A data science project applied to online content creation

Having goals and expectations is essential to foster motivation. Exceeding expectations is a powerful source of motivation, but not meeting them can put the whole project in danger.

Based on a scientific approach, I wanted to set reasonable expectations so that I maximise my chances to exceed them, and benefit from this motivation boost to move faster towards my goal.

My crystal ball seems to be broken… Source

Before diving into technical details, have you heard about Make Your Brain Work by Amy Brann? …


Give access to your products using 3 R commands

Take your projects out of the museum Source

The 3 most common cloud services used by data science teams are Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure (Azure).

Leveraging Docker container technology, these platforms may provide entry points to machine learning models or analytics web applications. These tools are then accessible online.

Amazon, Google and Microsoft offer free trials, which simply put…


Including comments 😉

A good-looking web application. Source: unsplash.com

Extremely flexible, these standalone tools may contain basic features (e.g. data visualization) as well as advanced functionalities (e.g. label training data for machine learning).

Having a background in web development is not required for data experts. High-level libraries were developed for the most commonly used programming languages in the industry: Shiny (R) and Bokeh (Python).

Below in this article, you will find a total of 24 explained…


Leverage genius work from R community in your projects!

There are more than 16 000 packages on the Comprehensive R Archive Network (CRAN) that gather a lot of commonly used methods in data science projects. Time runs fast, and it may takes days to code functionalities for sometimes basic tasks… Fortunately, we can leverage many packages to focus on what is essential for projects to be successful!

Quick reminder: install and use packages

The most common way is to install a package directly from CRAN using the following R command:

# this command installs tidyr package from CRAN
install.packages("tidyr")

Once the package is installed on your local machine, you don’t need to run this command…

Eric Bonucci

🇫🇷 Data scientist

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