Solve Interview Case Studies 10x Faster Using Dynamic Programming. Introduction The ability to solve case studies comes with regular practice. Many a times, if you find yourself failing at thinking like a pro, perhaps, it’s just because you haven’t practiced enough. To help you become confident, I’ve written multiple case studies in last one month. You can check the recent ones here. If you haven’t solved any of them, I’d suggest you to check them out first. Dynamic Programming a.k.a Dynamic Optimization isn’t any trick or a mathematical formula which delivers correct answer just by providing the inputs. In this article, I have explained the art of dynamic programming using a Taxi-Replacement case study.
For freshers, I’d recommend checking out the previous case study before trying their hand at this one. Case Study – Taxi Replacement Assume, you’ve decided to start a Taxi Operator firm. A critical decision which can set you for success is to consider / estimate the time to change the cars. Cost (INR/Km) The initial car cost (as of now) is 100,000 INR. And, Cluster Analysis. 1 Clustering Techniques Much of the history of cluster analysis is concerned with developing algorithms that were not too computer intensive, since early computers were not nearly as powerful as they are today. Accordingly, computational shortcuts have traditionally been used in many cluster analysis algorithms. These algorithms have proven to be very useful, and can be found in most computer software. More recently, many of these older methods have been revisited and updated to reflect the fact that certain computations that once would have overwhelmed the available computers can now be performed routinely.
In R, a number of these updated versions of cluster analysis algorithms are available through the cluster library, providing us with a large selection of methods to perform cluster analysis, and the possibility of comparing the old methods with the new to see if they really provide an advantage. 2 Hierarchial Clustering > cars = read.delim('cars.tab',stringsAsFactors=FALSE) Benchmarks and codes | VeRoLog. Rdocumentation. R Graphical Manual. Package: EMD Version: 1.5.3 Date: 2013-04-26 Title: Empirical Mode Decomposition and Hilbert Spectral Analysis Author: Donghoh Kim and Hee-Seok Oh Maintainer: Donghoh Kim <email@example.com> Depends: R (>= 2.11), fields (>= 6.7.6), locfit (>= 1.5-8) Description: This package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H. -S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
License: GPL (>= 2) URL: Packaged: 2013-04-25 11:53:07 UTC; donghohkim NeedsCompilation: yes Repository: CRAN Date/Publication: 2013-04-25 14:51:45 Install log. Rstudio/webinars. Version Control with Git and SVN. Version control helps software teams manage changes to source code over time. Version control software keeps track of every modification to the code in a special kind of database. If a mistake is made, developers can turn back the clock and compare earlier versions of the code to help fix the mistake while minimizing disruption to all team members. Version control systems have been around for a long time but continue to increase in popularity with data science workflows. The RStudio IDE has integrated support for version control. If you are new to version control, check out our book, video tutorial, and explanation: Version control is an indispensable tool for coordinating the work of teams and also has many benefits for individual work.
Requirements RStudio supports the following open source version control systems: To use version control with RStudio, you should first ensure that you have installed Git and/or Subversion tools on your workstation (details below). Installation Git Subversion.
Lavaan. Seefeld_StatsRBio. Gzip - Decompress gz file using R. Unzip a tar.gz file in R? Day1.pdf. Arules. Revolution R Open. Foreach.pdf. Ecodist. Nonlinear regression. H2O. FIAR. rCharts. Twitter. FasteR! HigheR! StrongeR! - A Guide to Speeding Up R Code for Busy People. This is an overview of tools for speeding up your R code that I wrote for the Davis R Users’ Group. First, Ask “Why?” It’s customary to quote Donald Knuth at this point, but instead I’ll quote my twitter buddy Ted Hart to illustrate a point: I’m just going to say it.I like for loops in #Rstats, makes my code readable.All you [a-z]*ply snobs can shove it!
— Ted Hart (@DistribEcology) March 12, 2013 Code optimization is a matter is a matter of personal taste and priorities. . (1) Do you want your code to be readable? If you need to explain your code to yourself or others, or you will need to return to it in a few months time and understand what you wrote, it’s important that you write it in a way that is easy to understand. Some optimal code can be hard to read. . (2) Do you want your code to be sharable? Most of the considerations of (1) apply here, but they have to be balanced with the fact that, if your code is painfully slow, others are not going to want or have time to use it. No? Compare.
Storage. Ggplot2. Treemap. GoogleVis. Rpanel.