Data Science in Insurance (online certificate)

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
The goals of the e-learning modules are the following
  • Creating improved awareness around data culture;
  • Sharing a common vision on data topics and main steps of a machine learning process;
  • Discovering practical applications of data science (in Insurance and Finance);
  • Being introduced to technical aspects in a pedagogical way.

Notebooks:

Web pages with pedagogical explanations and examples of code. Exercises and case studies are also attached to these notebooks for your practicing
The goals of the e-learning modules are the following
  • 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.
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Reacfin Academy