Introduction to Data Science is a foundational course meticulously crafted for students enrolled in the Master’s Degree in Data Science program. This course serves as a gateway to the dynamic world of data science, offering a comprehensive overview of key concepts, methodologies, and tools essential for success in the field. Through a blend of theoretical insights and practical applications, students will delve into the fundamentals of data manipulation, statistical analysis, machine learning, and data visualization, including methods in data acquisition, cleaning, and visualization. Taught in Python using, Pandas, Matplotlib, and Seaborn, the course also includes an introduction to Python, IPython, and Jupyter Notebooks. Emphasizing hands-on learning experiences and real-world case studies, Introduction to Data Science equips students with the necessary skills and knowledge to navigate complex data landscapes, laying a solid groundwork for their journey towards becoming proficient data scientists.
The Statistics course in our Data Science program offers a comprehensive introduction to essential statistical concepts and methodologies crucial for data analysis and interpretation. Through a combination of theoretical teachings and practical applications, students delve into topics such as probability theory, hypothesis testing, regression analysis, and multivariate analysis. Emphasizing hands-on learning experiences and real-world case studies, this course equips students with the necessary tools and techniques to explore, analyze, and draw meaningful insights from data sets of varying complexities. With a focus on statistical reasoning and data-driven decision-making, students develop the foundational skills required to excel in the field of Data Science.
This course provides a comprehensive exploration of big data technologies, focusing on the tools, techniques, and platforms used to store, process, and analyze massive datasets in real-time. Students will learn to harness the power of big data to uncover insights, drive decision-making, and create value within organizations. Key topics include distributed computing frameworks such as Hadoop and Spark, data storage solutions like NoSQL databases and data lakes, and data processing techniques including MapReduce, stream processing, and ETL (Extract, Transform, Load) pipelines. The course emphasizes best practices for managing big data environments, including data governance, security, and scalability considerations. By the end of this course, students will be equipped with the skills and knowledge to design, implement, and manage big data solutions that support data-driven decision-making and foster innovation in various organizational contexts.
The Machine Learning course in our Master’s Degree program in Data Science offers an in-depth exploration of the principles, algorithms, and applications of machine learning techniques. Designed to equip students with the knowledge and skills necessary to tackle complex data analysis and prediction tasks, this course covers a wide range of topics. By the end of the course, students are equipped with the knowledge and practical skills to tackle a variety of machine learning tasks, from classification and regression to clustering and reinforcement learning.
The Data Mining and Visualization course in our Master’s Degree program in Data Science provides students with a comprehensive understanding of the principles, techniques, and applications of data mining and visualization. This course is designed to equip students with the skills necessary to extract meaningful insights from large and complex datasets and to effectively communicate those insights through visualization. By the end of the course, students are equipped with the knowledge and skills to effectively mine and visualize data, making data-driven decisions and communicating insights to stakeholders in various industries and domains.
The Big Data Analytics course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of the principles, techniques, and tools for analyzing and deriving insights from large and complex datasets. This course focuses on equipping students with the skills necessary to navigate the challenges posed by big data, including scalability, variety, velocity, and veracity. By the end of the course, students are equipped with the knowledge and skills to tackle complex big data analytics tasks, making data-driven decisions and driving innovation in the field of Data Science.
The Natural Language Processing (NLP) course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of the principles, techniques, and applications of processing and analyzing natural language data. This course focuses on equipping students with the skills necessary to work with textual data in various forms, including written text, speech, and social media. By the end of the course, students are equipped with the knowledge and skills to tackle complex NLP tasks, making data-driven decisions and driving innovation in the field of Data Science. They are prepared to apply NLP techniques to a wide range of applications and domains, including information retrieval, text mining, sentiment analysis, and conversational AI.
The Deep Learning course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of deep learning principles, techniques, and applications. Deep learning, a subset of machine learning, has emerged as a powerful approach for solving complex problems in various domains, including computer vision, natural language processing, speech recognition, and autonomous systems. By the end of the course, students are equipped with the knowledge and skills to tackle complex deep learning tasks, making data-driven decisions and driving innovation in the field of Data Science.
The Python Programming course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of the Python programming language and its applications in data analysis, machine learning, and other data science tasks. By the end of the course, students are equipped with the knowledge and skills to leverage Python programming for a wide range of data science tasks, making them proficient in one of the most essential tools in the field of Data Science.
The R programming course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of the R programming language and its applications in statistical analysis, data visualization, and data manipulation. By the end of the course, students are equipped with the knowledge and skills to leverage R programming for a wide range of data science tasks, making them proficient in one of the most essential tools in the field of Data Science. They are prepared to tackle complex data analysis challenges and drive innovation in various industries and domains.
The Data Ethics and Privacy course in our Master’s Degree program in Data Science is designed to provide students with a comprehensive understanding of the ethical and privacy considerations inherent in the practice of data science. In an age where data is increasingly valuable and pervasive, it’s essential for data scientists to navigate ethical dilemmas and ensure the responsible use of data. By the end of the course, students are equipped with the knowledge and skills to make ethically sound decisions and to promote responsible data practices in their future careers as data scientists.
The Capstone Project course in our master’s degree program in Data Science is the culmination of the student’s learning journey, providing them with the opportunity to apply the knowledge and skills acquired throughout the program to solve real-world data science problems. The Capstone Project serves as a comprehensive, hands-on experience that allows students to demonstrate their proficiency in data science methodologies, tools, and techniques. Through the Capstone Project course, students gain valuable practical experience in applying data science techniques to real-world problems, honing their analytical, problem-solving, and communication skills.
Applied Learning Practicum (3 credit hours) offers students the opportunity to put their theoretical knowledge into practice through real-world experiences like internships or work placements. These experiences can take various forms: (1) alternative work/study, internships, cooperative education, (2) employment relevant to the student’s field of study, or (3) collaborative projects with faculty applying coursework to professional contexts. Before beginning their field placement work, students must ensure the University has a Collaborative/Cooperative Agreement with the practicum or internship site, and department approval is necessary to ensure alignment with the program of study. This course is a recurring requirement for all enrolled students, emphasizing its importance as part of executive formatted programs, ensuring students engage with practical applications throughout their academic journey.
