Bachelor of Data Science

High school diploma or equivalent, acceptable grade average, English proficiency test, aptitude test according to specialization

Core Curriculum Requirements (34 credits)
The remaining 6 credit hours required to satisfy the core are fulfilled by the concentration Science (3 crs) and the DSCI 000/4416 – Capstone l (Data Science Senior Project l) (1 cr.) and DSCI 000/4417 – Capstone II (Data Science Project II) (2 cr.). Data Science students must take 1 credit hour of Natural Science lab.

Concentration Requirements (33 cr.)
DSCI 000/1411 – Fundamentals of Data Science I (3 cr.)
DSCI 000/1412 – Fundamentals of Data Science II (3 cr.)
DSCI 000/2411 – Data Visualization (3 cr.)
DSCI 000/3411 – Fundamentals of Simulation (3 cr.)
DSCI 000/3415 – Fundamentals of Machine Learning (3 cr.)
DSCI 000/4411 – Fundamentals of Data Mining (3 cr.)
DSCI 000/4412 – Introduction to Big Data Technologies (3 cr.)
DSCI 000/4413 – Analysis of Categorical Data (3 cr.)
DSCI 000/4415 – Advanced Machine Learning (3 cr.)
DSCI 000/4416 – Capstone l (Data Science Senior Project l) (1 cr.)
DSCI 000/4417 – Capstone II (Data Science Project II) (2 cr.)
DSCI 000/4950 – Industrial Training (3 cr.)
Collateral Requirements (39 credits)
MACT 131/1121 – Calculus I (3cr.)
MACT 132/1122 – Calculus II (3 cr.)
MACT 231/2123 – Calculus III (3 cr.)
MACT 240/2132 – Linear Algebra (3 cr.)
MACT 233/2141 – Differential Equations (3 cr.)
MACT 000/2146 – Optimization I (3 cr.)
MACT 000/3146 – Optimization II (3 cr.)
MACT 306/3211 – Applied Probability (3 cr.)
MACT 307/3223 – Statistical Inference (3 cr.)
MACT 406/4212 – Stochastic Processes (3 cr.)
MACT 427/4231 – Applied Regression Methods (3 cr.)
MACT 428/4232 – Analysis of Time Series Data (3 cr.)
MACT 429/4233 – Applied Multivariate Analysis (3 cr.)
Data Science Electives (24 credits)
The elective credits are courses chosen from the following list or by the permission of the Department Chair or the Director of the Data Science Program. Students who are planning on taking 2000 or higher level CSCE courses should take the prerequisite courses CSCE 1001 and CSCE 1101. It is advisable to plan to finish these two courses by the third semester of your program.

CSCE 106/1001 – Fundamentals of Computing I (3 cr.)
CSCE 110/1101 – Fundamentals of Computing II (3 cr.)
CSCE 321/2202 – Analysis and Design of Algorithms (3 cr.)
CSCE 253/2501 – Fundamentals of Database Systems (3 cr.)
CSCE 465/3601 – Fundamentals of Artificial Intelligence (3 cr.)
CSCE 453/4501 – Database Systems (3 cr.)
CSCE 000/4603 – Fundamentals of Computer Vision (3 cr.)
CSCE 485/4930 – Selected Topics in Computer Science and Engineering (1-3 cr.)
DSCI 000/3413 – Biostatistics (3 cr.)
MACT 304/3143 – Numerical Methods (3 cr.)
MACT 412/4213 – Mathematical Modeling with Applications (3 cr.)
MACT 495/4980 – Senior Thesis (3 cr.)
MOIS 305/2101 – Introduction to Information Systems/Technology (3 cr.)
MOIS 406/3201 – Management Information Systems and Database Management (3 cr.)
MOIS 432/3601 – Decision Support Systems and Business Intelligence (3 cr.)