- What is the most important step in a machine learning project? - Aug 18, 2017.
In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.
- Teaching the Data Science Process - May 17, 2017.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
- How to stay out of analytic rabbit holes: avoiding investigation loops and their traps - Apr 6, 2017.
Data scientists tend to think that their main job is to answer complex questions and gain in-depth insights, bu in reality it is all about solving problems – and the only way to solve a problem is to act on it.
- Putting Together A Full-Blooded AI Maturity Model - Apr 5, 2017.
Here is a proposed “7A” model that is useful enough to capture of the core of what AI offers without falsely implying there is a static body of best practices in this area.
- Fixing Deployment and Iteration Problems in CRISP-DM - Feb 1, 2017.
Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.
- Bringing Business Clarity To CRISP-DM - Jan 24, 2017.
Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.
- Four Problems in Using CRISP-DM and How To Fix Them - Jan 18, 2017.
CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key elements of the approach.
- Data Science for IoT course: Strategic foundation for decision makers - Sep 9, 2016.
The course is based on an open problem solving methodology for IoT analytics which we are developing within the course. The course starts in Sept 2016. To sign up or learn more email email@example.com.
- What Should Data Scientists Know About Psychology? - Mar 14, 2016.
Due to training in the scientific method, data management, statistics/data analysis, subject matter expertise, and communicating results into substantive knowledge psychology researchers must have a solid understanding of data science and vice-versa.
- KDnuggets™ News 16:n09, Mar 2: Data Science Process; Scikit-feature; Automated Data Science - Mar 9, 2016.
The Data Science Process; scikit-feature: Open-Source Feature Selection Repository; Automated Data Science and Data Mining; Top Big Data Processing Frameworks.
- Top KDnuggets tweets, Feb 29 – Mar 06: Data Science Process; Wisdom of Crowds fails to solve this simple puzzle - Mar 7, 2016.
Wisdom of Crowds fails to solve this simple #math #puzzle ; #DataScience Process - the work flow of a data scientist; R is the fastest-growing language on #StackOverflow; @DeepDrumpf #DeepLearning #Twitterbot imitates #DTrump, more plausible than real one.
- The Data Science Process - Mar 4, 2016.
What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.
- Creating a methodology for Data Science for IoT (IoT Analytics) - Jan 13, 2016.
While there is no specific methodology to solve Data Science for IoT (IoT Analytics) problems, perhaps it is time to draft one.
- CRISP-DM, still the top methodology for analytics, data mining, or data science projects - Oct 28, 2014.
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.
- New Poll: Methodology for Analytics, Data Mining, Data Science Projects? - Oct 13, 2014.
KDnuggets revisits the question of methodology, and asks "What main methodology are you using for your analytics, data mining, or data science projects?" Please vote.