To achieve full understanding of the use and application of ML algorithms, our participants will work on a real-life industry project, translating theoretical knowledge to practical process and overcoming realistic challenges.
Scope:~400 work hours total
Data:Real data provided by company
Guidance:Experienced mentors provided by Y-DATA
Support:Weekly meetings with company data-owner
Predicting formation of dry areas in DSW evaporation pondsPredict the formation of dry areas in both the Salt and Carnallite Ponds
Port mapping using behavioral vessel dataUse vessel behavioral data in order to map port related areas of interest.
Speeding up Transformer-based NLP modelsTrain NLP models based on BERT, ELECTRA, ROBERTA and other models using a GPU, and then experiment with various methods to reduce their complexity and run times on a CPU.
Full project cycle
The process of working on the project follows popular industry standards and methodologies and incorporates a growing set of tools the students possess to methodically understand and solve a real-world problem. Our students have a full-cycle data science project in their portfolio upon graduation, covering all industry-standard stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation.
Example ProjectAutomatic detection of low-value queries in technical Q&A forum
A customer operates a forum where programmers ask each other questions, provide answers and rate questions giving them \"ups\" and \"downs\". The forum has a core expert community that provides good answers and valuable insights. However, they often waste their time handling questions of little to no value: marking questions as duplicates and redirecting them, closing topics with incoherent or irrelevant questions etc. Because of this, the overall efficiency of the system suffers.