Upon completion of the course, the student will be able to:

1. 1. Demonstrate deep expertise in probability theory and statistics, including contemporary developments, supporting independent research, creative thinking, and critical evaluation of complex interdisciplinary problems.
   2. Critically evaluate and apply advanced principles to build modeling-based computational systems in data science and related fields with originality.
   3. Innovatively apply probabilistic, statistical, and data science methods to natural sciences, showing originality and critical thinking in uncertain and complex contexts.
   4. Justify and critically assess theoretical foundations of their specialization, including contemporary knowledge, to develop new approaches and conduct independent research.
   5. Analyze and apply modern methods and models of data analysis, synthesizing knowledge from multiple domains to make independent, original decisions in the presence of uncertainty.
   6. Demonstrate advanced knowledge of machine learning algorithms, including supervised and unsupervised learning, for innovative applications in complex data environments.
2. 1. Apply mathematical, statistical, and data science methods to independently conduct research and solve theoretical and applied problems in complex and uncertain environments.
   2. Critically evaluate and adapt emerging methods in fast-developing data science and statistics fields, developing unique strategic approaches for interdisciplinary integration.
   3. Design, analyze, and model large-scale data systems, formulate original statistical and data science models for interdisciplinary challenges, and make decisions based on critical assessment and independent judgment.
   4. Develop innovative algorithms using programming tools to solve contemporary problems, contributing new knowledge and methods through original research.
   5. Apply advanced Python algorithms to address complex and novel contexts, transforming them into research-based innovations through critical evaluation.
   6. Create and optimize databases and resources, integrating knowledge across domains to support research, independent decision-making, and teamwork.
   7. Develop and implement large-scale big data solutions using Hadoop or Spark, integrating these with statistical and data science models in complex, uncertainty-rich, large-data environments.
3. 1. Strategically use diverse sources to obtain information and present it critically in complex, uncertain research contexts.
   2. Organize, analyze critically, and synthesize information to draw original conclusions and provide new insights to professional knowledge.
   3. Process data, make strategic decisions in complex situations, conduct discussions and debates with originality, and present results with critical thinking and independence.