Data Science B.S. and Minor
Data Leading to Targeted Recommendations
How does a music streaming app predict what new music you will like? What factors influence your recommendations on social media? Enter the data scientist. They are able to process large amounts of data and extract targeted recommendations. The B.A. in data science program will teach you how to:
- process and clean data
- model trends appropriately
- draw conclusions informed by mathematically sound techniques
- employ machine learning and how to effectively communicate your findings
From Calculus to Real-World Data Problem Solving
Your journey into data science will begin with probability and statistics. You will work your way through calculus and introductory statistics, then move on to the core data science sequence of Math 245, 345 and 445. Along the way, you will develop proficiency in industry-standard software from your mathematics courses, as well as some introductory programming and machine learning. You’ll also learn about industry ethics and technical writing, which will improve your ability to relay technical information to a non-technical audience. Your study will culminate in a seminar, where you will be able to put all of your training into action as you work on real-world big data problems.
High Demand and High Salaries
RIC’s B.S. degree in data science provides a pathway into one of the most in-demand careers, with one of the highest starting salaries. From startups to Fortune 500s to government agencies, organizations are capitalizing on big data and will need data scientists like you to help them make informed decisions.
Interested in Data Science?
Rhode Island College is an exclusive member of the Common Application.
Upon completion of this program, students will:
- have acquired a background in the content and methodology of mathematics, computer science and statistics
- understand and use concepts and techniques in calculus, linear algebra, probability, and discrete mathematics
- understand and use algorithmic thinking and programming in a high-level language
- understand and use concepts and techniques in data collection, analysis, modeling and statistical inference
- apply their knowledge in mathematics, statistics and computer science to solve problems
- choose, fit and use mathematical models to solve problems
- use high-level language to explore, visualize and form hypotheses about data
- understand the connections between the knowledge domains of mathematics, computer science and statistics and use a variety of skills from these domains to solve problems
- conduct data-based investigations and effectively communicate their findings
- receive raw data from a variety of sources and formats and then clean, transform and structure the data for analysis
- communicate data-based findings visually, orally and in writing
- gain exposure to the ethical questions related to data science such as citation and data ownership and the security of data
Writing in the Discipline
Why or in what ways is writing important to your discipline/field/profession?
In any career involving science and mathematics – including business, research, teaching, and other pursuits – written communication regarding process and results is important. People in careers in quantitative fields need to be able to explain results - including explanations for non-technical audiences, and they need to be able to detail the steps of developing a mathematical model and analyzing data.
Which courses are designated as satisfying the WID requirement by your department? Why these courses?
Within the Data Science major, the course M460: Seminar in Data Science is designated as satisfying the WID requirement. As part of this course, students will choose a scientific problem, and the instructor will act as a facilitator to help guide the student through each of their solution processes, with the student documenting the work throughout.
What forms or genres of writing will students learn and practice in your department’s WID courses? Why these genres?
The writing will fall into the category of technical writing, for the documentation and formal presentation for the analysis done on the student’s problem, and should also have a component of explanatory mathematical writing (see Russek (1998), Flesher (2003)), as the student prepares a less-technical presentation of the work for the class and, potentially, a wider audience.
What kinds of teaching practices will students encounter in your department’s WID courses?
The details of styles and intermediate assignments will vary from instructor to instructor. There may be a variety of relatively short, low-stakes assignments. Regarding the main project, it is expected that there will be feedback at a variety of times during the time span of the project: when a topic is selected, as resources and methods are chosen, as an outline is created, as a first and revised drafts are created. There is an expectation of an ongoing feedback loop for each student.
When they’ve satisfied your department’s WID requirement, what should students know and be able to do with writing?
A student who has completed the Data Science major should be able to:
- Write clear process-oriented work that details the reasoning and steps in solving a problem in mathematical modeling and data analysis. This includes the ability to justify each step in a solution.
- Write clear explanatory work to describe the mathematical and statistical concepts at hand. This includes the ability to describe mathematical concepts to an audience new to the topic at hand.
- Write clear explanatory work to describe the conclusions and implications that result from a data science investigation. This includes the ability to describe the results to an audience who is not necessarily expert in the topic at hand.
Minor in Data Science
Declaring a minor allows you to explore other areas of interest and make interdisciplinary connections. Minor areas at RIC complement and reinforce all major areas of study. By declaring a minor, you can set yourself apart as a candidate for job, internship and volunteer opportunities.