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a Physicist in Wall Street: Trading Resources in R First apologize for the problem with the feed, I was playing with feedburner options. I am working on a new more advanced tutorial, that I hope to publish in the following days. As a rookie in the life of the trading in R I made a collection of resources on topics related to finance in R. The first place to see what packages exists in R for finance proposes is the CRAN R repository. the home of Business Information “Give me a real-time view of the pharma trends so that I’m always the first to know” As the pharma industry evolves, it’s important to know you are basing your decisions on analysis that reflects the very latest trends and issues. With event-sensitive analysis, you can build your strategy based on analysis and forecasts that are reliable and accurate, to give you the tools to rapidly respond to changes across key diseases areas, companies and drugs. Evaluate your drug’s clinical and commercial potential against related treatments based on emerging clinical trial results and treatment guidelines Assess up-to-date company revenue projections and changes to product and portfolio strategies to enhance your competitive strategy with analysis that reflects new product developments, licensing issues and M&A activity Compare the latest treatment trends and unmet needs to benchmark your marketed products and pipeline development across 6 major disease areas

finance by Joseph Rickert Late last Saturday afternoon I was reading in my usual spot at the Dana Street Coffee House in Mt. View. Python for Android Mobile app development for smartphones is hot. This is no more prevalent than in the Android space where the activity level oftentimes is frenzied. However, when it comes to building a “real” Android app, it seems there's only one programming choice: Java (although it is possible with a lot more work to use C/C++ with Android's Native Development Kit).

Marco Fucci - Works I'm a Web Developer, DevOp and Consulting CTO based in London. I've been assisting startups, mid-sized and big companies with web development, project management and technical advisory for many years. I'm also known as the guy who doesn't talk too much but when he talks it's worth listening. I'm passionate about new technologies and security, my favourite tools at the moment are Django and Javascript as they have been by my side for years but I value other languages as well and I have myself used many of them. Raspberry Pi Want an Extra Slice of Pi? Grab yourself a pi-toppi-top is the upgradable laptop you build yourself. It features a 13” HD screen, built-in trackpad and keyboard and offers over 10 hours of battery life. pi-top makes using a Raspberry Pi easier than ever, and allows you to swap out and replace your old pi board as new upgrades are released.

FAQs Table of Contents: The Raspberry Pi is a credit-card-sized computer that plugs into your TV and a keyboard. It is a capable little computer which can be used in electronics projects, and for many of the things that your desktop PC does, like spreadsheets, word processing, browsing the internet, and playing games. Groupe: Raspberry Pi Raspberry Pi 7” Touchscreen Display The Touchscreen Display gives users the ability to create all-in-one, integrated projects such as tablets, infotainment systems and embedded projects. Buy Now Learn More Quant Pythonista - A blog about scientific Python, quant finance, statistics, data analysis, and hacking of all kinds TL;DR I’ve finally gotten around to building the high performance parser engine that pandas deserves. It hasn’t been released yet (it’s in a branch on GitHub) but will after I give it a month or so for any remaining buglets to shake out: A project I’ve put off for a long time is building a high performance, memory efficient file parser for pandas. The existing code up through and including the imminent pandas 0.9.0 release has always been makeshift; the development focus has been on parser features over the more tedious (but actually much more straightforward) issue of creating a fast C table tokenizer.

Quant Pythonista A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. However, I was dissatisfied with the limited expressiveness (see the end of the article), so I decided to invest some serious time in the groupby functionality in pandas over the last 2 weeks in beefing up what you can do. So this article is a part show-and-tell, part quick tutorial on the new features. Note that I haven’t added a lot of this to the official documentation yet.

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