Data Science in Insurance (online certificate)

Why an online data science course with application in insurance?

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 Python) so that the knowledge you acquire isn’t just theoretical

Structure of the course

The Online Data Science Certificate is composed of 2 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
  • a. Creating improved awareness around data culture;
  • b. Sharing a common vision on data topics and main steps of a machine learning process;
  • c. Discovering practical applications of data science (in Insurance and Finance);
  • d. 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
  • a. Combining methodological lessons with practical use cases and exercises;
  • b. Go deeper on methodological aspects of data science;
  • c. Apply methodologies on real business cases.

A flexible 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 collection and visualization track: designed to develop your skills in data collection (scraping, text mining, natural language processing) and data visualization (including dashboarding)

Content of the course

E-learning modules

The e-learning modules are divided in 3 blocks containing each 3 e-learning modules

1. Basis of a machine learning process
(mandatory for both tracks)
  • Data Preparation and Data Quality
  • Introduction to Machine Learning
  • Introduction to Data Visualization

2. Advanced machine learning techniques
(included in technical track)
  • Supervised machine learning part 1
  • Supervised machine learning part 2
  • Unsupervised machine learning
3. Data culture and applications
(included in data collection and visualization track)
  • Text Mining
  • Scraping
  • Advanced data visualization

Certification on the e-learning modules will be organized through quiz you will have to answer at the end of the e-learning modules


The notebooks are divided in 3 blocks containing each 2 notebooks
1. Introduction to data science software
(these 2 notebooks are optional for the participants already familiar with these software)
  • Introduction to R
  • Introduction to Python

2. Machine learning process
(included in technical track)
  • Machine learning process for supervised machine learning with application in insurance pricing (in Python)
  • Machine learning process for unsupervised learning with application in clustering life insurance contracts (in R)
3. Scraping and Data Visualization
(included in data collection and visualization track)
  • Introduction to scraping techniques (in Python)
  • Data Visualization with application in insurance and dashboarding (in R)

Certification on the notebooks will be organized through exercises/cases studies you will have to solve after the notebooks’ completion

Interested in knowing more about our online data science course?

Don't hesitate to contact us on for more information.