There comes a time in a software toolchain’s lifecycle where the focus shifts from developer...

There comes a time in a software toolchain’s lifecycle where the focus shifts from developer...

As we’ve mentioned on previous occasions, one of the defining characteristics of data science is the emphasis on the availability of “large” data sets, which we define as “enough data that statistical efficiency is not a concern” (note that a “large” data set need not be “big data,” however you choose to define it). In Related posts:

In honor of the Ryder Cup, here's a fun puzzle for the mathematically inclined golfer to consider: how many different paths are possible in an 18 hole round of match play golf? If you'd rather not wade through the math then you can skip ahead to the "practical exploration" section of this post to see some actual match play...

This is the next post in the DVI indicator series. After the first two (here and here) analyzed in details the post-entry returns and the entry power of this indicator, it’s time to take a look at the trading performance. Using the Systematic Investor Toolbox, we get some pretty decent results: CAGR of 16.15% and

The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R Related posts:

I recently attended a week-long R course in Newcastle, taught by Colin Gillespie. It went from “An Introduction to R” to “Advanced Graphics” via a day each on modelling, efficiency and programming. Suffice to say it was an intense 5 days! Overall it was the best R course I’ve been on so far. I’d recommend it to others,...

Welcome to last part of the series post again! In previous part I discussed about the solutions to the questions mentioned in first part. In this part, we will implement whole scenario using R and MySQL together and see how we can process bigdata(computationally ) Let us recall those questions and summarize their answers to The post Build...

Welcome to the second part of the series blog posts. In first part we tried to understand the challenges of fitting predictive model to the large dataset. In this post I will discuss about the solution approach to that challenges. Let’s start rolling. As machine learning technique requires accessing whole dataset for fitting model on The post Build...

Wellcome to the series blog posts. Since long time, I am writing post on Machine learning with R. Today I am gonna discuss on big data problem while fitting machine learning on it and its solution using MySQL and R. Before we jump directly to solution, let us discuss about big data little bit. (You The post Build...

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