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.
9
Jan 2024

Manufacturing Trends to Lookout for in 2024

English
3
Aug 2023

Embracing IIoT: The Smart Path for OEMs to Thrive with a Leading IIoT Smart Manufacturing Partner

English
21
Jul 2023

CDAP: $15,000 Grant To Jump Start Canadian Manufacturing Digital Transformation Projects

English

Related articles

Back to the blog
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
10
Apr 2024

Définir la différence entre le temps de production et le temps de cycle

Le temps de cycle et le temps de production sont des indicateurs clés de la performance manufacturière. Découvrez ce que chacun signifie et comment les utiliser pour augmenter la productivité dans le cadre de votre processus de production.

French
13
Mar 2024

Moteurs d’efficacité manufacturière : Monitoring des machines et du TRG

Le monitoring des machines et du TRG (taux de rendement global) sont efficaces pour améliorer l'efficacité globale, mais quelle est la différence entre eux et qu'est-ce qui est le mieux adapté à vos opérations?

French
13
Mar 2024

Engines of Manufacturing Efficiency: Machine Monitoring and OEE

Machine Monitoring and OEE (Overall Equipment Effectiveness) are effective at boosting overall efficiency, but what is the difference between them and what is best for your operations?

English