Statistics.com, a provider of online education in statistics and analytics, announces a partnership with CrowdANALYTIX, a predictive modeling "managed crowdsourcing" company, offering a new online course, "Applied Predictive Analytics in partnership with CrowdANALYTIX", which will run from Oct. 11 to Nov 8, 2013. The goal of this course is to teach users (who have basic knowledge of R programming, predictive analytics and statistics) to apply machine learning techniques in real world case studies. This course provides a hands on approach, presenting the opportunity to participate in a private educational competition hosted by CrowdANALYTIX. Business Case Study: We will study data from the "daily deals" industry (consisting of websites like Groupon, Living Social etc. which source local deals to offer each day). The daily deals industry is emerging and highly competitive.
Your Challenge: Develop a usable and scalable algorithm that delivers a real-time flight profile to the pilot, helping them make flights more efficient and reliably on time. Prize Pool: $250,000 Heritage Health Data Analysis Prize ($3M), can administrative health care data be used to accurately predict which patients will be admitted to the hospital? Your Challenge: Develop a usable and scalable algorithm that delivers a real-time flight profile to the pilot, helping them make flights more efficient and reliably on time. Heritage Health Data Analysis Prize ($3M), can administrative health care data be used to accurately predict which patients will be admitted to the hospital? Kaggle, the leading platform for data prediction competitions CrowdANALYTIX, converts business challenges into analytics competitions DrivenData: Data Science Competitions for Social Good Innocentive, mainly focusing on life sciences, but has other interesting competitions TunedIT, education, research and industrial contests.
As enterprises embrace AI and work towards integrating it increasingly into their business processes, one of the key decisions they are all having to make is whether to buy or build the AI components that will drive their enterprises into the future. More than 61 percent of businesses said they have already implemented AI, demonstrating that adoption is on the rise [Narrative Science, 2018]. Cost, time-to-market, ROI, criticality to business success, and quality of solution are common factors that must be considered between the buying versus building decisions, and they apply to AI initiatives as well. The scarcity of data scientists is well-known, but most enterprises still feel the need to first hire a bunch of data scientists before embarking on AI projects. Although enterprises may eventually achieve the goal of building a strong core in-house team, most AI initiatives can rely on vendors in the meantime.
I am sure most of you here are familiar with Kaggle and the contests they host. Though its a great platform for data scientists, I feel it limits the participants to be able to solve only prediction problems. Also, the budding young data scientists who are now venturing out to test waters never stand a chance to win, but no doubt, its a great learning curve. I stumbled upon a site, CrowdAnalytix.com, which is similar to Kaggle, but these guys host a wide variety of contests, from Research to Predictive Modeling to Visualization. I feel this gives an opportunity for young statisticians and data science students to participate and win good prize money as the contests are not long and very intense like Kaggle.
Recently I decided to try my hand at the Extraction of product attribute values competition hosted on CrowdAnalytix, a website that allows companies to outsource data science problems to people with the skills to solve them. I usually work with image or video data, so this was a refreshing exercise working with text data. The challenge was to extract the Manufacturer Part Number (MPN) from provided product titles and descriptions that were of varying length – a standard RegEx problem. After a cursory look at the data, I saw that there were 54,000 training examples so I decided to give Deep Learning a chance. Here I describe my solution that landed me a 4th place position on the public leaderboard.