Data Science and Machine Learning

Overview

The Bachelor of Science in Data Science and Machine Learning is an innovative and interdisciplinary program designed to prepare students for the rapidly evolving fields of data science, artificial intelligence, and machine learning. This program combines rigorous training in mathematics, statistics, computer science, and specialized machine learning algorithms to provide students with a comprehensive understanding of the principles and practices underlying the analysis, interpretation, and management of large data sets.

Throughout the program, students engage with real-world data sets and projects, developing skills in data preprocessing, visualization, statistical inference, predictive modeling, and algorithmic design. The curriculum is structured to not only impart technical proficiency in programming languages such as Python and R and tools like Tensor Flow and PyTorch but also to foster critical thinking, problem-solving, and ethical considerations in data science practices. Graduates of this program are well-equipped to tackle complex data-driven challenges and contribute to innovations across a wide range of industries, including technology, finance, healthcare, and beyond, positioning them at the forefront of the digital economy.

Goals

1. Foundational Knowledge: Providing students with a solid foundation in mathematics, statistics, and computer science, which are crucially important for understanding and applying data science and machine learning techniques.
2. Data management skills: Teaching students how to collect, organize, manage, and store large data sets effectively to ensure data quality and accessibility for analysis.
3. Providing students with analytical skills: Developing students’ ability to analyze and interpret data, using statistical methods and data visualization techniques to extract ideas and guide the decision-making process.
4. Master the concept and use of machine learning: Providing students with a comprehensive understanding of various machine learning algorithms and models including supervised, unsupervised and enhanced learning, along with the skills needed to implement these models to solve real-world problems.
5. Providing students with programming experience: Ensuring that students are proficient in the programming languages commonly used in data science and machine learning, such as Python and R, in addition to getting students familiar with relevant libraries and frameworks.
6. Ethical and Legal Awareness: Instilling an understanding of the ethical considerations and legal implications related to data privacy, data security and the use of artificial intelligence and prepare students to deal with these challenges in their professional work.
7. Problem Solving Skills: Developing students’ ability to apply data science and machine learning techniques to solve complex problems across various domains such as healthcare, finance and technology.
8. Communication Skills: Enhancing students' ability to communicate complex data findings to specialized and non-specialized audiences effectively through clear visualizations and convincing narratives.
9. Research ability: Enhancing research skills that enable students to undertake independent projects and contribute to field knowledge as well as keep up with emerging trends and technologies.