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Data Science DEPP Engagement Process

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Data science team at Dell EMC uses a methodology called DEPP that guides the collaboration with the business stakeholders through the following stages: Descriptive Analytics to clearly understand what happened and how the business is measuring success.Exploratory Analytics to understand the financial, business and operational drivers behind what happened.Predictive Analytics to transition the business stakeholder mindset to focus on predicting what is likely to happen.Prescriptive Analytics to identify actions or recommendations based upon the measures of business success and the Predictive Analytics. The DEPP Methodology is an agile and iterative process that continues to evolve in scope and complexity as the clients mature in their advanced analytics capabilities
Read more at: Lessons in Becoming an Effective Data Scientist I was recently a guest lecturer at the University of California Berkeley Extension in San Francisco. On a lovely Saturday afternoon, the classroom was crowded wit…

Architecturally Aligned Testing

One of the good articles I read in a long time, that makes complete sense in today's agile environment:  Architecturally Aligned Testing Testing microservices should not be done in a separate test phase, by a dedicated test team, but instead collaboratively by cross-functional teams. There is a shift left in testing to ensure that teams stay autonomous and a shift right in testing towards exploration and experimentation.

Diagram Editor for JupyterLab

A Diagram Editor for JupyterLab - Jupyter Blog The new JupyterLab interface is much more than a replacement for the classic notebook. It aims to bring together all the pieces required for a complete scientific workflow. The extension-based architecture of JupyterLab comes with a number of components already enabled: a Jupyter notebook, a text editor, a file browser in the sidebar, a number of editors and viewers for various file formats, and much more.

Why are there so many Machine Learning paid certifications?

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I often wonder when I see people completing certificates on ML, are these more for showing the world that you know and understand ML or is it really a sense of achieving something! If it is the latter then why pay ? and why need a certificate at all?

Let me elaborate on the resources that are free and can do a better job than most paid ML courses:
Googles free website to choose videos, docs,tutorials, courses , sample code and Interactive demos - https://ai.google/education/#?modal_active=noneHave you read this? Triskelion one of the leaders on Kaggle explains how he went from a  beginner and finished up as a master with a top 10 finish -  https://mlwave.com/reflecting-back-on-one-year-of-kaggle-contests/ or https://machinelearningmastery.com/master-kaggle-by-competing-consistently/Kaggles Learning modules that are amazing Machine Learning - https://www.kaggle.com/learn/machine-learningR - https://www.kaggle.com/learn/rData Visualization - https://www.kaggle.com/learn/data-visualisati…

Track GitHub trending repositories in your favorite programming language

This one is definitely something every person who codes should do

Track GitHub trending repositories in your favorite programming language by native GitHub notifications!


vitalets/github-trending-reposgithub-trending-repos - Track GitHub trending repositories in your favorite programming language by native GitHub notifications!