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If you can work with Python/Julia/R then I would still recommend the IPython notebook, since you will not get anything more interactive and better suited to dat
by ThePhysicist 11y ago
If you can work with Python/Julia/R then I would still recommend the IPython notebook, since you will not get anything more interactive and better suited to data analysis and visualization in one package. It basically allows you to run arbitrary snippets of code and interactively work with your data (code, visualize repeat). In general, I would not try to develop your algorithms directly within your app but instead generate some example data, export that data and then work with it in IPython (or another tool) to iteratively build the algorithm. As soon as you have a working solution you can then integrate/translate the code back into your application.
- lasryaric 11y agoGood point!
- pvnick 11y agoIpython notebook completely changed the way I do data science, specifically because its iterative/exploratory capabilities are so powerful. You can also hook it up to large scale service like hadoop/spark. Embedded Matplotlib charts have spoiled me. I cringe at the thought that I used to write scripts to output csv, run them on the command line, then copy the output to excel to graph the data. Never again!
- jlees 11y agoSeconding the IPython Notebook. I've used matplotlib but also like Plot.ly for its prettier, more sharable charts. It's also not a huge amount of work to set up a JSON-Flask-D3 visualisation pipeline out of test data; that's exactly what I did for my current data project, using cubism.js to easily visualise a time series. Again, benefits are the ability to easily share. Also, sometimes, Google Spreadsheets/Excel can be the easiest solution to throw up quickly - or using built-in data explorations in RStudio. I've also thrown graph data into Neo4j because it gives you a nice visual interface for free.