- 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.
Agile, CRISP-DM, Data Analytics, Data Management, Data Mining, Decision Management, Development, Software Engineering
- 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.
CRISP-DM, Data Analyst, Data Engineering, Data Governance, Data Science Team, Machine Learning Engineer
- 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.
CRISP-DM, Deep Learning, Descriptive Analytics, Machine 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.
Business, CRISP-DM, Machine Learning, Methodology
- 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.
CRISP-DM, Data Scientist, Data Workflow, DVC, GitHub, Version Control
- 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.
CRISP-DM, DVC, GitHub, Machine Learning, Open Source, Reproducibility, Version Control
- 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.
Analytics, Analytics Leader, CRISP-DM, Deployment, IBM, IBM DSX, ROI
- 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.
Analytics, CRISP-DM, Data Mining, Data Science, Decision Modeling, IIA, Methodology
- 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.
CRISP-DM, Data Mining, Data Science, Decision Modeling, Methodology, Predictive Analytics
- 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.
CRISP-DM, Data Mining, Methodology
- 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.
CRISP-DM, Data Science, Methodology, Springboard
- 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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CRISP-DM, Data Mining, Modeling, Olav Laudy, ROI
- 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.
CRISP-DM, Data Mining, James Taylor, Methodology, Poll
