Data Science Analytics -
Use appropriate data analytics and statistical techniques on available data to discover new relations and deliver insights into research problems or organizational processes and support decision-making.

• Apply designated quantitative techniques, including statistics, time series analysis, optimization, and simulation to deploy appropriate models for analysis and prediction
• Effectively use variety of data analytics techniques, such as Machine Learning (including supervised, unsupervised, semisupervised learning), Data Mining, Prescriptive and Predictive Analytics, for complex data analysis through the whole data lifecycle
• Utilize a variety of techniques through the data analysis life cycle.
• Implement data security measures.

Practical Applications -
Identify business challenges suitable for data analysis and apply a structured approach to analyze use cases across diverse business and industrial contexts, ensuring that data-driven insights align with organizational objectives. Additionally, develop tailored data products for various industries and proficiently employ MLOps strategies to deploy real-world solutions, emphasizing the seamless integration of machine learning models into operational workflows.

• Use systems and software engineering principles to organizations information system design and development, including requirements design
• Explain the use Cloud Computing technologies and cloud powered services design for data infrastructure and data handling services
• Develop data products for different industries.
• Utilize MLOps strategies to deploy a real-world solution.