# Data Science, MS

The College of Computer and Information Science (CCIS) and the Department of Electrical and Computer Engineering (ECE) jointly offer a new interdisciplinary Master of Science program in data science. This program is designed to give students a comprehensive framework for processing, modeling, analyzing, and reasoning about data. Students will engage in an extensive core intended to develop depth in computational modeling, data collection and integration, data storage and retrieval, data processing, modeling and analytics, and visualization. Students will also be given a variety of elective areas in the College of Computer and Information Science, the College of Engineering, and throughout the campus to explore key contextual areas or more complex technical applications. Successful program graduates will be well positioned to attain data scientist and data engineer positions in a fast-growing field or to progress into doctoral degrees in related disciplines.

The Master of Science in Data Science is comprised of eight courses; five core courses and three electives. The core courses are designed and developed by the CCIS and ECE faculty. Elective courses consist of graduate courses offered in CCIS, the College of Engineering, and other partner colleges.

## Course Requirements

The MS in Data Science curriculum requires five core courses that represent the essential mathematical/statistical and technical knowledge for deep data analysis. These courses examine foundational programming concepts and languages, integration, collection, storage, retrieval, large-scale computing, mathematical concepts in statistics, linear algebra, and optimization, as well as visual and computational analysis, machine learning, and visualization. The courses are tailored toward technically or mathematically trained students.

The five core courses include:

- Two core courses in algorithms and data processing
- Two core courses in machine learning and data mining
- One core course in information visualization

Three elective courses are drawn from a selection of courses across Northeastern.

**Placement Exams**

Each incoming masters student, regardless of his or her background, takes two placement exams administered one week prior to the beginning of the semester. The two exams cover fundamentals of computer science and programming skills and basic statistics, probability, and linear algebra. If the student does not get a B or above in a part of the placement exam, then the student must take the corresponding introductory course.

- (DS 5010) The introductory course on fundamentals of programming and data structures covers data structures (lists, arrays, trees, hash tables, etc.), program design, programming practices, testing, debugging, maintainability, data collection techniques, and data cleaning and preprocessing. This course will have a class project where the students will use the concepts they learn to collect data from the web, clean, and preprocess and ready for analysis.
- (DS 5020) The introductory course on basics of statistics, probability, and linear algebra covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes' rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues and eigenvectors.

## Required Course Work

A grade of B or higher is required in the following courses:

Code | Title | Hours |
---|---|---|

Algorithms | ||

Complete 4 semester hours from the following: | 4 | |

Algorithms | ||

Fundamentals of Computer Engineering | ||

Data Management and Processing | ||

DS 5110 | Introduction to Data Management and Processing | 4 |

Machine Learning and Data Mining | ||

DS 5220 | Supervised Machine Learning and Learning Theory | 4 |

DS 5230 | Unsupervised Machine Learning and Data Mining | 4 |

Presentation and Visualization | ||

DS 5500 |

## Electives

Code | Title | Hours |
---|---|---|

Complete 12 semester hours from the following: | 12 | |

College of Computer and Information Science | ||

Information Retrieval | ||

Foundations of Artificial Intelligence | ||

Natural Language Processing | ||

Social Computing | ||

Empirical Research Methods | ||

Special Topics in Artificial Intelligence | ||

Special Topics in Database Management | ||

College of Engineering | ||

Special Topics in Civil Engineering | ||

Computer Vision | ||

High-Performance Computing | ||

Detection and Estimation Theory | ||

Information Theory | ||

Combinatorial Optimization | ||

Advanced Computer Vision | ||

Advanced Machine Learning | ||

Data Mining for Engineering Applications | ||

Data Mining in Engineering | ||

Statistical Methods in Engineering | ||

College of Social Sciences and Humanities | ||

Dynamic Modeling for Environmental Decision Making | ||

Big Data for Cities | ||

Geographic Information Systems for Urban and Regional Policy | ||

Urban Theory and Science | ||

Advanced Spatial Analysis of Urban Systems | ||

Perspectives on Social Science Inquiry | ||

Research Design | ||

Quantitative Techniques | ||

D'Amore-McKim School of Business | ||

Business Analytics Fundamentals | ||

Predictive Analytics for Managers | ||

Introduction to Big Data and Digital Marketing Analytics | ||

College of Science | ||

Statistics for Bioinformatics | ||

Complex Networks and Applications | ||

Statistical Physics | ||

Computational Physics | ||

Network Science Data | ||

Bouvé College of Health Sciences | ||

Epidemiology and Population Health | ||

Epidemiology | ||

Biostatistics in Public Health | ||

Social Epidemiology | ||

College of Arts, Media and Design | ||

Game Design and Analysis | ||

Game Analytics |

*Note: *Students that take 3-credit-hour elective courses (ie Bouvé, CSSH courses) will register for an accompanying data science project course ( (DS 8982)). In order to earn this additional credit, students will be expected to work with faculty to design an additional project in line with the curricular aims of their chosen elective and the data science core learning outcomes.

## Program Credit/GPA Requirements

32 total semester hours required

Minimum 3.000 GPA required