Airflow Documentation

Airflow is a platform to programmatically author, schedule and monitor workflows. Use airflow to author workflows as directed acyclic graphs DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing. complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Building (Better) Data Pipelines using Apache Airflow

Airflow is a platform to programmatically author, schedule and monitor workflows (a.k.a. DAGs or Directed Acyclic Graphs)

An.Introduction.to.Machine.Learning.with.Application.in.R

The purpose of this document is to provide a conceptual introduction to statistical or machine learning (ML) techniques for those that might not normally be exposed to such approaches during their required typical statistical training 1 . Machine learning 2 can be described as a form of a statistics, often even utilizing well-known nad familiar techniques, that has bit of a different focus than traditional analytical practice in the social sciences and other disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.

Springer - R.A.Carmona - Statistical analysis of financial data in S-PLUS 豆瓣

This book grew out of lectures notes written for a one-semester junior statistics course offered to the undergraduate students majoring in the Department of Oper-ations Research and Financial Engineering at Princeton University. Tidbits of the history of this course will shed light on the nature and spirit of the book.

Portfolio_Optimization_with_R_Rmetrics

This is a book about portfolio optimization from the perspective of compu- tational finance and financial engineering. Thus the main emphasis is to briefly introduce the concepts and to give the reader a set of powerful tools to solve the problems in the field of portfolio optimization.

R Graphics

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R's graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.

Social media Mining with R

Explore the social media APIs in R to capture data and tame itEmploy the machine learning capabilities of R to gain optimal business valueA hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data

scikits-learn user guide

A basic introduction to writing extensions with scikit learn package.

Visualize This

This book is example-driven and written to give you the skills to take a graphic from start to finish. You can read it cover to cover, or you can pick your spots if you already have a dataset or visualization in mind. The chap- ters are organized so that the examples are self-contained. If you’re new to data, the early chapters should be especially useful to you. They cover how to approach your data, what you should look for, and the tools avail- able to you. You can see where to find data and how to format and prepare it for visualization. After that, the visualization techniques are split by data type and what type of story you’re looking for.

Writing R extensions

A basic introduction to writing extensions with R language.