ALGORITHMS FOR DATA SCIENCE

Course Description

This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.

ALGORITHMS FOR DATA SCIENCE

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Course outcomes

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Course Syllabus

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Learning Outcomes

1. L01 Explain algorithm and analyze algorithm complexity SOLO 4
2. L02 Process binary search trees and heaps SOLO 3
3. L03 Perform hashing techniques SOLO 4
4. L04 Compare different sorting algorithms and perform the most efficient sorting technique SOLO 4
5. L05 Compare different searching algorithms and perform the most efficient searching technique SOLO 4
6. L06 Analyze graphs and implement breadth-first search algorithm SOLO 4
7. L07 Perform dynamic programming techniques SOLO 4

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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