Data Science

The Best Free Books for Learning Data Science


The Elements of Statistical Learning - Another valuable statistics text that covers just about everything you might want to know, and then some (it's over 750 pages long). Make sure you get the most updated version of the book from here (as of this writing, that's the 2017 edition). Data Mining and Analysis - This Cambridge University Press text will take you deep into the statistics and algorithms used for various types of data analysis. Do you need books to learn data science?

Data Analyst - IoT BigData Jobs


Seeking Data Analyst Allied 100, LLC, a fast-growing nationwide distributor of emergency medical equipment located in Woodruff, Wisconsin, is accepting resumes for a full-time year-round Data Analyst. Responsibilities include gathering data and raw information from a cross-section of focus areas and work closely with senior managers and executives to help focus our sales, marketing, and operational activities around data-backed decisions. You'll help department heads interpret the information and offer feedback on new tests and opportunities to improve our results over time, generate routine queries to help spot business trends, occasionally help compile reports and offer help to others in the organization, and see the impact of your advice immediately! Required experience includes two or more years of analyzing raw data to generate summary reports and recommendations, expert-level understanding of Excel, Access, PowerPoint, and SQL, and the ability to work well on teams and in groups. Ideal candidates will have a BA/BS or advanced degree and experience with marketing analytics programs (including Google Analytics, Google Adwords, email delivery software, and search engine optimization tools), and experience and understanding of financial statements and financial modeling.

Three ways that big data reveals what you really like to watch, read and listen to


Anyone who's watched "Bridget Jones's Diary" knows one of her New Year's resolutions is "Not go out every night but stay in and read books and listen to classical music." The reality, however, is substantially different. What people actually do in their leisure time often doesn't match with what they say they'll do. Economists have termed this phenomenon "hyperbolic discounting." In a famous study titled "Paying Not to Go to the Gym," a couple of economists found that, when people were offered the choice between a pay-per-visit contract and a monthly fee, they were more likely to choose the monthly fee and actually ended up paying more per visit.

Should I Open-Source My Model? – Towards Data Science


I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

Cloudera's Hilary Mason: To make AI useful, make it more "boring"


When it comes to Artificial Intelligence, the industry is at a crossroads of fascination versus function. We're awed by the technology, but a number of forces are conspiring to minimize the progress we're making, especially in the Enterprise. There are a few people out there who are adamantly trying to address this. One of them is Hilary Mason, Cloudera's GM of Machine Learning. Mason was previously Chief Data Scientist at Bitly, then founder and CEO of Fast Forward Labs, which Cloudera acquired in 2017.

Mindsync - Bitcoin Wiki


Mindsync is a decentralized, community-driven AI platform where everyone can participate in the growing artificial intelligence market as a customer, expert, developer or supplier to order or create and share AI services as value. Establish the expert community of Artificial Intelligence, Machine Learning and Data Science to solve customer's tasks, develop ML models, share experience, and improve competence. This significantly reduces computation cost by threefold in comparison with cloud computing. Deployment of a blockchain assures security and data integrity. Persistance of ML-models hashes, data, solution quality assessments, solution ratings, and the platform participant metadata are all saved in the embedded blockchain.

The Data Driven Partier: Movie Mustache – Towards Data Science


The concept behind'Movie Mustache' is simple, but revolutionary. This game was foreign to me until a few weeks ago when I got to experience it watching the Adam Sandler classic, The Waterboy. As amazing as it was to watch every single character wear the handlebar mustaches that were taped to the TV, that paled in comparison to the problem statement that followed: How can we place the mustaches to maximize our drinking as a group? As always, the code seen here can be viewed in its entirety on my GitHub. Due to the availability of facial recognition packages, this problem can be solved in less than a day with the right approach.

UK government backs AI projects for insurance industry - Reinsurance News


The UK government has announced that it plans to invest in 40 artificial intelligence (AI) and data analytics projects to boost productivity and improve customer service in the UK insurance, accountancy, and legal services industries. The government has pledged £13 million to support these collaborative industry and research projects and develop the next generation of professional services. One of these projects, developed by Intelligent Voice Ltd, Strenuus Ltd. and the University of East London, will combine AI and voice recognition technology to detect and interpret emotion and linguistics to assess the credibility of insurance claims. Another project is an analysis tool that looks at images collected by drone to assess flood-damaged areas, using a 3D image recognition system to evaluate flood extent and depth alongside impacts on buildings and infrastructure to help with insurance claim assessments. Other examples include an online bot that uses AI to provide answers to online legal questions and software that analyses accounting data and suggests ways to cut expenditure.

Here's what it takes to make IoT data ready for AI and machine learning


The integration of artificial intelligence and the Internet of Things introduces a wide array of connected health tools that produce a vast amount of data that must be synthesized, analyzed, stored and communicated by a robust information infrastructure. But if hospitals don't structure and store IoT patient data properly, that information could be rendered not assessable by AI tools. For starters, significant infrastructure is needed to streamline IoT-generated data to make sure it is simple to assess and manage with AI. "AI adoption and scale will be accelerated by the relatively low cost of deployment," said Rick Krohn, president of HealthSense, a connected health consulting firm. "A terabyte of storage costs less than $100, and wearable sensors and cloud infrastructure are becoming increasingly affordable. But AI requires sophisticated applications that deliver contextually aware right-place-right-time clinical decision support."

IBM Takes Watson AI to AWS, Google, Azure - InformationWeek


Cloud computing has made a lot of technology more accessible, and artificial intelligence and its underlying technologies are no exception. If you want more organizations to be able to use your technology, then make it possible for them to use it on one of the big public cloud providers -- Microsoft Azure, Google Cloud Platform, and Amazon Web Services (AWS). Indeed, many organizations are now using the AI services that are available and have been built on those public cloud platforms -- AWS Rekognition, for instance. In an effort to broaden the distribution of its flagship artificial intelligence technology, IBM this week announced that it is making IBM Watson portable across all these public cloud services. The company unveiled the strategy this week at the IBM Think 2019 event in San Francisco.