# Data Science Courses

### Tool Courses

- DSC 1010, Introduction to Data Science (3)
- CSM 4015, Senior Capstone (1)
- MTH 1151, Elementary Statistics for the Sciences (3)
- CSC 1110, Programming I (3)
- CSC 1120, Programming II (3)

**3 Hours**

An interest and/or curiosity for computing, data exploration, and statistics is recommended. Successful students will have sold math skills taught in high-school algebra and pre-calculus courses. This course will introduce students to this field and equip them with some of its basic tools as well as its general mindset. The focus in the treatment of these topics will be on breadth, rather than depth, with an emphasis on problem-based learning. Real data sets from a variety of disciplines will be used. Students who complete this course will learn the steps involved in data collection, exploratory data analysis, modeling, data visualization, and effective communication.

**1 Hour*** Prerequisites:* XXX 3015 Junior Cornerstone Seminar

*and*ENG 3010

*or*ENG 3950 Third Year Writing

This course is a culminating experience in the major, which also addresses the goals for the Senior Capstone as defined in the course description for GND 4015. These goals include reflection on the students’ whole educational experiences and on their transition from the university setting to post-graduation

**3 Hours****Prerequisites:***Math ACT score greater than or equal to 22, Math SAT score greater than or equal to 520, * *Math RSAT score greater than or equal to 550*, *Belmont Math Placement Test score greater than or equal to 20, MTH 1010, MTH 1110, or MTH 1130.*

The study of statistical procedures widely used in the sciences. Topics include, in addition to those taught in MTH 1150, modeling with probability distributions, multiple regression, analysis of variance, chi-square tests, nonparametric statistics, and bootstrapping. Analysis of data using computer software will relate to the sciences. Special emphasis will be placed on the communication of statistical results from scientific research. Credit is not allowed for this course if the student already has credit for MTH 1150.

**3 Hours****Prerequisites:***Math ACT score greater than or equal to 27, Math SAT score greater than or equal to 610, Belmont Math Placement Test score greater than or equal to 27,* CSC 1020, *or* MTH 1130

An introduction to computer organization, algorithm development, and programming.

**3 Hours*** Prerequisites:* CSC 1110

A continuation of algorithm development and programming, including basic aspects of string processing, recursion, internal search/sort methods, and simple data structures.

### Major Requirements

- DSC 1000, Seminar in Data Science (1)
- MTH 1210, Calculus I (4)
- MTH 1220, Calculus II (4)
- DSC 2010, Statistical Computing (3)
- MTH 2030, Topics in Statistics (3)
- CSC 2020, Database Systems (3)
- DSC 4900, Data Science Project and Portfolio (3)

**1 Hour**

This course is required for all data science majors and is to be taken during the first spring semester after declaring Data Science as a major. The seminar provides an orientation to the field of data science and the study of data science at Belmont. Students will learn about data science coursework and curriculum, student organizations, research and other extracurricular opportunities for students, careers for data science graduates, and graduate study in data science.

**4 Hours****Prerequisites:***Math ACT score greater than or equal to 27, Math SAT score greater than or equal to 610, Math RSAT score greater than or equal to 630, Belmont Math Placement Test score greater than or equal to 27, or MTH 1130*

An introduction to analytical geometry, limits, integration, and differentiation.

**4 Hours****Prerequisites:***MTH 1210; or MTH 1170 with co-req MTH 1190 *

Further techniques of integration with applications; exponential and logarithmic functions; parametric equations; and sequences and infinite series.

**3 Hours*** Prerequisites:* MTH 1151 or equivalent course

Students will learn to manipulate data and process information using an appropriate programming language (e.g. R, Python). No previous programming experience is assumed. Students will use data from multiple sources including experiments or surveys, local industry/non-profit partners, and public data sets. Throughout the course students will analyze data using statistical and data science techniques. Students will become proficient at selecting the data of interest from large tabular sources, and applying sample aggregate functions (e.g. max, mean) as well as more complex statistical tests. Appropriate use of computers and software will be integrated into the laboratory and data analysis experience.

**3 Hours****Prerequisites:***MTH 1151 and MTH 1162*

Topics will be selected from the following: experimental design, sampling, nonparametric methods, time series analysis, categorical analysis, multivariate analysis, and advanced regression analysis. Course offerings and topics will appear in the schedule of classes. This course may be repeated for credit for different topics. This course will not count toward either a major or a minor in mathematics.

**3 Hours*** Prerequisites:* CSC 1110 or DSC 2010

An introduction to database management system concepts and applications. Students will practice the design and implementation of relational databases, and use SQL to make accurate and efficient queries. Students will also work with unstructured, NoSQL databases, and learn the tradeoffs in efficiency and utility between different database paradigms. They will become proficient at accessing and manipulating data, through both direct, command-line interfaces and libraries embedded within more general programming languages.

**3 Hours*** Prerequisites:* Consent of Instructor

This course provides students with an opportunity to make connections among ideas and experiences gained from foundations in mathematics, computer science, and statistics, and apply them to another domain. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data, to applying suitable and appropriate analytics methods to the problem, and communicating the results in a clear and comprehensible way. The course will emphasize a good understanding of the foundational knowledge of the core discipline and the domain area and prepares students for future professional endeavors.