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Top 8 great resources for Data Mining

Fresh information on data mining is provided each day by special data analysis websites, blogs and professional organizations. You can improve your data mining knowledge and skills with these resources. We try to summarize a list of eight great data mining resources below.

The Society of Data Miners

Professional organizations help members share their knowledge for career advancement. Members build networks of colleagues and experienced practitioners to update and improve their skills through discussions and knowledge exchange.

Data miners protect the profession in a variety of ways to establish ethical standards and meaningful professional certifications. SocDM, The Society of Data Miners, founded in 2013, is the first professional organization dedicated specifically to data mining.

KDnuggets

KDnuggets is a key data-mining industry news website. It is hosted by prestigious data scientist Gregory Piatetsky-Shapiro, KDnuggets provides an active flow of information about data mining topics, events, jobs and tools.

It is worthwhile to have a look at KDnuggets on a regular basis. Posts here stay on hot topics in data mining and related issues to date, and it is a good starting point for information that is not entirely new, but for you, probably it is still new.

The attached links on the homepage and the well-organized content by topics lead to a variety of information sources. Topics covered include some basics such as software, classes and jobs, as well as specialties such as contests, calls for research papers and even data mining humor.

All Analytics

All Analytics is an industry publication that covers a variety of data analysis issues. Supported by one of the major software vendors, this site offers short, professionally written and edited articles on the data analysis, with thousands more new products in the archives are published daily.

Here are some representatives Posts:

  • “3 Approaches to Justifying Analytics Results”, from Bryan Beverly, Statistician, Bureau of Labor Statistics
  • “Cellphone Tracking: Protection vs. Privacy”, from Ariella Brown, Writer & Social Media Consultant
  • “Anatomy of a Data Management Project”, from Fabian Pascal, Founder, Editor & Publisher, Database Debunkings

The New York Times

At a cocktail party, sometimes people will hear the argument about the value or the ethics of one’s data collection or analysis practices; chances are that the argument was started with something appeared in The New York Times.

The New York Times often has stories about the data analysis, but that is not necessarily what you will see in the heading. Here is one example:

  • In 2012 Charles Duhigg piece “How Companies Learn Your Secrets” – “If they could entice those women or their husbands to visit Target and buy baby-related products, the company’s cue-routine-reward calculators could kick in and start pushing them to buy groceries, bathing suits, toys and clothing, as well.”

The headline of the article does not include data analysis in it, but that’s what it was. Actually the article raised  many arguments for months after its publication. Look at their past article titles, and you will see that the New York Times contains fresh information that relates to everyday data analysis most.

Forbes

Forbes is a business publication that emphasizes the business and prospects of publicly traded companies and industries. It has frequently posts in the market for various data-related products and services. Following are some Forbes contributors who create content focus on data-related topics:

  • Gil Press (Pressed Data) ;
  • Piyanka Jain (Putting Data to Work);
  • Naomi Robbins (Effective Graphs); and
  • Lisa Arthur (The Marketing Revolution).

Smart Data Collective

Smart Data Collective is an analytics industry website that provides curated content by analytics professionals. Most items can be found here reposted by lesser known blogs by independent analysis practitioners and small industrial sites and you can find original content as well. New posts appear daily.

Representative contributions are as follows:

  • “What You Need to Know About Cloud Analytics”, from Timo Elliott, VP, Global Innovation Evangelist at SAP
  • “What the ‘Small Data’ Revolution Means for Marketers”, from Noah Jessop, co-founder and CEO of CommandIQ
  • “Using Data to Fight Counterfeiting”, from Travis Korte, research analyst at the Center for Data Innovation

Cross Industry Standard Process for Data Mining

CRISP-DM is the predominant data mining method and the unofficial industry standard. The CRISP-DM process model, detailed instructions from the industry consortium that develops CRISP_DM, explains elements of the process, why and how it was developed, and many step-by-step data mining details.

  • “Cross Industry Standard Process for Data Mining, commonly known by its acronym CRISP-DM, was a data mining process model that describes commonly used approaches that data mining experts use to tackle problems.” (Wikipedia)
  • “CRISP-DM remains the most popular methodology for analytics, data mining, and data science projects, with 43% share in latest KDnuggets Poll, but a replacement for unmaintained CRISP-DM is long overdue.” (KDnuggets)

Nate Silver

Nobody communicates to the world of data analysis, such as Blogger and statistician Nate Silver. Therefore, he is the most famous statisticians in the world. (He is the only famous statistician in the world, evidence that the rest of us need to work on our communication skills!) Nate also a master of data analysis, so learn from the best by his read blog.

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