Sure. Gunnar's "Topology and Data" paper (already linked to by another poster) is the first place to go for an overview of TDA (although it's getting dated). A more updated version (but behind paywall) is "Topological pattern recognition for point cloud data" ( http://dx.doi.org/10.1017/S0962492914000051 )
Robert Ghrist has another take on TDA ( https://www.math.upenn.edu/~ghrist/preprints.html ) that is more "engineering" focused and also uses a different mathematical tool set than the above (eg. Sheaves/Co-Sheaves). I particularly like the sensor coverage results.
For Mapper consider using PythonMapper by Daniel Müllner ( http://danifold.net/mapper/ ). The UI is touchy - I use it mostly via scripts only. Mapper is pretty simple to implement (just a series of well understood pieces - the magic is in the interpretation/understanding what you've done). As an example consider the kepler-mapper project ( https://github.com/MLWave/kepler-mapper ). More lines of code are used for calling out to the d3 visualization than implementing the core mapper algorithm.
For my persistent homology calculations I always use Dionysus ( http://www.mrzv.org/software/dionysus/ ). Rumor has it a much improved parallelized version will be released soon.
Robert Ghrist has another take on TDA ( https://www.math.upenn.edu/~ghrist/preprints.html ) that is more "engineering" focused and also uses a different mathematical tool set than the above (eg. Sheaves/Co-Sheaves). I particularly like the sensor coverage results.
Rob Ghrist and John Harer ( http://fds.duke.edu/db/aas/math/faculty/john.harer/publicati... )both have textbooks available if you want to get into the fundamentals in the field.
Jose Perea has some nice results using ideas from TDA in a variety of contexts. eg texture classification ( https://www.math.msu.edu/user_content/docs/KleinBottleTextur... ) and signal processing ( https://www.math.msu.edu/user_content/docs/Sw1Pes_Theory2015... )
Here's a talk I gave at ICERM last summer using persistent homology as a feature generating method for drug discovery:
https://icerm.brown.edu/video_archive/#/play/726
(slides available for download if you poke around on the icerm web site).
This is my "part two" of the video linked to in the parent article:
https://www.youtube.com/watch?v=3Z73Wd2T1xE