Why an online data science course with application in insurance?
Fast development of data science
Data Science is developing at a fast pace in the industry with a lot of use cases and applications being implemented around various topics
Training yourself to these new techniques is essential to be up-to-date and make the most of these new opportunities in your professional career.
A lot of e-learning training or MOOCS are available on-line on the (statistical) techniques themselves but few are oriented towards specific cases studies in insurance
The goals of this online data science certificate are double
Introduce you to the main data sciences techniques from a methodological point of view
But also let your practice data science on specific insurance use cases (including coding in R and/or Python) so that the knowledge you acquire isn’t just theoretical
Structure of the course
The Online Data Science Certificate is composed of 3 pillars
E-learning modules:
Presenting basis of machine learning process, advanced machine learning techniques and data culture applications
Combining methodological lessons with practical use cases and exercises;
Go deeper on methodological aspects of data science;
Apply methodologies on real business cases.
Interactive expert sessions:
Designed to help the students refining their understanding of the concepts presented in the e-learning modules and notebooks and discuss practical applications
A modular approach
Not all of you have the same interests and we therefore offer you the opportunity to select among two complementary training tracks:
Technical track: designed to strengthen your skills with supervised and unsupervised machine learning techniques
Data preparation and visualization track: designed to develop your skills in data preparation (data collection and treatment) and data visualization (including dashboarding) [6 e-learning modules and 4 notebooks]
Another possibility is to choose one coding software (R or Python) and focus on the notebooks linked to this software
Python track: focused to develop your skills with Python [9 e-learning modules and 3 notebooks]
R track: focused to develop your skills with R [9 e-learning modules and 3 notebooks]
Structure of the course
A modular approach – 4 possible tracks
Technical track
Basis of a ML process
Introduction to Python
Introduction to R
Advanced ML techniques
Supervised ML
Unsupervised ML
Data preparation & visualization track
Basis of a ML process
Introduction to Python
Introduction to R
Data culture & applications
Data Preparation pipe-line
Data Vizualisation
Python track
Basis of a ML process
Introduction to Python
Advanced ML techniques
Supervised ML
Data culture & applications
Data Preparation pipe-line
R track
Basis of a ML process
Introduction to R
Supervised ML
Unsupervised ML
Data culture & applications
Data Vizualisation
Interested in knowing more about our online data science course?
Don't hesitate to contact us on learning@reacfinacademy.com for more information.