Tag: data scientist

Feed: R-bloggers. Author: Jan Fischer. A major problem arises when comparing forecasting methods and models across different time series. This is a challenge we regularly face at STATWORX. Unit dependent measures like the MAE (Mean Absolute Error) and the RMSE (Root Mean Squared Error) turn out to be unsuitable and hardly helpful if the time series is measured in different units. However, if this is not the case, both measures provide valuable information. The MAE is perfectly interpretable as it embodies the average absolute deviation from the actual values. The RMSE, on the other hand, is not that easy to ... Read More

Feed: R-bloggers. Author: Econometrics and Free Software. Yesterday I released an ebook on Leanpub,called Modern R with the tidyverse, which you can alsoread for free here. In this blog post, I want to give some context. Modern R with the tidyverse is the second ebook I release on Leanpub. I released the first one, calledFunctional programming and unit testing for data munging with R aroundChristmas 2016 (I’ve retired it on Leanpub, but you can still read it for freehere) . I just had moved back to my home country ofLuxembourg and started a new job as a research assistant at ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: karan-sachdeva; In my last blog post, I covered how you can deliver an AI pilot in just eight weeks and at the same time design your program in a way to scale the AI across your enterprise.  Culture, architecture and technology is fundamental to move from AI pilot to AI @ Scale. I also discussed how IBM is helping one of biggest ANZ-region banks to do AI @ Scale to deliver personalized offers using a bot to automate risk modeling and crediting the money in their customers’ accounts. It ... Read More

Feed: R-bloggers. Author: Mango Solutions. There are so many wonderful EARL talks happening this year – it’s hard to highlight them all! But we thought we’d share some that the Mango team are really looking forward to: Ana Henriques, PartnerRe Using R in Production at PartnerRe Ana Henriques is the Analytics Tool Lead in PartnerRe’s Life & Health Department. Ana is now focused on business-side delivery of platforms and tools to support data science and related functions. Her talk will focus on the open source infrastructure supporting this process: version control, continuous integration, containerisation and container deployment and orchestration. Kevin Kuo, ... Read More

Feed: R-bloggers. Author: Business Science. [This article was first published on business-science.io, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Wrangling Big Data is one of the best features of the R programming language, which boasts a Big Data Ecosystem that contains fast in-memory tools (e.g. data.table) and distributed computational tools (sparklyr). With the NEW dtplyr package, data scientists with dplyr experience gain the benefits of data.table backend. We saw a 3X speed ... Read More

Feed: Planet big data. Author: Andrew Hogan. Last August, a tweet from a data scientist reminding colleagues to get out of tables of data and talk to real people — you know, do qualitative research — went viral among these two constituencies. The resulting debate reflected the state of the relationship between data science and design: They’re not on the same page. One participant even deleted one of his tweets, overwhelmed by the volume and combative tone of the feedback.  The worlds of data science and design are indeed colliding. More and more companies want to transform experiences with machine learning. The best organizations rely on customer analytics to make decisions. At the same time, the size of design teams has ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. I’ve written several times about how the “economies of learning” are more powerful than the “economies of scale”.  Through a continuous learning and refinement process, organizations can simultaneously drive down marginal costs while accelerating time-to-value and de-risking projects via digital asset re-use and refinement (see Figure 1). Figure 1: Exploiting the Digital Economies of Learning Think about how us lowly humans learn.  Whether trying to hit a golf ball or playing the piano or water skiing, we learn though the “feedback loop of failure”.  And the more real-time, immediate ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Ankit Garg. Data science has become an integral part of a 21st century organization. Almost all the companies, large, medium or small, want to utilize data science to fulfill their business imperatives by Improving their current processes for better operational efficiency thereby reducing costs Growing current business and opening new revenue streams Streamlining supporting functions including HR There are much more industry applications and avenues still to be explored then what is stated above, still setting up and successfully running a data science practice is something which has not been fully ... Read More

Feed: R-bloggers. Author: Pranjali Galgali. Congratulations! The fact that you’re here means you’re probably trying to figure out what career path is right for you. Maybe you are about to graduate or maybe you’re contemplating a career change. Either way, this is an exciting journey, so hang on tight! In recent years, jobs in data science have become increasingly popular. We have more data than we know what to do with and problems we want to solve. Whether in business, healthcare, space exploration, or another industry, there are endless opportunities to harness the power of data! Successful data scientists use ... Read More

Feed: SAS Blogs. Author: James Ochiai-Brown. What does an analytics platform look like? We might imagine a large server with data and software used for a wide range of operational reporting, BI and data analysis. There may be development and test environments. But typically most users will be working in the same production environment, sharing the same software and computing resources. The cost of infrastructure and the time required to deploy software and hardware changes dictated this arrangement. Today, cloud technology and scripted deployment of virtual infrastructure and software have removed these constraints by reducing costs and enabling rapid deployment ... Read More