This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study. Further information is available at CHC PATHS (honors.umass.edu/chc-paths/). REGISTRATION SHOULD BE DONE DURING PRE-REGISTRATION AND COMPLETED BEFORE THE END OF THE ADD-DROP PERIOD.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks.

The design and analysis of efficient algorithms for important computational problems. Emphasis on the relationships between algorithms and data structures and on measures of algorithmic efficiency. Sorting (heapsort, mergesort, quicksort), searching, graph algorithms. Experimental analysis of algorithms also emphasized. Use of computer required.

The design and analysis of efficient algorithms for important computational problems. Emphasis on the relationships between algorithms and data structures and on measures of algorithmic efficiency. Sorting (heapsort, mergesort, quicksort), searching, graph algorithms. Experimental analysis of algorithms also emphasized. Use of computer required.

Embedded Computational Systems is a hands-on laboratory for building integrated systems. We develop analytical skills and discuss sensors, motor drivers, I/O, control logic, and operating systems for embedded applications. Students build several individual projects and invent one integrated, semester-long team project.

Embedded Computational Systems is a hands-on laboratory for building integrated systems. We develop analytical skills and discuss sensors, motor drivers, I/O, control logic, and operating systems for embedded applications. Students build several individual projects and invent one integrated, semester-long team project.