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50+ Data Science and Machine Learning Cheat Sheets

50+ Data Science and Machine Learning Cheat Sheets
Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms. Cheatsheets on Python, R and Numpy, Scipy, Pandas There are thousands of packages and hundreds of functions out there in the Data science world! An aspiring data enthusiast need not know all. Here are the most important ones that have been brainstormed and captured in a compact few pages. Mastering Data science involves understanding of statistics, Mathematics, Programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions. Here are the cheatsheets by category: Cheat sheets for Python: Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. Share more & Learn! Related:

Related:  gummibearehausentips+utilityData Mining

Python Map Reduce on Hadoop - A Beginners Tutorial November 17 2013 Share Tweet Post This article originally accompanied my tutorial session at the Big Data Madison Meetup, November 2013. The goal of this article is to: Academic Phrasebank – Referring to Sources One of the distinguishing features of academic writing is that it is informed by what is already known, what work has been done before, and/or what ideas and models have already been developed. Thus, in academic texts, writers frequently make reference to other studies and to the work of other authors. It is important that writers guide their readers through this literature. Computational Urban Design Research Studio Laster semester we utilize two kinds of clustering algorithms to do our analyze. The first one is distance based clustering, the second one is grid based clustering. Although logically they are very similar, both of them are forming clusters based on distances, they are different in doing this, and results can be different. Below is the logic of these 2 algorithms. A. distance based clustering: 1.

Finding the natural number of topics for Latent Dirichlet Allocation - Christopher Grainger Update (July 13, 2014): I’ve been informed that I should be looking at hierarchical topic models (see Blei’s papers here and here). Thanks to Reddit users /u/GratefulTony and /u/EdwardRaff for bringing this to my attention. However, Redditor /u/NOTWorthless says HDPs do not provide a ‘posterior on the correct number of topics in any meaningful sense’. I’ll do more research and do a follow-up post. You can follow the conversation on Reddit here.

Online Statistics Education: A Free Resource for Introductory Statistics Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Graph theory Refer to the glossary of graph theory for basic definitions in graph theory. Definitions[edit] Definitions in graph theory vary. The following are some of the more basic ways of defining graphs and related mathematical structures. Graph[edit] In the most common sense of the term,[1] a graph is an ordered pair

Arun et al measure with NPR data · GitHub Skip to content Learn more Please note that GitHub no longer supports old versions of Firefox. IPython Books - IPython Cookbook IPython Interactive Computing and Visualization Cookbook This advanced-level book covers a wide range of methods for data science with Python: Interactive computing in the IPython notebook High-performance computing with Python Statistics, machine learning, data mining Signal processing and mathematical modeling Highlights 500+ pages100+ recipes15 chaptersEach recipe illustrates one method on one real-world exampleCode for Python 3 (but works fine on Python 2.7)All of the code freely available on GitHubContribute with issues and pull requests on GitHub This is an advanced-level book: basic knowledge of IPython, NumPy, pandas, matplotlib is required.

First Order Inductive Learner In machine learning, First Order Inductive Learner (FOIL) is a rule-based learning algorithm. Background[edit] Algorithm[edit] The FOIL algorithm is as follows: News and Events: PhD candidate in Computational Linguistics and Dialogue Processing - The Institute for Logic, Language and Computation Newsitem added on 10 September 2015. The ILLC is looking for a highly motivated, creative and talented PhD candidate to join the newly established Dialogue Modelling Group led by Raquel Fernández. The mission of the group is to understand dialogical interaction by developing empirically-motivated formal and computational models that can be applied to various dialogue processing tasks and to human-machine interaction. The PhD position is part of an NWO VIDI project focused on studying linguistic interaction in the presence of asymmetry, that is, imbalances or mismatches between dialogue participants. Looking into asymmetric settings provides a great opportunity for investigating the dynamic changes that linguistic interaction can potentially bring about: how do our choices of words and phrases contribute to language learning, to knowledge transfer, or to opinion shifts?

Color Wheel Pro: Classic Color Schemes Monochromatic color scheme The monochromatic color scheme uses variations in lightness and saturation of a single color. This scheme looks clean and elegant. B+ tree A simple B+ tree example linking the keys 1–7 to data values d1-d7. The linked list (red) allows rapid in-order traversal. This particular tree's branching factor is b=4. A B+ tree is an n-ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves. The root may be either a leaf or a node with two or more children.[2]

Imperial College London Applications for 2015 entry are now open. Imperial College Business School operates a number of application deadlines throughout the year. For more information please see their website. Top 10 data mining algorithms in plain R Knowing the top 10 most influential data mining algorithms is awesome. Knowing how to USE the top 10 data mining algorithms in R is even more awesome. That’s when you can slap a big ol’ “S” on your chest… …because you’ll be unstoppable! Today, I’m going to take you step-by-step through how to use each of the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.