In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.]]>

In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.]]>

Lecture 21 October 2014]]>

Lecture 21 October 2014]]>

A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python’s Theano library. The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs. Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk ]]>

A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python’s Theano library. The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs. Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk ]]>

Draft presentation slides for my Stanford class on customer acquisition and sales. Overview on why most startups fail at acquiring customers. Covers: LTV, CAC, sales, and a number of other customer channels such as SEM, pay per install, PR, social media, and others. ]]>

Draft presentation slides for my Stanford class on customer acquisition and sales. Overview on why most startups fail at acquiring customers. Covers: LTV, CAC, sales, and a number of other customer channels such as SEM, pay per install, PR, social media, and others. ]]>

A graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.]]>

A graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.]]>

]]>

]]>

]]>

]]>