American International University - Bangladesh

What you'll study

The syllabus is organised in semesters with progressive emphasis on programming, algorithms, statistics and applied data topics. Early semesters cover introductory computing and calculus; middle semesters introduce data-science fundamentals and computational statistics; later semesters focus on machine learning, big data, data warehousing and applied topics. Example courses (by semester):

• Semester 1: Introduction to Computer Studies; Introduction to Programming and Lab; Differential Calculus and Coordinate Geometry; Physics I; English Reading Skills & Public Speaking; Bangladesh Studies.
• Semester 2: Object Oriented Programming 1 (Java); Discrete Mathematics; Principles of Accounting; Integral Calculus & Ordinary Differential Equations; Data Structure and Lab; English Writing Skills & Communications.
• Semester 3: Introduction to Data Science; Introduction to Database; Algorithms; Complex Variable, Laplace & Z‑transformation; Object Oriented Programming 2; Object Oriented Analysis and Design.
• Semester 4: Web Technologies; Operating Systems; Software Engineering; Computational Statistics and Probability; Matrices, Vectors, Fourier Analysis; Data Processing.
• Semester 5: Computer Organisation & Architecture; Artificial Intelligence and Expert System; Numerical Methods; Business Communication; Data Privacy, Security and Protection; Statistical and Data Analysis; Data Visualization.
• Semester 6: Data Warehousing and Data Mining; Programming for Data Science; Introduction to Network Engineering and Lab; Ethics in Data Science & Engineering; plus two Data Science major courses.
• Semester 7: Research Methodology; Programming in Python; Big Data Architecture; Machine Learning & Expert Systems; additional Data Science major courses.
• Semester 8: Internship; Project & Thesis (requires Research Methodology); plus two Data Science electives.

The programme also lists a range of general electives and major specialisations students may select from, including Advanced Database Management, Natural Language Processing, Deep Learning, Social Computing and Data Analytics for Business, and other computing, software engineering, network engineering, cybersecurity, robotics, business intelligence and bioinformatics options.

Entry requirements

The source page provides the full curriculum but does not specify admission criteria. Prospective applicants should consult the university’s admissions office or website for current entry standards and required qualifications.

Career prospects

Graduates exit with a mix of programming, statistical and systems knowledge suitable for roles in data analytics, data engineering, machine learning application, database administration, business intelligence and related IT functions. The curriculum’s internship and project components are intended to facilitate transition into industry or further study in specialised computing or data fields.

Scholarships & funding angle

No specific scholarships are listed on the curriculum page. Applicants should explore the university’s central scholarships, financial aid, merit-based awards and external funding opportunities; internships and industry projects embedded in the programme can also support professional advancement and employability.