NUMERICAL MATHEMATICS FOR DATA SCIENCE

Course Description

This course provides an understanding of numerical mathematic applications in data science. The floating-point representation of real numbers, truncation and round off errors, iterative approaches, and convergence are some of the main points in numerical mathematics that are covered in this course. Students will study the most basic and crucial algorithms for the fundamental numerical mathematics problems, such as the solution of algebraic equations, numerical estimation of derivatives and integrals, solution of differential equations, approximation of functions by polynomials and Fourier series and solution of systems of linear algebraic equations. Upon completion of this course, students will be able to formulate and solve problems using mathematical methods and tools, identify, understand, and solve algebraic equations and develop experience with numerical and symbolic mathematical software.

NUMERICAL MATHEMATICS FOR DATA SCIENCE

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Course outcomes

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Course Syllabus

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Learning Outcomes

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Program Outcomes

1. Demonstrate advanced knowledge of core concepts and theoretical frameworks in Management Information Systems, and apply them to develop innovative models that reflect current advancements in the field.
2. Analyze complex organizational problems and design integrated information systems solutions by applying advanced MIS knowledge and synthesizing multiple theoretical perspectives.
3. Develop and manage database systems and data-driven applications that ensure effective storage, retrieval, and use of organizational data in real-world contexts.
4. Utilize data analytics, business intelligence tools, and data visualization techniques to extract actionable insights and support strategic, data-driven decision-making.
5. Evaluate and apply emerging technologies such as AI, IoT, and Blockchain to drive digital transformation, enhance organizational performance, and formulate original research inquiries.
6. Conduct independent and collaborative research using appropriate methodologies, and communicate findings effectively through scholarly writing and presentations suited to diverse audiences.
7. dentify and address ethical, legal, and societal implications in the research, development, and use of information technologies, adhering to scientific and professional standards.
8. Collaborate effectively in multidisciplinary teams and demonstrate leadership in addressing complex technology-driven challenges.
9. Apply project management methodologies and tools to plan, execute, and monitor IT projects within organizational settings.
10. Develop and integrate information systems that align with organizational goals and strategies, supporting innovation and sustainable growth.
11. Perform comprehensive systems analysis and design, including requirements gathering, modeling, prototyping, and evaluating enterprise system architectures such as ERP, CRM, and SCM.
12. Assess and mitigate organizational information security risks by applying appropriate principles, technologies, and best practices in cybersecurity.

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