Sarah Flannery became famous at a young age of 16 when her algorithm for cryptography was speculated to be a far better alternative to the widely adopted RSA algorithm. She presented an algorithm at the Young Scientist Competition with no fan fare and she won the competition. This book would not have been written but for one reason , the competition result got picked up by “London Times “ and an article appeared on the front page with a nerdy picture and a catchy title , ”Sarah Flannery, 16 , who baffled the judges with her grasp of cryptography”.

Image compression is a billion dollar industry. An optimal way to store and retrieve image data storage is the key. Though there are multiple algorithms that are usually used , one such algorithm that has found wide spread popularity is SVD( Singular Value Decomposition). To explain SVD in simple words, let’s take an image  For illustration purpose, I have deliberately chosen two colors. Now let’s say you want to store this data.

There is a need to visualize higher dimensional spaces in various applied math problems, though one cannot give any physical meaning to such higher dimensional spaces. Our inability to see anything more than 3 dimensions does not mean we cannot visualize and understand multidimensional space. For a superb account of ways to visualize higher dimensional space, one can read “Flatland”, the classic book of a 2 dimensional world, which describes the experiences of a 2 dimensional square trying to come to terms with a 3 dimensional world.

Via LANYRD In this brief video, Jonathan Seidman and Ramesh Venkataramiah discuss the use of Hadoob, Hive for doing analytics on 500 GB /day data that gets generated via Orbitz. A website like Orbitz generates millions of searches each day. Storing and processing the ever-growing volumes of data generated by all of those searches becomes prohibitive though traditional systems such as relational databases. This presentation details how Orbitz is using new tools such as Hadoop and Hive to meet these challenges.

Visualization of data is one of the key components of analysis. The better the visual, the better the insight in to the data patterns. “Color” in a graphic is one of the key elements and in this aspect, Cartography provides valuable lessons. Via Colorbrewer **: ** Different Schemes of colouring can be used based on the data requirements : Sequential schemes are suited to ordered data that progress from low to high.