[unable to retrieve full-text content]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.
Original Post: Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM
[unable to retrieve full-text content]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.
Original Post: What is the most important step in a machine learning project?
[unable to retrieve full-text content]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.
Original Post: How A Data Scientist Can Improve Productivity
[unable to retrieve full-text content]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.
Original Post: Data Version Control: iterative machine learning
[unable to retrieve full-text content]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.
Original Post: Four Problems in Using CRISP-DM and How To Fix Them
Previous post Next post Tweet Tags: Automated Data Science, CRISP-DM, Data Science, Kaggle This opinion piece aims to clear up some proposed misconceptions surrounding data science automation. comments By Sandro Saitta, Nestlé Nespresso. Editor’s note: This blog post was an entrant in the recent KDnuggets Automated Data Science and Machine Learning blog contest, where it received an…
Original Post: Data Science Automation: Debunking Misconceptions
Previous post Tweet Tags: CRISP-DM, Data Science, Methodology 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. By Matthew Mayo, KDnuggets. comments Last week, KDnuggets top tweet was a Quora answer to What is the…
Original Post: The Data Science Process, Rediscovered
Previous post Next post Tweet Tags: CRISP-DM, Data Science, Methodology, Springboard 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. comments By Springboard. At Springboard, our data…
Original Post: The Data Science Process