Data Analytics

SCHOOL OF COMPUTER SCIENCE AND INNOVATION

Ling Zhu, Chair
Ling.Zhu@liu.edu
516-299-1546

B.S. in Data Analytics

The Bachelor of Science in Data Analytics (BSDA) will prepare students for the growing demand in industries for data-literate professionals who can understand and perform data analytics and apply the knowledge in decision-making in various practical fields. In addition to the common core curriculum, the upper-division coursework innovatively consists of the following modules:

  • Foundational Module: programming in Python, Data Analytics with Excel, R, and Python, Data Structures and Algorithms
  • Core Module: Database Management, Data Visualization, Advanced Statistics, Data Mining and Business Intelligence, Machine Leaning, Intro to AI
  • Applied Module: Data Analytics Ethics, Intro to Fintech, Intro to Modern Cryptography, Computational Genomics, Deep Learning, and Capstone Project
  • Elective Module: elective courses can be taken in other programs such as Accounting, Artificial Intelligence, Business Administration, Computer Science, Digital Engineering, Entrepreneurship, Fashion Merchandising, Finance, Marketing, and Sports Management

The BSDA is a STEM designated degree program. The core faculty in the program all hold their Ph.D. degrees in Computer Science, Management Information Systems, Economics, or Operation Research from top-tier research universities, with extensive industry and research experience at Amazon, National Science Foundation, the National Academies of Science, Engineering, and Medicine, and Wall Street and Main Street firms.


Program Curriculum

Course # Course Name Credits

Required Data Analytics Courses
(48 Credits )
DA 103 Programming in Python
3
DA 118 Data Analytics using Excel
3
DA 120 Data Analytics with R and Python 3
DA 125  Multivariate and Advanced Studies 3
DA 130 Database Management 3
DA 131 Data Structures and Algorithms
DA 140 Data Visualization 3
DA 153 Data Analytics Ethics 3
DA 155 Intro to Fintech
DA 162 Intro to Artificial Intelligence 3
DA 163 Data Mining and Business Intelligence 3
DA 166 Computational Genomics 3
DA 250 Machine Learning 3

DA 260

Deep Learning

3

DA 265 Introduction to Modern Cryptography
DA 460  Senior Capstone Project
3
 Required Electives
(12 Credits)
 Choose from any AI or DA courses (not already required above. With program director's written approval, students can choose up to 9 credits of electives from any of the following subject areas ACC, BUS, CS, ECO, ENT, FIN, FM, LAW, MAN, MIS, MKT, QAS or SPM.
 Liberal Arts and Sciences Electives
(28 Credits) 
 Required Courses (which can be included in core or electives)
MTH 22 Applied Linear Algebra 3
MTH 23 Foundations of Statistical Analysis 3
     
 Course #
 Course Name
 Credits
Required Core Courses
(32 Credits)
POST 101 Post Foundations 1
FY First-Year Seminar 3
ENG 1** Writing 1 3
ENG 2** Writing 2 3
MTH 5 Quantitative Reasoning
Choose one course from each of the five below course clusters and one additional course from one of the clusters.
Scientific Inquiry & the Natural World
4
Creativity Media & the Arts 3
Perspectives on World Culture 3
Self, Society & Ethics 3
Power, Institutions & Structures (ECO 10 Required) 3
One additional course from one of the five above clusters. (ECO 11 Required) 3

* Some courses may count as core and others as electives.

** In addition to ENG 1 and 2, students take at least 3 more writing intensive (WAC) courses as part of their major, core, or elective courses.  ENG 303 and 304 can satisfy the ENG 1 and 2 requirement for students in the Honors College.

Credit Requirements
Total Major Requirement Credits 48
Elective Major Credits 12
Total Core Requirement Credits 32
Elective Liberal Arts & Sciences Credits 28
Total Degree Credits 120