Certificate in Data Science Courses
COS102 Introduction to Computer Programming (3 credits)
This course delves into the fundamentals of computer programming, focusing on programming methodology, procedural abstraction, and an introduction to object-oriented programming using Python. Through a hands-on approach, students will engage in integrated lab sessions during lectures, ensuring practical application of concepts throughout the course. Prerequisite: None
COS331 Data Mining (4 credits)
Throughout this course, students will delve into fundamental principles and algorithms essential for extracting actionable insights from raw data. Core topics encompass data preprocessing, exploratory analysis, dimensionality reduction, classification, clustering, association rule mining, and anomaly detection. Engaging with real-world datasets and case studies spanning various domains including business, science, security, and healthcare, students will gain practical experience and insights into the application of these techniques in diverse contexts. Prerequisite: MAT201, COS211, DAS241
DAS101 Introduction to Data Science (3 credits)
This course introduces students to the fundamentals of data science, covering essential concepts, tools, and techniques used in analyzing and interpreting data. Through a combination of lectures, practical exercises, and projects, students will gain hands-on experience in data manipulation, visualization, and analysis. Prerequisite: COS102
DAS241 Data Visualization (3 credits)
This course introduces students to the principles and techniques of data visualization using the R programming language. Through hands-on projects and theoretical concepts, students will explore various visualization libraries and tools available in R to effectively communicate and analyze data. Prerequisite: COS102, STA101 or COS211
DAS341 Business Data Analysis (3 credits)
This course introduces core statistical techniques of data retrieval, analysis and modeling used by business professionals to make an efficient data-driving decision. It also covers the topics of effective interpretation of data and statistical results in business world. Prerequisite: STA101
STA101 Introduction to Statistics (3 credits)
This course is an introductory course in statistics intended for students in a wide variety of areas of study. Topics covered include basic descriptive measures (histograms, average, and standard deviation etc.), probability theory, statistical inference, confidence intervals, hypothesis tests and regression with applications in the real world. In addition, students will learn and use statistical programming language R to help understand and perform select statistical analyses. Prerequisite: None