16 May
2019

Top 3 Skills of a Data Scientist

Discover the main skills of a data scientist!

Human Resources
Industry 4.0
Smart Factory
Top 3 Skills of a Data Scientist

data analyticsNowadays, several technologies allow companies to obtain a large amount of data. The return on investment of these technologies depends largely on the actions that are taken from the data collected. Businesses are changing and increasingly feel the need to base their decisions on solid, reliable data. You will not be surprised to learn that the role of a data scientist is a role increasingly in demand within organizations.

What is a data scientist?


The role of data scientist is to manage, analyze and interpret the data, while taking into account the organizational reality, which requires a very developed business sense.

3 fundamental areas of expertise of a data scientist:

Technical competencies:

The data scientist must have programming knowledge with languages such as R or Python as well as knowledge of computer architecture and databases. He or she must be able to adapt to different IT environments and have intellectual agility to learn and constantly adopt new methods. This person must master data manipulation and be comfortable with several different data structures.

Analytical competencies:

The role of data scientists requires to be an expert in solving complex problems. In this category are skills in advanced statistics, machine learning, advanced mathematics, modeling, simulations, artificial intelligence, etc. In general, the fields of study in science, technology, engineering, mathematics and physics make it possible to develop the analytical skills sought and make it possible to practice solving scientific problems.

Business competencies:

The data scientist must understand the corporate environment in which the data evolves. In the field of data science, successful projects are those that are based on a specific situation and that end with concrete solutions that can be integrated into the work environment.

 

 

Data scientists are in demand more than ever. It must be remembered, however, that their success requires first and foremost reliable and sufficient data. Real-time production tracking solutions or data analytics are definitely avenues to consider!

 

Want to learn more?
Download the ebook
Related blog articles

Related articles

Back to the blog
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
17
Sep 2019

Types de gaspillages dans le lean manufacturing - le gaspillage dû à l'attente

French
10
Sep 2019

Types de gaspillage dans la production lean -  le gaspillage provenant des défauts de fabrication

French
15
Oct 2019

Comment calculer le débit dans l'industrie de la viande

French

Related articles

Back to the blog
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
5
Jun 2018

Advanced Deep Learning AI Heatmap

Insight into the areas within specific sectors where deep neural networks can potentially create the most value.

English
2
Mar 2018

Blog Review: How AI Will Change Businesses Decision Making

We review the article from the Supply Chain Game Changer Blog about Artificial Intelligence, AI data-based models for better decision making and augmented intelligence which will eventually spread to manufacturing.

English
12
Apr 2018

Smart Factory Analytics: Creating New Business Opportunities

This post explains how implementing smart factory analytics can change what you previously thought your factory was capable of. Instead of thinking we don’t do that, you can look at your production data and figure out how could we do that?

English