been gained, the software below can be used to query the data to gain some useful insights. With the exception of Excel (which most people will have access to) the other software has been developed by the open source commutes and is available for free or for a small suggested donation to fund further development. If you are a member of University of Leicester staff or a student then then additional statistical software is available on university managed computers via the self-service system. Further details regarding what software is available on university computers can be found here for those who are access this site from on campus.

With all the talk of "big data" in data science it can be easy to forget the basics. If you are not handling a data set with thousands of lines of data, you may well be able to use Excel to visualise and analyse your data. Excel has expanded its capabilities over the years and now includes a wide variety of functions, including various graphing and statistical offerings. Microsoft have a training section to help you get up to speed with the basics as well as some more advanced features of Excel.

Galaxy is a workspace environment for the processing of raw NGS data. . The interface provides a way using common NGS analysis tools with out the need for learning the command line, and individual tools can also be combined into a pipeline/ While there are online servers that run the Galaxy software, it can also be installed locally by selecting the correct version of Galaxy container for your desired analysis at this link. Note you will need to install an environment to run the images in on your computer. The docker environment is recommended. Alternatively to install Galaxy natively on your Unix/Linux to MacOS computer from the command line, the series of commands (which can just be cut and pated into a terminal window) can be found here. Users should note that an independent FTP server may need to be installed on the target system, and it is necessary to set up administrator privileges on the instalation of Galaxy in order for settings to be saved.

Gephi takes a more visual approach to data exploration. It allows the user to take a global view of large multi factorial data sets, and then apply different predefined analysis to visualise data clusters, networks, linkage and play with algorithm parameters to see how changing them affects the out come in real time. It is also possible to select data element(s) in the both table view and graphical view and then perform other analysis relative to the selected data point. The overview video gives a good idea of the types of analysis that are possible. The website also has a dedicated training page, with some tutorials in French, Spanish and Chinese in addition to English.

While Tulip allows individual algorithms to be applied to the data one at a time, Orange allows users to construct analysis pipelines using visual interface, which uses blocks to represent algorithms that can be linked together. The parameters for each algorithm can be altered by clicking on each block and the output of the algorithm is also shown by double clicking on each one. Pipelines are built up by linking blocks together and specific data can be passed between blocks by selecting it in each analysis block. The training videos are under the documentation section of the website. Informative YouTube Videos on how the various "widgets" work and a simple explanation of the underlying statistics and algorithms can be found here

When a data set becomes too large to handle in Excel then you are going to need to analyse the data via some form of command driven process, rather than the more familiar point and click approach of Excel. However, this does not necessarily mean you have to dive right in at the deep end and learn programming. R is a programming language that was developed to allow statisticians to analyse data, however R Studio makes using R a little more friendly than hammering out commands on the command line or writing scripts. While it is not completely point and click, it does allow a lot of the functionality of R to be accessed via such an interface. The R studio website has tutorials that cover the basics of data processing.

Tulip takes a more structured approach to analysis, and allows predefined algorithms to be applied to tabulated data. The parameters of each algorithm can be altered in a panel similar to the approach taken in more familiar applications like office applications and graphics software. The tutorial section of the website takes the form of worked examples that led the user through several analysis by the hand to get them used to Tulips features.