At the end of this BOOST program, you will be able to:

• Transform relevant variables of a classic supervised model (feature engineering).
• Train a classic supervised model that meets business expectations.
• Evaluate the performance of a classic supervised model.
• Fine-tune the hyperparameters of a classic supervised model.

Prerequisites

To enroll in this program, you need to meet the following conditions:

• Language Level: you will need to provide a certificate not older than 2 years to demonstrate a minimum level of English B2. Certificates accepted:
  • English Diploma from a secondary institution (private or public) or an accredited higher learning institution
  • Linguaskill minimum score: 160
  • BULATS minimum score: 60
  • TOEIC minimum score: 785
  • IELTS minimum score: 6.5
  • TOEFL iBT minimum score: 88
  • Cambridge FCE / CAE / BEC HIGHER / BEC VANTAGE minimum score or grade: B or 160
  • Certificate from a language center demonstrating a minimum level of B2 (including the number of training hours completed and the company signature or stamp)

    Don’t have one of these certificates? Find out where to take an exam.

• Equipment:
  • Access to a computer (PC or Mac) with a microphone, webcam, and good Internet access.
  • To complete this project, your computer will also need to meet the following requirements:
    • CPU: 2 x 64-bit, 2.8 GHz, minimum core I3 or AMD Ryzen 3 (ideally core I7 or AMD Ryzen 7),
    • RAM : 8 Go min (ideally 16 Go, but it's possible to use Google Colaboratory for the larger datasets in the program, with a free account).
    • Storage : 512 Go, SSD ideally. 

• Academic prerequisites: 
  • a bachelor's degree, or equivalent, in computer science, economics, finance or data analysis;
  • or a an associate's degree, or equivalent, and at least 2 years of professional experience in computer science, economics, finance or data analysis;

All other candidates will need to go through a specific admissions process. These candidates must have:

• an associate's degree, or equivalent, in any subject and at least 1 year of professional experience in any field
• and a completed placement test consisting of:
  
  • OpenClassrooms course completion certificates from Perform an Initial Data Analysis and Use Python Libraries for Data Science;
  • a personal data analysis project, accompanied by a 10-minute recorded video that explains the project.

Recommended Skills

To ensure success in your BOOST program, we recommend having already mastered the following skills:

• Use specialized Python libraries to conduct an exploratory data analysis.
• Perform data cleaning on structured data.
• Conduct a univariate statistical analysis.
• Conduct a multivariate statistical analysis.
• Communicate exploratory results using clean and relevant data visualizations.

If you need to get these skills, they can be acquired by taking the BOOST program "Exploratory Data Analysis with Python - Expert".