Crime Analysis with Shiny & R #Combine multiple heat map chart and merge them output$crimeHeatMap <- renderPlot({ base_size <- 10 #Aggregate crime category by different time in a day git/github guide All statistical/computational scientists should use git and github, but it can be hard to get started. I hope these pages help. (More blather below.) There are many resources for git and github; my aim is to provide the minimal guide to get started. I love git and github.

Latent Semantic Analysis (LSA) Tutorial Latent Semantic Analysis (LSA), also known as Latent Semantic Indexing (LSI) literally means analyzing documents to find the underlying meaning or concepts of those documents. If each word only meant one concept, and each concept was only described by one word, then LSA would be easy since there is a simple mapping from words to concepts. Unfortunately, this problem is difficult because English has different words that mean the same thing (synonyms), words with multiple meanings, and all sorts of ambiguities that obscure the concepts to the point where even people can have a hard time understanding. For example, the word bank when used together with mortgage, loans, and rates probably means a financial institution.

The Hidden World of Facebook "Like Farms" Facebook has become the advertising outlet of choice for many of the world’s businesses and companies. Whenever there is a new product to test, a service to announce or event to promote, many organisations turn to Facebook to post news of the development. To enable this, Facebook allows users to create pages devoted to specific topics. Mining of Massive Datasets The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it.

Solutions & Notes for ISL Hastie-Tibshirani Using a little bit of algebra, prove that (4.2) is equivalent to (4.3). In other words, the logistic function representation and logit representation for the logistic regression model are equivalent. Let… All about the position: Data scientist Teradata Aster is seeking experienced individuals with demonstrated capability in the applied analytic and/or data science space. Proficiency in data manipulation, analytic algorithms, advanced math, and/or statistical modeling is required and application development experience a plus. We are looking for exceptional individuals to join our Professional Services team as an Analytic Data Scientists. This client-facing role will be engaged in the design and deployment of solutions.

Using Latent Dirichlet Allocation to Categorize My Twitter Feed Over the past 3 years, I have tweeted about 4100 times, mostly URLS, and mostly about machine learning, statistics, big data, etc. I spent some time this past weekend seeing if I could categorize the tweets using Latent Dirichlet Allocation. For a great introduction to Latent Dirichlet Allocation (LDA), you can read the following link here. For the more mathematically inclined, you can read through this excellent paper which explains LDA in a lot more detail.

The R Inferno What is it? Abstract: If you are using R and you think you’re in hell, this is a map for you. A book about trouble spots, oddities, traps, glitches in R. Many of the same problems are in S+. Cluster Analysis R has an amazing variety of functions for cluster analysis. In this section, I will describe three of the many approaches: hierarchical agglomerative, partitioning, and model based. While there are no best solutions for the problem of determining the number of clusters to extract, several approaches are given below. Data Preparation Prior to clustering data, you may want to remove or estimate missing data and rescale variables for comparability. # Prepare Data mydata <- na.omit(mydata) # listwise deletion of missing mydata <- scale(mydata) # standardize variables

Data Science Bootcamp - 12 week career prep New York City in-person instruction + ongoing career coaching + job placement support Winter Bootcamp: January 12, 2015 - April 3, 2015 Application Period Closed More Free Data Mining, Data Science Books and Resources More free resources and online books by leading authors about data mining, data science, machine learning, predictive analytics and statistics. The list below based on the list compiled by Pedro Martins, but we added the book authors and year, sorted alphabetically by title, fixed spelling, and removed the links that did not work. The descriptions are by Pedro. An Introduction to Data Science by Jeffrey Stanton, Robert De Graaf, 2013.An introductory level resource developed by Syracuse UniversityAn Introduction to Statistical Learning: with Applications in R by G.

From Deconstruction to Big Data: How Technology is Reshaping the Corporation Evans affirms that we are undergoing a re-acceleration of technological change despite the global recession and that something sudden and dramatic is happening. One important aspect of this is how Big Data is reshaping business, and transforming internal organization and industry architecture. He goes on to explain that two information technology drivers are reshaping internal organization: business strategy and the structures of industries.

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