Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."
There's a significant issue with big data today: fully using it requires data scientists who are expensive and often a bottleneck on data utility. Big data is complex, and it takes specialist knowledge and skills to get a grip on the potential uses. For big data to fully reach its potential, businesses need to get beyond the data scientist. This relief is coming in the form of artificial intelligence. While AI won't replace the data scientist any time soon, it can assume some of the tasks currently handled by data scientists.
Northside Hospital in Atlanta is adopting machine learning technology to enable the organization to predict when insurance companies will end payments. The new technology it's using is from The SSI Group, which is providing technology that aggregates all remittance data coming through its clearinghouse to make the predictions. The goal is to enable providers that manually build their own spreadsheets to predict payments to use the SSI technology to determine when they can expect to get paid, down to the day and time, according to the vendor. "Without predictive analytics, hospitals and other providers are left guessing when they will receive payments," says Eric Nilsson, chief technology officer at SSI. Using analytics, SSI can give greater visibility on the payment of institutional, professional, in-patient and out-patient claims.
Across industries, Big Data and Artificial Intelligence (AI) have proven to be powerful tools when it comes to informing companies about their target customers. Gartner predicts that by 2019, more than 50% of organizations will redirect their investments to customer experience innovations. As a result, many organizations have built teams to collect and analyze data on every step of the customer journey – taking into account where, why and how customers interact with their channels. By analyzing this data in real time, companies are able to keep up with evolving customer demands. Dissecting every interaction to understand what drives customer behavior may seem like a gargantuan task for many.
The 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms. The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis.
We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.
May Masoud is a Solution Specialist at SAS Canada, as part of the Data Sciences team. Leveraging her analytics background, she helps businesses visualize the potential of their data, and surface insights using modern data mining and machine learning techniques. With a Master of Business Analytics following a Bachelor in Statistics & Economics, May aims to create value at every step of the analytics lifecycle: data discovery, model build, model deployment, and business strategy. She has touched the analytics landscape in a variety of industries, whether it is oil production models for the energy sector or solving churn problems in the telecom industry. May's aim is to ubiquitize self-serve analytics and enable citizen data scientists.
This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. As a professional computer scientist and statistician, I hope to shed some useful light on the topic. I have potential bias: I've written four R-related books, I've given a keynote talk at useR!; I currently serve as Editor-in-Chief of the R Journal; etc. But I hope this analysis will be considered fair and helpful. This is subjective, of course, but having written (and taught) in many different programming languages, I really appreciate Python's greatly reduced use of parentheses and braces: This is of particular interest to me, as an educator.
Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine's "magic bullet" phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an "AI revolution," one that will finally result in high-value care. While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action.
Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. and is the Program Director of both the Strata Data Conference and the Artificial Intelligence Conference. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.