Data Science (BSc)

Program Overview

The Data Science program at the University of Nicosia is a 4-year program that combines theory and practice in computer science, statistics, and mathematics. It emphasizes hands-on learning, real-world application, and electives for specialization. Graduates are prepared for careers in the data-driven economy and further postgraduate education in data science and related fields.

Program Outline

Outline:

Structure and Course Schedule:

The Data Science program at the University of Nicosia is a 4-year program with a total of 240 ECTS credits. The program is divided into 8 semesters, with each semester consisting of 6 modules. The program combines theory and practice, focusing on three main disciplines: Computer Science, Statistics, and Mathematics, as well as real-world application domains.

Module Descriptions:

(Section A: Computer Science Requirements)

• COMP-111 Programming Principles I & COMP-113 Programming Principles II: These modules introduce the fundamentals of programming, focusing on problem-solving and algorithm design.
• COMP-140 Introduction to Data Science & COMP-142 Software Development Tools for Data Science: These modules provide students with the essential tools and techniques for data science, including data acquisition, cleaning, and analysis.
• COMP-211 Data Structures & COMP-240 Data Programming: These modules focus on data structures and algorithms used in data science, as well as programming skills for data manipulation.
• COMP-242 Data Privacy and Ethics: This module explores the ethical and privacy concerns associated with data collection and analysis.
• COMP-244 Machine Learning and Data Mining I & COMP-248 Project in Data Science: These modules introduce machine learning and data mining concepts and methods, and students apply those concepts to a data science project.
• COMP-302 Database Management Systems & COMP-340 Big Data: These modules explore databases and big data management and analysis techniques.
• COMP-342 Data Visualization & COMP-344 Machine Learning and Data Mining II: These modules focus on data visualization techniques and advanced machine learning methods.
• COMP-370 Algorithms & COMP-405 Artificial Intelligence: These modules cover advanced algorithms and artificial intelligence concepts for data science applications.
• COMP-446 Web and Social Data Mining & COMP-447 Neural Networks and Deep Learning: These modules explore data mining from web and social media sources and advanced deep learning techniques.
• COMP-494 Data Science Final Year Project I & COMP-495 Data Science Final Year Project II: These modules allow students to work on a real-world data science project, applying the knowledge and skills acquired throughout the program.

(Section B: Mathematics and Statistics Requirements)

• MATH-101 Discrete Mathematics, MATH-195 Calculus I, MATH-196 Calculus II: These modules provide a foundational understanding of essential mathematical concepts.
• MATH-225 Probability and Statistics I & MATH-280 Linear Algebra I: These modules introduce the basic principles of probability, statistics, and linear algebra.
• MATH-325 Probability and Statistics II & MATH-326 Linear Models I: These modules delve deeper into probability, statistics, and linear regression models.
• MATH-329 Bayesian Statistics & MATH-335 Optimization Techniques: These modules explore Bayesian statistics and optimization techniques for data analysis.

(Section C: Major Electives)

• Students can choose from a range of elective courses in data science, computer science, and related fields to personalize their learning and focus on specific areas of interest.

(Sections D-G: Additional Electives)

• The program offers electives in various subjects such as biology, chemistry, business, languages, and liberal arts, allowing students to broaden their knowledge and develop additional skills.

Assessment:

Assessment Methods:

• The program employs various assessment methods, including:
• Final Exams:
Comprehensive exams at the end of each semester covering the material of the modules.
• Continuous Assessment: Ongoing assessments throughout the semester, typically involving assignments, quizzes, and projects.

Grading:

• Letter grades are assigned based on the final exam and continuous assessment weightage.
• A minimum cumulative grade point average (CPA) of 2.0 is required for graduation.

Teaching:

Teaching Methods:

• The program uses a variety of teaching methods, including:
• Lectures:
Traditional lectures delivered by professors to introduce key concepts and theories.
• Tutorials: Smaller group sessions focused on problem-solving and practical application of the material.
• Laboratory sessions: Hands-on sessions where students apply data science tools and techniques to real-world problems.
• Project work: Individual or group projects requiring students to apply their learning to complex datasets and problems.

Faculty:

• The program is taught by experienced and qualified faculty members from the Department of Computer Science.
• The faculty members have expertise in various areas of data science, computer science, and statistics.

Unique Approaches:

• The program emphasizes hands-on learning and problem-solving through laboratory sessions and projects.
• The program focuses on real-world application of data science techniques, ensuring graduates are prepared for the demands of the data-driven economy.

Other:

• The program aims to provide students with the skills and knowledge to meet the demands of the data-driven economy.
• The program prepares graduates for further postgraduate education and research in data science and related fields.
• The program emphasizes a strong sense of social commitment, global vision, and independent learning ability.
• Students have access to various academic support services, including the NEPTON English Placement Test and English language support classes.
If additional information becomes available, the extraction can be updated accordingly.