Data Science

This course provides a general overview of the common topics in the data science domain. Students are introduced to data collection, data engineering, machine learning algorithms and packages, data visualization techniques and related programming tools and frameworks.

Data collection, data engineering, machine learning algorithms and packages, data visualization, and related programming tools and frameworks. These topics are covered in DSC 101, but here they will be discussed in more depth, and projects will reflect real world challenges. In addition, data mining techniques and more advanced machine learning algorithms will be introduced. In some cases, the statistical concepts behind some of the algorithms will be discussed. Projects may involve text and image classification tasks, developing regression models, etc.

This course presents the art and science of turning data into readable graphics. We’ll explore the design and creation of data visualizations based on data available and tasks to be achieved. This process includes data modeling, data processing, mapping data attributes to graphical attributes, and strategic visual encoding. Students will evaluate the effectiveness of visualization designs and create their own data visualizations.

This course aims at providing mathematical explanations of the machine learning models discussed in DSC101 and DSC201. Topics include probability, probabilistic models, statistical concepts related to machine learning, and analysis of some of the popular machine learning models with the help of probability and statistics. Projects will reflect real world challenges and will aim at discovering how machine learning models work.

This course will offer programming languages and techniques necessary to process and analyze data. Special emphasis will be on advanced use of Python and R languages to analyze datasets from a variety of disciplines and industries.

Students will apply their data science knowledge and technology to a real world scenario. Students can accomplish this by working with a local businesses, acquiring data from governmental entities, or through an internship. The capstone will conclude with a final deliverable report and presentation to the business, government, or internship entity.

Individual work on special topics in data science.

Study of selected topics in data science.