• Scoring Model. Developed a web application service for predicting the required amount of training and assessing the chance of passing the certification exam using Python, Pandas, Scikit-learn, FastAPI, and Heroku. The model increased the conversion rate of educational program sales from 20% to 70%.• Matching Model. Created scripts for classifying test questions using machine learning (Scikit-learn) and text processing (NLTK) methods, which increased classification accuracy by 10% compared to the manual method and reduced time costs by 90%.• Statistical Analysis. Conducted a study of the causes of unsuccessful attempts to pass the certification exam using Python, Pandas, and statistical analysis methods. The results of the study led to changes in the curricula, which helped to meet the requirements of the certification exams.• Analysis of the strengths and weaknesses of the programs. Researched statistically significant strengths and weaknesses of training programs using data analysis methods (Python, Pandas, Matplotlib). The results of the research were used to prepare a presentation at an international conference on project management.

• Increased profit on the project by 50% (early delivery 1.5 years earlier with full payment). It also increased the company's bonus fund.• Reduced uncertainty in projects (set up planning and project control processes). It also contributed to the reduction of inertia in decision-making.• Built a favorable atmosphere in the company (reduction of conflicts, improvement of communications).