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Presentations, code and links to videos for the PyData London Conference 2015 that was held at Bloomberg near Moorgate in London.

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# PyData London Conference 2015
Presentations, code and links to videos for the PyData London Conference 2015 that was held at Bloomberg near Moorgate in London.

# Tutorials,  19th June 2015

| Title                                                                                              | Author(s)                                                                                  | Video | Presentation | Code |
|----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|-------|--------------|------|
| Accelerating Scientific Code with Numba                                                            | Graham Markall                                                                             | [Youtube](https://www.youtube.com/watch?v=q45JJ8BXP2g)      |              | [Website](http://big-grey.co.uk/numba/) |
| Analysis and transformation of geospatial data using Python                                        | Demeter Sztanko                                                                            |       |              |      |
| Getting started with Bokeh / Let's build an interactive data visualization for the web..in Python! | Sarah Bird, Bryan Van de Ven                                                               | [Youtube](https://www.youtube.com/watch?v=XBiS0oBzX3o)       | [iPython Notebook, Github](https://github.com/bokeh/bokeh-notebooks/tree/master/tutorial)             |  [iPython Notebook](https://github.com/bokeh/bokeh-notebooks/tree/master/tutorial)    |
| Getting Started with Cloud Foundry for Data Science                                                | Ian Huston                                                                                 | [Youtube](https://www.youtube.com/watch?v=lp2c05yDJko)       | [SpeakerDeck](https://speakerdeck.com/ihuston/pydata-london-2015-getting-started-with-cloud-foundry-for-data-science)              | [Github](https://github.com/ihuston/python-cf-examples)      |
| How “good” is your model, and how can you make it better?                                          | Chih-Chun Chen, Dimitry Foures, Elena Chatzimichali, Giuseppe Vettigli, Raoul-Gabriel Urma | [Youtube](https://www.youtube.com/watch?v=oKHeAtOgMNA)       | [iPython Notebook, Github](https://github.com/cambridgecoding/pydata-tutorial)             |    [iPython Notebook, Github](https://github.com/cambridgecoding/pydata-tutorial)  |
| Open Source Tools for Financial Time Series Analysis and Visualization                             | Yves Hilpisch                                                                              |       |              |      |
| Probabilistic programming in sports analytics                                                      | Peadar Coyle                                                                               |       |              |      |
| Spark. A View from the Trenches                                                                   | Sahan Bulathwela, Maria Mestre                                                             | [Youtube](https://www.youtube.com/watch?v=aKfrjQlVmAU)       | [iPython Notebook, Nbviewer](http://nbviewer.ipython.org/gist/skimaria/94f2a09ef090f6132f62)             |  [iPython Notebook, Nbviewer](http://nbviewer.ipython.org/gist/skimaria/94f2a09ef090f6132f62)    |

# Talks, 20 - 21st June 2015

| Title                                                                                           | Authors                                               | Video | Presentation | Code |
|-------------------------------------------------------------------------------------------------|-------------------------------------------------------|-------|--------------|------|
| A Beginner's Guide to Building Data Pipelines with Luigi                                        | Dylan Barth, Stuart Coleman                           |       |              |      |
| A Fast, Offline Reverse Geocoder in Python                                                      | Ajay Thampi                                           |       |              |      |
| A practical guide to conquering social network data                                             | Benjamin Chamberlain, Davide Donato, Josh Levy-Kramer |       |              |      |
| A Tube Story: How can Python help us understand London's most important transportation network? | Camilla Montonen                                      |       |              |      |
| Agent-Based Modelling, the London riots, and Python                                             | Thomas French, Fred Farrell                           |       |              |      |
| Collect and Visualise Metrics With InfluxDB and Grafana                                         | Marek Mroz                                            |       |              |      |
| Constructing protein structural features for Machine Learning                                   | Ricardo Corral Corral                                 |       |              |      |
| Data-visualisation with Python and Javascript: crafting a data-viz toolchain for the web        | Kyran Dale                                            |       |              |      |
| Defining Degrees of Separation in Data Classifications Using Predictive Modelling               | Yiannis Pavlosoglou, Adam Reviczky, Neri Van Otten    |       |              |      |
| Deploying a Model to Production                                                                 | Alex Chamberlain                                      |       |              |      |
| Financial Risk Management: Analytics and Aggregation with the PyData stack                      | Miguel Vaz                                            |       |              |      |
| Getting Meaning from Scientific Articles                                                        | Éléonore Mayola                                       |       |              |      |
| Hacking Human Language                                                                          | Hendrik Heuer                                         |       |              |      |
| Hierarchical Data Clustering in Python                                                          | Frank Kelly                                           |       |              |      |
| How DataKind UK helped Citizens Advice get more from their data                                 | Emma Prest, Billy Wong                                |       |              |      |
| How We Turned Everyone at Our Company into Analysts with Python and SQL                         | Arik Fraimovich                                       |       |              |      |
| Hyperparameter Optimisation for Machine Learning in Python: Building an automatic scientist     | Thomas Greg Corcoran                                  |       |              |      |
| If It Weighs the Same as a Duck: Detecting Fraud with Python and Machine Learning               | Ryan Wang                                             |       |              |      |
| Information Surprise or How to Find Data                                                        | Oleksandr Pryymak                                     |       |              |      |
| Integration with the Vernacular                                                                 | James Powell                                          |       |              |      |
| Jointly Embedding knowledge from large graph databases with textual data using deep learning    | Armando Vieira                                        |       |              |      |
| Jupyter (IPython): how a notebook is changing science                                           | Juan Luis Cano                                        |       |              |      |
| Keynote - How to Find Stories in Data                                                           | Helena Bengtsson                                      |       |              |      |
| Keynote - What's it Like to be a Bot?                                                           | Eric Drass                                            |       |              |      |
| Keynote: CRISP-DM: The Dominant Process for Data Mining                                         | Meta S. Brown                                         |       |              |      |
| Localising Organs of the Fetus in MRI Data Using Python                                         | Kevin Keraudren                                       |       |              |      |
| Machine Learning with Imbalanced Data Sets                                                      | Natalie Hockham                                       |       |              |      |
| Making Computations Execute Very Quickly                                                        | Russel Winder                                         |       |              |      |
| NLP on a Billion Documents: Scalable machine learning with Spark                                | Martin Goodson                                        |       |              |      |
| Our Data, Ourselves                                                                             | Giles Greenway                                        |       |              |      |
| Performance Pandas                                                                              | Jeff Reback                                           |       |              |      |
| Political risk event extraction using Python and Apache Storm                                   | Aeneas Wiener                                         |       |              |      |
| PyPy, The Python Scientific Community and C extensions                                          | Romain Guillebert                                     |       |              |      |
| Python and scikit-learn based open research SDK for collaborative data management and exchange  | Grigori Fursin, Anton Lokhmotov                       |       |              |      |
| Python for Image and Text Understanding: One Model to rule them all!                            | Roelof Pieters                                        |       |              |      |
| Rescuing and Exploring Complex Life Science Data                                                | Paul Agapow                                           |       |              |      |
| Ship It!                                                                                        | Ian Ozsvald                                           |       |              |      |
| Simulating Quantum Physics in Less Than 20 Lines of Pure Python                                 | Katie Barr                                            |       |              |      |
| Smart Cars of Tomorrow: Real-Time Driving Patterns                                              | Ronert Obst                                           |       |              |      |
| Sudo Make me a (London) Map                                                                     | Linda Uruchurtu                                       |       |              |      |
| The Dark Art of Search Relevancy                                                                | Eddie Bell                                            |       |              |      |
| The London Air Quality API                                                                      | Andrew Grieve                                         |       |              |      |
| Using the SALib Library for Conducting Sensitivity Analyses of Models                           | Will Usher                                            |       |              |      |
| Veni, Vidi, Voronoi: Attacking Viruses using spherical Voronoi diagrams in Python               | Tyler Reddy                                           |       |              |      |

Conf2015

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