Degree: Bachelor of Science (BS)
Major: Data Science and Analytics
Program Overview
The Data Science and Analytics BS program provides students with a broad foundation in the field and with the instruction, skills, and experience needed to understand and handle large amounts of data to derive actionable information. The degree program has a unique focus on real-world data and real-world applications. This program provides students with a strong background in the fundamentals of mathematics and science. Students can use their technical and open electives to pursue interests in software engineering, algorithms, artificial intelligence, machine learning, databases, data mining, bioinformatics, security, and computer systems. In addition to an excellent technical education, all students in the Case School of Engineering are exposed to societal issues, ethics, professionalism, and have the opportunity to develop leadership skills.
This major is one of the first undergraduate programs nationwide with a curriculum that includes mathematical modeling, computation, data analytics, visual analytics and project-based applications – all elements of the future emerging field of data science.
The Bachelor of Science degree program in Data Science and Analytics is accredited by the Computing Accreditation Commission of ABET, under the commission’s General Criteria and Program Criteria for Data Science.
Program Educational Objectives
Graduates from the Data Science and Analytics Bachelor of Science program will be prepared to:
- Analyze real-world problems and create data-driven solutions based on the fundamentals of data science and computing.
- Work effectively, professionally, collaboratively, and ethically.
- Assume positions of leadership in industry, academia, public service, and entrepreneurship.
- Successfully progress in advanced degree programs in data science, computing, and related fields.
Learning Outcomes
- Students analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Students design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Students communicate effectively in a variety of professional contexts.
- Students recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Students function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Students apply theory, techniques, and tools throughout the data analysis life cycle and employ the resulting knowledge to satisfy stakeholders’ needs.
Co-op and Internship Programs
Opportunities are available for students to alternate studies with work in industry or government as a co-op student, which involves paid full-time employment over seven months (one semester and one summer). Students may work in one or two co-ops, beginning in the third year of study. Co-ops provide students the opportunity to gain valuable hands-on experience in their field by completing a significant engineering project while receiving professional mentoring. During a co-op placement, students do not pay tuition but maintain their full-time student status while earning a salary. Alternatively or additionally, students may obtain employment as summer interns.
Undergraduate Policies
For undergraduate policies and procedures, please review the Undergraduate Academics section of the General Bulletin.
Accelerated Master's Programs
Undergraduate students may participate in accelerated programs toward graduate or professional degrees. For more information and details of the policies and procedures related to accelerated studies, please visit the Undergraduate Academics section of the General Bulletin.
Program Requirements
Students seeking to complete this major and degree program must meet the general requirements for bachelor's degrees and the Unified General Education Requirements. Students completing this program as a secondary major while completing another undergraduate degree program do not need to satisfy the school-specific requirements associated with this major.
Required Mathematics, Science and Engineering Courses:
Course List Code | Title | Credit Hours |
CHEM 111 | Principles of Chemistry for Engineers a | 4 |
CSDS 132 | Programming in Java | 3 |
ENGR 399 | Impact of Engineering on Society | 3 |
MATH 121 | Calculus for Science and Engineering I | 4 |
MATH 122 | Calculus for Science and Engineering II | 4 |
or MATH 124 | Calculus II |
MATH 223 | Calculus for Science and Engineering III | 3 |
or MATH 227 | Calculus III |
MATH 224 | Elementary Differential Equations | 3 |
or MATH 228 | Differential Equations |
PHYS 121 | General Physics I - Mechanics | 4 |
or PHYS 123 | Physics and Frontiers I - Mechanics |
PHYS 122 | General Physics II - Electricity and Magnetism | 4 |
or PHYS 124 | Physics and Frontiers II - Electricity and Magnetism |
Total Credit Hours | 32 |
Core Requirement
Course List Code | Title | Credit Hours |
CSDS 133 | Introduction to Data Science and Engineering for Majors | 3 |
CSDS 233 | Introduction to Data Structures | 4 |
CSDS 234 | Structured and Unstructured Data | 3 |
CSDS 302 | Discrete Mathematics | 3 |
CSDS 310 | Algorithms | 3 |
CSDS 312 | Introduction to Data Science Systems | 3 |
CSDS 313 | Introduction to Data Analysis | 3 |
CSDS 341 | Introduction to Database Systems | 3 |
CSDS 344 | Computer Security | 3 |
or CSDS 356 | Data Privacy |
CSDS 398 | Senior Project in Data Science | 4 |
MATH 380 | Introduction to Probability | 3 |
| Statistical Theory with Application I | |
| Basic Statistics for Engineering and Science | |
| Statistical Theory with Application II | |
| Data Analysis and Linear Models | |
Total Credit Hours | 41 |
Core courses provide our students with a strong background in foundations and analytics.
Foundations
Each student must supplement their competence in foundational technical areas by taking at least three additional courses, totaling at least 9 credit hours from the following list. Other courses, beyond those that are listed, may be approved by the student’s academic advisor. The following list is organized in topical areas for informational purposes only; foundation courses may come from the same or from different areas.
Foundation Courses:
Course List Code | Title | Credit Hours |
CSDS 293 | Software Craftsmanship | 4 |
CSDS 338 | Intro to Operating Systems and Concurrent Programming | 4 |
CSDS 344 | Computer Security | 3 |
CSDS 356 | Data Privacy | 3 |
CSDS 393 | Software Engineering | 3 |
STAT 243 | Statistical Theory with Application I | 3 |
STAT 244 | Statistical Theory with Application II | 3 |
| 3-4 |
CSDS 340 | Introduction to Machine Learning | 3 |
CSDS 390 | Advanced Game Development Project | 3 |
CSDS 391 | Introduction to Artificial Intelligence | 3 |
CSDS 442 | Causal Learning from Data | 3 |
CSDS 491 | Artificial Intelligence: Probabilistic Graphical Models | 3 |
CSDS 305 | Files, Indexes and Access Structures for Big Data | 3 |
CSDS 335 | Data Mining for Big Data | 3 |
or CSDS 435 | Data Mining |
CSDS 477 | Advanced Algorithms | 3 |
MATH 201 | Introduction to Linear Algebra for Applications | 3 |
or MATH 307 | Linear Algebra |
MATH 327 | Convexity and Optimization | 3 |
ECSE 246 | Signals and Systems | 4 |
ECSE 313 | Signal Processing | 3 |
ECSE 346 | Engineering Optimization | 3 |
ECSE 416 | Convex Optimization for Engineering | 3 |
Applications
Data science graduates are expected to be knowledgeable in a wide range of areas of applications of the data science profession. The breadth requirement is satisfied by choosing at least two courses (totaling at least 6 credit hours) from the following list. Additional courses, beyond those that are listed, may be approved by the student’s academic advisor.
Course List Code | Title | Credit Hours |
BIOL 319 | Applied Probability and Stochastic Processes for Biology | 3 |
BIOL 311A | Survey of Bioinformatics: Technologies in Bioinformatics | 1 |
BIOL 311B | Survey of Bioinformatics: Data Integration in Bioinformatics | 1 |
BIOL 311C | Survey of Bioinformatics: Translational Bioinformatics | 1 |
DSCI 330 | Cognition and Computation | 3 |
DSCI 351 | Exploratory Data Science | 3 |
ECON 326 | Econometrics | 4 |
ECON 327 | Advanced Econometrics | 3 |
CSDS 458 | Introduction to Bioinformatics | 3 |
CSDS 459 | Bioinformatics for Systems Biology | 3 |
MKMR 310 | Marketing Analytics | 3 |
MPHP 301 | Introduction to Epidemiology | 3 |
MPHP 426 | An Introduction to GIS for Health and Social Sciences | 3 |
Technical Electives
Students are required to complete two more technical electives for at least 6 credit hours. The courses can be any CSDS course or a course from the foundations and applications lists. The combination of core, foundations, and application courses with technical and open electives makes it possible to achieve a minor in fields as different as Economics and Biology. Interested students should contact their advisors.
Sample Plan of Study
The following is a suggested program of study. Current students should always consult their advisors and their individual graduation requirement plans as tracked in SIS.
Plan of Study Grid First Year |
Fall |
CHEM 111 | Principles of Chemistry for Engineers | 4 |
CSDS 132 | Programming in Java | 3 |
MATH 121 | Calculus for Science and Engineering I | 4 |
a | 3 |
| Credit Hours | 14 |
Spring |
PHYS 121
| General Physics I - Mechanics
or Physics and Frontiers I - Mechanics | 4 |
MATH 122
| Calculus for Science and Engineering II
or Calculus II | 4 |
CSDS 133 | Introduction to Data Science and Engineering for Majors | 3 |
CSDS 233 | Introduction to Data Structures | 4 |
a | 3 |
| Credit Hours | 18 |
Second Year |
Fall |
CSDS 234 | Structured and Unstructured Data | 3 |
CSDS 302 | Discrete Mathematics | 3 |
MATH 223
| Calculus for Science and Engineering III
or Calculus III | 3 |
PHYS 122
| General Physics II - Electricity and Magnetism
or Physics and Frontiers II - Electricity and Magnetism | 4 |
a | 3 |
| Credit Hours | 16 |
Spring |
CSDS 310 | Algorithms | 3 |
CSDS 341 | Introduction to Database Systems | 3 |
MATH 224
| Elementary Differential Equations
or Differential Equations | 3 |
a | 3 |
b | 3 |
| Credit Hours | 15 |
Third Year |
Fall |
CSDS 313 | Introduction to Data Analysis | 3 |
CSDS 344 | Computer Security () c | 3 |
a | 3 |
b | 3 |
| 3 |
| Credit Hours | 15 |
Spring |
CSDS 312 | Introduction to Data Science Systems | 3 |
CSDS 356 | Data Privacy () c | 3 |
ENGR 399 | Impact of Engineering on Society | 3 |
a | 3 |
b | 3 |
| 3 |
| Credit Hours | 18 |
Fourth Year |
Fall |
a | 3 |
c | 3 |
c | 3 |
d | 3 |
| 3 |
| Credit Hours | 15 |
Spring |
CSDS 398 | Senior Project in Data Science | 4 |
a | 3 |
d | 3 |
| 3 |
| 3 |
| Credit Hours | 16 |
| Total Credit Hours | 127 |