Tag: visualization

Feed: R-bloggers. Author: Shige. The cyphr package seems to provide a good choice for small research group that shares sensitive data over internet (e.g., DropBox). I did some simple experiment myself and made sure it can actually serve my purpose. I did my experiment on two computers (using openssl): I created the test data on my Linux workstation running Manjaro then I tried to access the data on a Windows 7 laptop. For creating the data (Linux workstation): library(cyphr)# Create the test datadata_dir dir.create(data_dir)dir(data_dir)# Encrypt the test datacyphr::data_admin_init(data_dir)key filename cyphr::encrypt(saveRDS(iris, filename), key)dir(data_dir)# Cannot read the data with decrypting itreadRDS(filename)# Read ... Read More

Feed: R-bloggers. Author: Greg Wilson. We received almost 400 applications for our 2019 internship program from students with very diverse backgrounds. After interviewing several dozen people and making some very difficult decisions, we are pleased to announce that these twelve interns have accepted positions with us for this summer: Therese Anders: Calibrated Peer Review. Prototype tools to conduct experiments to see whether calibrated peer review is a useful and feasible feedback strategy in introductory data science classes and industry workshops. (mentor: Mine Çetinkaya-Rundel) Malcolm Barrett: R Markdown Enhancements. Tidy up and refactoring the R Markdown code base. (mentor: Rich Iannone) ... Read More

Feed: Planet big data. Author: Praveen Sripati. The good thing about K8S is that almost all of the big companies are rallying around it, instead of providing their own alternatives. Here you can get the number of contributors to K8S grouped by companies for last month. Metrics for other data ranges can also be seen. There are also couple of other pre-built dashboard around a lot of other metrics there. Obviously Google has the most number of contributors followed by RedHat and VMware. The visualization is provided by Grafana. The home page (https://devstats.cncf.io/) provides metrics for other CNCF projects (including ... Read More

Feed: R-bloggers. Author: Bob Carpenter. First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we don’t need to explore all of it. Sampling to characterize the posterior There’s a misconception among Markov chain Monte Carlo (MCMC) practitioners that the purpose of sampling is to explore the posterior. For example, I’m writing up some reproducible notes on probability theory and statistics through sampling (in pseudocode with R implementations) and have just come to the point where I’ve introduced and implemented Metropolis ... Read More

Feed: R-bloggers. Author: John Mount. As of cdata version 1.0.8 cdata implements an operator notation for data transform. The idea is simple, yet powerful. First let’s start with some data. d wrapr::build_frame( "id", "measure", "value" | 1 , "AUC" , 0.7 | 1 , "R2" , 0.4 | 2 , "AUC" , 0.8 | 2 , "R2" , 0.5 ) knitr::kable(d) 1 AUC 0.7 1 R2 0.4 2 AUC 0.8 2 R2 0.5 In the above data we have two measurements each for two individuals (individuals identified by the "id" column). Using cdata‘s new_record_spec() method we can capture a description ... Read More

Feed: R-bloggers. Author: Ryan Sheehy. Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Debbie Berebichez, a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY. Hugo: Hi there, Debbie, and welcome to DataFramed. Debbie: Hi, Hugo. It’s a pleasure of mine to be here. Hugo: It is such a pleasure to have you on this show, and I’m really excited to be here today to talk in particular about critical thinking in data science, and what that actually means, and as we know, to get critical about critical thinking ... Read More

Feed: Cambridge Intelligence. Author: Sam Mabaso. We work really hard at Cambridge Intelligence but we also know how to have fun. Every month we organize a company-wide social event – a perfect opportunity for us to unwind and spend time together outside work.We also run monthly country-themed lunches. Our team is made up of people from all over the world including India, Japan, Scotland, Italy, Turkey, and South Africa. To celebrate this diversity through food, we take turns to prepare and share lunches that feature authentic culinary delights.Here’s what we’ve been up to.October 2018Escape RoomsEscape rooms really get our adrenaline ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: satyajit maitra. BigQuery is Google’s serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator. Analyze all your data by creating a logical data warehouse over managed, columnar storage, as well as data from object storage and spreadsheets. Build and operationalize machine learning solutions with simple SQL. Easily and securely share insights within your organization and beyond as ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Civis Analytics. By Peter Cooman The path to D3 mastery is dark and full of terrors. D3 itself is a JavaScript (JS) library and on top of that, you’ll need a basic understanding of HTML (Hypertext Markup Language) and CSS (Cascading Style Sheets) to get the most out of it. If you’re a data scientist, chances are JavaScript, at best, ranks as your fourth language, after R, Python, and SQL. In this three-part series of blog posts, I will show you step-by-step how you can combine R with D3, HTML, and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kartik Patel. As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction. In some businesses, Data Scientists, professional analysts and IT staff are often buried under requests for analysis and data and, as a result, these teams are unable to focus on strategic issues and on crucial questions that require 100% accuracy to drive the direction of the business. For other businesses, the issue is more pressing. When ... Read More