- A Holistic Framework for Managing Data Analytics Projects - May 22, 2020.
Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.
- Has AI Come Full Circle? A data science journey, or why I accepted a data science job - Apr 10, 2020.
Personal journeys in Data Science can vary greatly between individuals. Some are just getting starting and wading into this vast ocean of opportunity, and others have been involved during its decades-long evolution as a professional field. This review of a longer journey can provide a broader perspective of how you might fit into this interesting career.
- On Building Effective Data Science Teams - Mar 4, 2019.
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
- Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM - May 29, 2018.
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
- 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.
- How A Data Scientist Can Improve Productivity - May 25, 2017.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
- Data Version Control: iterative machine learning - May 11, 2017.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.
- An ode to the analytics grease monkeys - Feb 2, 2017.
Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.
- 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.
- KDnuggets™ News 17:n03, Jan 25: Automated Machine Learning Overview; Data Science Puzzle; Chatbots on Steroids - Jan 25, 2017.
The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them
- 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 Automation: Debunking Misconceptions - Aug 2, 2016.
This opinion piece aims to clear up some proposed misconceptions surrounding data science automation.
- The Data Science Process, Rediscovered - Mar 9, 2016.
The Data Science Process is a relatively new framework for doing data science. It is compared to previous similar frameworks, and a discussion on process innovation versus repetition is then undertaken.
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- 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.
- Predictive Analytics – a Soup Story - Sep 1, 2015.
CRISP-DM is most popular methodology for analytics, data mining, and data science projects. Learn how to cook CRISP-DM recipe in five simple steps.
- New Standard Methodology for Analytical Models - Aug 3, 2015.
Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.
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- Nine Laws of Data Mining, part 2 - Jun 30, 2015.
The second group data mining laws includes: There are always patterns, Data mining amplifies perception in the business domain, Prediction increases information locally by generalisation, Value law, Law of Change. Tom Khabaza explains.
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- Nine Laws of Data Mining, part 1 - Jun 29, 2015.
Tom Khabaza, one of the authors of the Clementine data mining workbench and of CRISP-DM methodology for data mining process, proposes and explains 9 laws of data mining.
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- Top stories for Oct 26 – Nov 1: Will Deep Learning take over Machine Learning? - Nov 2, 2014.
Will Deep Learning take over Machine Learning, make other algorithms obsolete? Cartoon: Halloween Costume for Big Data; CRISP-DM, still the top methodology for analytics, data mining, or data science projects; Big Data accelerates medical research? Or not?
- KDnuggets 14:n28, Top Data Science methodology; Big Data Halloween costume - Oct 29, 2014.
KDnuggets latest stories, including: CRISP-DM, still the top methodology for analytics, data science; Cartoon: Halloween Costume for Big Data; Will Deep Learning take over Machine Learning? DM radio with KDnuggets, Oct 30.
- 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.