Charles River Analytics: Software Engineer – Human Machine Interface Design

[unable to retrieve full-text content]Seeking a Software Engineer to work closely with small teams of scientists, software engineers, and subject matter experts, using modern technologies, to design and develop cutting edge information visualizations, human automation teaming methodologies, and novel display interfaces.
Original Post: Charles River Analytics: Software Engineer – Human Machine Interface Design

Crawling the internet: data science within a large engineering system

by BILL RICHOUX Critical decisions are being made continuously within large software systems. Often such decisions are the responsibility of a separate machine learning (ML) system. But there are instances when having a separate ML system is not ideal. In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed. Through this example, we discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure.Data scientists promote principled decision-making following several different arrangements. In some cases, data scientists provide executive level guidance, reporting insights and trends. Alternatively, guidance and insight may be delivered below the executive level to product managers and engineering leads, directing product feature development via metrics and A/B experiments.This post focuses on an even lower-level pattern, when…
Original Post: Crawling the internet: data science within a large engineering system

Video: R for AI, and the Not Hotdog workshop

Related To leave a comment for the author, please follow the link and comment on their blog: Revolutions. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more… If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook…
Original Post: Video: R for AI, and the Not Hotdog workshop

How to add Trend Lines to Visualizations in Displayr

In Displayr, Visualizations of chart type Column, Bar, Area, Line and Scatter all support trend lines.  Trend lines can be  linear or non-parametric (cubic spline, Friedman’s super-smoother or LOESS). Adding a linear trend line Linear trend lines can be added to a chart by fitting a regression to each series in the data source. In the chart below, the linear trends are shown as dotted lines in the color corresponding to the data series. We see there is considerable fluctuation in the frequency of each search term. But the trend lines clarify that the overall trend for SPSS is downward, whereas the trend for Stata is increasing. The data for this chart was generated by clicking Insert > More > Data > Google Trends. In the textbox for Topic(s) we typed in a comma-separated list of search terms (i.e., “SPSS,…
Original Post: How to add Trend Lines to Visualizations in Displayr

Clean Your Data in Seconds with This R Function

All data needs to be clean before you can explore and create models. Common sense, right. Cleaning data can be tedious but I created a function that will help. The function do the following: Clean Data from NA’s and Blanks Separate the clean data – Integer dataframe, Double dataframe, Factor dataframe, Numeric dataframe, and Factor and Numeric dataframe. View the new dataframes Create a view of the summary and describe from the clean data. Create histograms of the data frames. Save all the objects This will happen in seconds. Package First, load Hmisc package. I always save the original file.The code below is the engine that cleans the data file. cleandata <- dataname[complete.cases(dataname),] The function The function is below. You need to copy the code and save it in an R file. Run the code and the function cleanme will…
Original Post: Clean Your Data in Seconds with This R Function