Bachelor of Science in Data Science
Buffalo, USA
BSc
DURATION
4 years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
TUITION FEES
USD 37,945 / per year
STUDY FORMAT
On-Campus
Key Summary
Big Data is transforming every aspect of our world, and understanding how it works will continue to be a key area of expertise for every sector and industry, including business, healthcare, education, and government. To study data science is to understand what it means to be a 21st-century problem-solver who can turn information into insights—common career paths: business analyst, statistician, researcher, deep learning engineer, data scientist.
Understanding and Shaping the World of Data
As a data science student at Canisius, you’ll develop a core set of technical skills in mathematics, computer science, and business intelligence. Through experiential learning, you’ll use the latest technology to explore and analyze data sets and anticipate how they might be applied to various problem-solving situations.
If your goal is to learn how to translate complex data into actionable insights, you’ll be well-prepared for an analyst role with a business or organization by the time you graduate. If your passion is building data models and algorithms, you’ll be well-positioned to continue your studies at the graduate level.
Dual Majors and Minors
As part of the Bachelor of Data Science program, you must complete a minor or a second major, which can be in any field and will depend on your interests, strengths, and career goals. Complementary study areas include computer science, mathematics, psychology, economics, finance, marketing, and digital media arts.
This unique approach allows you to create a flexible path for both your academic and your career goals.
An Interdisciplinary Approach
A well-rounded perspective is consistent with the liberal arts core at Canisius. Along with specific knowledge about data science, our program will arm you with the skills needed to thrive in the professional world—including written and oral communication, teamwork, synthesizing information, problem-solving, and critical thinking. Guided by Jesuit values, you’ll also develop a holistic understanding of the ethical issues that drive the use of data in modern life.
Individualized Attention and Mentoring
Unlike programs at larger institutions, Canisius students enjoy close collaboration with faculty, even at the undergraduate level. As a student in the data science program, you’ll benefit from small classes and individualized attention. You’ll learn from professors who are both scholars and practitioners in the field. They’ll get to know you on a personal level and help you build on your unique strengths and interests.
Graduate School Connections: MS in Data Analytics
After completing the Bachelor of Science in Data Science degree, students can expand their knowledge and skills and continue their education in our Master of Science in Data Analytics program.
Major Requirements
Computer Science Courses
- CSC 111 & 111L Introduction to Programming and Introduction to Programming Laboratory
- CSC 112 & 112L Data Structures and Data Structures Laboratory
- CSC 310 & 310L Information Organization and Processing and Information Organization and Processing Laboratory
- CSC 330 & 330L Operating System Design and Distributed Computing and Operating System Design and Distributed Computing Laboratory
Data Science Courses
- DAT 111- Intro to Reporting and Analysis
- DAT 211-Intro to Statistics with R
- DAT 411-Econometrics (cross-list current BUS or DAT course, not a new course)
- DAT 412-Machine Learning
Mathematics Courses
- MAT 111 Calculus I
- MAT 112 Calculus II
- MAT 211 Calculus III
- MAT 219 Linear Algebra
- MAT 351 Probability & Statistics I
- MAT 341 Numerical Analysis
Data Science: Program Learning Goals
Student Learning Goal 1: Foundations
- Objective 1A: A Foundation in Computer Science. Students will be fluent in programs, and understand database structures and distributed computing concepts. They will be ready to learn new languages and adapt to rapid changes in software.
- Objective 1B: Adaptable Foundation applied statistics: Students will be able to use the basic principles of probability theory in a variety of contexts, including both classical statistical approaches and computational-based methods. Students will be familiar with one modern statistical software platform and will be able to readily adapt to others
Either:
- Objective 1C: Substantial knowledge of a domain area. Students who chose this path will have a substantial background in an applications area (or domain) to which they can apply data science tactics. {assessment will be based on the outside major or minor}
Or
- Objective 1D: Additional mathematical or computational studies-Students who chose this option will demonstrate substantial additional capabilities in Mathematics or Computer Science, preparing them for research and development efforts in Data Science. {Assessment via the Mathematics or Computer Science major or minor}
Student Learning Goal 2: Effective Teamwork.
- Objective 2A: Students will demonstrate the ability to work in multi-disciplinary teams to address real-world problems.
- Objective 2B: Students will understand the current theoretical ideas related to the formation of effective collaborative teams.
Student Learning Goal 3: Effective Business Communication.
- Objective 3A: Students will be able to identify the needs of different audiences, and effectively present complex information in ways that suit the needs of multiple audiences.
- Objective 3B: Students will be able to write effectively to convey data analytic results in business or other domain contexts.
- Objective 3C: Students will be able to create and deliver effective oral presentations, as well as present ideas in less formal oral settings.
- Objective 3D: Students will be able to create effective graphics, both static and real-time active displays, that convey results to business or other domain audiences.
Student Learning Goal 4: Ethical Data Stewardship
- Objective 4A: Students will have an awareness of the ethical and moral issues that arise in working with large data-sets, and understand the steps that need to be taken to protect the rights and privacy of the individuals involved.