Undergraduate Programs

Title of the Academic Program

Bachelor of Science (B.Sc.) in Data Science and Analytics

Overview

The undergraduate program in Data Science and Analytics is a four-year B.Sc. program with 130 credits. It is designed to provide students with a solid foundation in the principles and practices of handling and analyzing large datasets. Through a multidisciplinary approach that integrates concepts from computer science, mathematics, and statistics, students gain a deep understanding of data manipulation, visualization, and interpretation. The curriculum emphasizes hands-on experience with various programming languages and statistical tools, allowing students to develop practical skills in data mining, machine learning, artificial intelligence, and predictive modeling. Furthermore, students are encouraged to apply their knowledge to real-world problems through internships, capstone projects, and research opportunities. By the end of the program, graduates will be equipped with the expertise and versatility to pursue careers in diverse fields such as business, healthcare, finance, and technology, where their analytical skills are in high demand.

Special Features of the Program

1. Experienced Faculty members
2. Modern Up-to-date Curriculum
3. State of the art Computer Laboratory facility
4. A modern Library with up-to-date Books and Journals
5. Academic collaboration with other prestigious Universities
6. Undergraduate Teaching Assistantships opportunities
7. Scholarship/ Financial assistance
8. Bright employment opportunities

Course Fee

Name of Programs	Total Credit	Fee per Credit	Tuition Fees	Lab & Activities Fees	Admission Fee	Grand Total
B.Sc. in Data Science and Analytics	130.0	5,000/=	683,000/=	48,800/=	20,000/=	751,800/=

Eligibility Criteria

1. Minimum GPA of 3.00 in both SSC and HSC Examinations with Mathematics in HSC level, OR
2. Candidates must have passed University of London and Cambridge GCE ‘O’ Level in at least five subjects and ‘A’ Level in at least two subjects including Mathematics. Only the best five subjects in ‘O’ Level and best two subjects in ‘A’ Level including Mathematics will be considered. Out of these seven subjects, a candidate must have at least 4B’s or GPA of 4.00 in the four subjects and 3 C’s or GPA of 3.5 in the remaining three subjects. (in the scale of A=5, B=4, C=3, D=2 and E=1, OR
3. American High School Diploma, AND
4. Acceptable EWU Admission Test Score
5. The final selection of candidates for admission in the Undergraduate Programs at EWU will be based on the Admission Test scores obtained with 75% from admission test, 10% from SSC/O-level and 15% from HSC/A-level.

Program Structure

Total Credit hours of the undergraduate BSc program in Data Science & Analytics is 130. The distribution of the courses is given below:

• A. General Education (GED) Courses:           33C
• B. Core Courses:                                                            79C
• C. Elective Courses:                                                    18C

GED Courses (33C): 

• a. English Communication Skills (6C)
• The following 2 courses must be chosen:
• ENG101– Basic English (3)
  • ENG102– Composition & Communication Skills (3)
  • 
    
  • b. Business/Entrepreneurship/Social Sciences & Liberal Arts (3C)
• Any 1 course must be chosen from the following list of courses:
• BUS101– Introduction to Business (3)
• EDC101– Basic Entrepreneurship (3)
  • GEN206– Introduction to Sociology (3)
• 
  
• c. Computer Skill/Programming Skill (3C)
• The following course must be taken:
  • CSE101– Introduction to Computers I (3)
• 
  
• d. Quantitative/Science/Environment Knowledge (6C)
• Any 2 courses from the following list of courses must be chosen:
• PHY100– Introductory Physics (3)
• GEB101– Basic Biology (3)
• GEN203– Ecological System & Environment (3)
  • SOC212– Social Ecology, Environment & Society (3)
• 
  
• e. Culture & History Knowledge (3C)
• The following course must be taken:
  • GEN226-Emergence of Bangladesh (3)
• 
  
• f. Open GED Courses (12C)
• Any 4 courses must be chosen from the following list of courses:
• PPHS102–Introduction to Public Health Sciences (3)
• GEN205– Introduction to Psychology (3)
• GEN206– Introduction to Sociology (3)
• GEN207– Industrial Psychology (3)
• GEN208– Introduction to Philosophy (3)
• GEN210– International Relations (3)
• GEN211– Concepts of Journalism & Media Studies (3)
• GEN239– Professional Ethics (3)
• ACT101– Financial Accounting (3)
• ECO101– Principles of Microeconomics (3)
• ECO102– Introduction to Macroeconomics (3)
• FIN101– Principles of Finance (3)
• MGT101– Principles of Management (3)
• MKT101– Principles of Marketing (3)
• Any other GED course approved by the University                    
• 
  

Core Courses (79C):

• a. Mathematics & Statistics Courses (25C)
• MAT101– Differential & Integral Calculus (3)
• MAT102– Differential Equations & Special Functions (3)
• MAT291– Linear Algebra (3)
• MAT295– Discrete Mathematics (3)
• MAT397– Numerical Methods & Optimization (4)
• STA191– Probability & Statistics (3)
• STA293– Probability Distributions (3)
  • STA395– Statistical Inference (3)
• 
  
• b. Computing Courses (16C)
• CS191– Programming with C (4)
• CS295– Programming with Python (4)
  • CS397– Data Structure & Algorithm (4)
  • CS399– Artificial Intelligence (4)
• 
  
• c. Data Science Courses (38C)
• DSA101– Introduction to Data Science (4)
• DSA201– Data Processing & Storage (4)
• DSA303– Regression Analysis (3)
• DSA305– Multivariate Analysis (3)
• DSA 307– Generalized Linear Model (3)
• DSA401– Data Mining (4)
• DSA403– Machine Learning (4)
• DSA405– Big Data & Cloud Computing (4)
• DSA407– Data Security & Privacy (3)
  • DSA 499 – Research Project (6)

• 
  

• Elective Courses (18C):

  A student must choose total 6 courses taking 3 courses from each of Group A and Group B:

  GROUP A

  STA430– Stochastic Processes (3)
  STA432– Bayesian Statistics (3)
  STA434– Time Series Analysis (3)
  STA436– Experimental Design (3)
  STA438– Biostatistics (3)
  STA440– Information Systems (3)
  ECO465– Basic Econometrics (3)

  GROUP B

  CS410– Deep Learning (3)
  CS412– Data Visualization (3)
  CS414– Cryptography & Blockchain (3)
  CS416– Bioinformatics (3)
  CS418– Machine Learning for Health Sciences (3)
  CS420– Machine Learning for Cyber Security (3)
  BUS420– Business Intelligence (3)
  FIN7430– Foundation of Financial Technology (3)
  FIN7431– System Analysis & Design (3)

Detailed Course Descriptions

Semester-Wise Flow-Chart to be followed during the 4 years of the undergraduate BSc program in Data Science & Analytics

Year	Semester	Course	Prerequisite
Year 1	Semester 1	ENG101(3)	None
		EDC101(3)	None
		MAT101(3)	None
		STA191(3)	None
	Short Summer	GEN226(3)	ENG101
		CSE101(3)	None
	Semester 2	ENG102(3)	ENG101
		DSA101(3)	STA191
		GEN203(3)	ENG101
		PHY100(3)	MAT101
	31 Credits

Year 2	Semester 1	MAT102(3)	MAT101
		STA293(3)	STA191
		CS191(4)	MAT10
		OPT001(3)	ENG101
	Short Summer	MAT291(3)	MAT102
		OPT002(3)	ENG101
	Semester 2	MAT295(3)	STA191
		DSA201(4)	CS191
		OPT003(3)	ENG101
		CS295(4)	CS191
	33 Credits

Year 3	Semester 1	MAT397(4)	MAT291
		STA395(3)	STA293
		OPT004(3)	ENG101
		CS397(4)	CS295
	Short Summer	DSA303(3)	STA395
		DSA305(3)	STA395
	Semester 2	CS399(4)	CS295
		DSA307(3)	STA395
		DSA401(4)	CS295
		DSA403(4)	CS397
	35 Credits

Year 4	Semester 1	DSA405(4)	DSA403
		DSA407(3)	DSA401
		DSA499A(3)	DSA403
		ELV001(3)	DSA403
	Short Summer	ELV002(3)	DSA405
		ELV003(3)	DSA405
	Semester 2	DSA499B(3)	DSA405
		ELV004(3)	DSA405
		ELV005(3)	DSA405
		ELV006(3)	DSA405
	31 Credits

Legends:
OPT001–OPT004: Any FOUR optional General Education Courses approved by the University.
ELV001 – ELV003: Any THREE courses from GROUP A of the elective modules.
ELV004 – ELV006:  Any THREE courses from GROUP B of the elective modules.

The course DSA499 (Research Project) is spread over two semesters. Although the registration for this course will be done in SEM 1 of 4^th year, the grades for this course will be finally assigned at the end of SEM 2 of 4^th year. Grade X (continuation) will be assigned at the end of SEM 1 of 4^th year.

• Outcome Based Education (OBE), BSc in DSA

Title of the Academic Program

Bachelor of Science in Data Science & Analytics (B.Sc. in Data Science & Analytics)

Name of the University

East West University

Vision of the University

East West University was established with interrelated objectives of augmenting national capacity for tertiary education to a larger number of students, and thereby contributing to the country’s human capital development process.

EWU is committed to equipping its graduates with professional skills and innovative and critical thinking capable of taking advantage of emerging opportunities at home and abroad. By providing access to affordable university-level education to students from less-well-to-do sections of the society, EWU seeks to harness the power of individuals through higher education, by raising employable skills and improving access to job opportunities, thereby contributing to a more balanced and just society. The vision that inspired EWU is to be a topnotch learning center that combines excellence in education with strong human values and social responsibility, aiming to improve the quality of life, principally of its students. In the process, EWU strives to broaden human welfare by promoting a knowledge-based society.

Mission of the University

1. To become a center of excellence for higher education, making quality postsecondary education accessible to a larger section of the community at an affordable cost, leading to wide dissemination of educational opportunities, advancement of knowledge creation and promotion of economic, social, and technological progress.
2. To excel in critical disciplines that addresses socio-economic challenges nationally and globally, and advance creative pursuits necessary for innovative solutions to societal issues.
3. To provide a stimulating learning environment in which the faculty and students can discover, critically examine, preserve, and transmit knowledge, values, and wisdom to benefit society and improve the quality of life for all.
4. To ensure that its students receive the highest quality of education, preparing them for a purposeful life, personally, professionally, and socially.

Name of the Program Offering Entity

Department of Mathematical and Physical Sciences (MPS)

Vision Statement of the Department of Mathematical and Physical Sciences

The Department of Mathematical and Physical Sciences is a department of the Faculty of Sciences and Engineering. Most important and essential subjects of any field of sciences are Mathematics, Statistics, Physics and Chemistry; and this department offers all these courses for the students of sciences and engineering.

The vision of the department is to produce highly competent Mathematician, Statistician, Physicist and Chemist who will address both national and global challenges for the sustainable development of the society by applying professional skills in any development work or excellent teaching performance or extraordinary research work in their respective areas.

Mission Statement (M) of the Department of Mathematical and Physical Sciences 

M1	To utilize advance knowledge in Mathematics and Mathematical Sciences through quality education and research towards the development of the society.
M2	To motivate research in mathematical and physical sciences by facilitating and promoting the scholarly activities.
M3	To prepare students by giving adequate knowledge and developing attitude so that graduates are in high demand in various sectors both nationally and internationally.

Objectives of the Department of Mathematical and Physical Sciences

1. To ensure quality in teaching-learning environment by practicing Outcome-Based Education (OBE).
2. To create an effective research environment.
3. To continually maintain qualified faculty members, standard laboratory facilities and other supportive objects/materials.
4. To update the program curriculum periodically to meet the requirements of national and international academic and non-academic employers.

Name of the Degree

Bachelor of Science in Data Science & Analytics (B.Sc. in Data Science & Analytics)

Description of the Program

The B.Sc. in Data Science & Analytics is a degree program under the department of MPS. This department started its activities in 2017 to teach/offer courses in Mathematics, Statistics, Physics and Chemistry. This program is designed carefully to meet the growing demand for applied statisticians and data science in Bangladesh and worldwide. The program will prepare graduates for the statistics, data scientist or analyst profession, which involves the application of mathematical, statistical, and computing techniques to the scientific fields in government and private sectors. After successful completion of the courses in data science and analytics each student will receive a B.Sc. certificate and a grade report.

Program Educational Objectives (PEOs) of B. Sc. in Data Science & Analytics

Graduates of the B.Sc. in Data Science & Analytics are expected to attain the following Program Educational Objectives (PEO) within a few years of graduation.

PEO-1: Establish themselves as leading data scientists, data analyst, and researcher in both academic and non-academic sectors through rigorous understanding, critical thinking, and practical experience.

PEO-2: Carryout research and experiments in dynamic fields and help the industries and other sectors to fulfill their demands in data management and the ability to think critically and solve complex problems using data-driven approaches.

PEO-3: Pursue further higher education in this field and contribute in addressing both local and global causes and challenges using data-driven approaches.

Program Learning Outcome (PLO)

Graduates in Data Science & Analytics will be able to:

PLO1-Observational Knowledge: Evaluate the details in a real-life data-based problem and its different aspects to choose the appropriate approach to deal or solve the problem.

PLO2-Data Management and Visualization: Collect and manage data, whether small or big, and use data visualization techniques to get a better insight to the data and communicate the data features effectively to prepare them for analyses.

PLO3-Valid Inference: Make valid and efficient inference from both quantitative and qualitative data using different statistical tools.

PLO4-Modelling: Apply quantitative modeling techniques to model real-life events, phenomenon, and systems, and justify their effectiveness and use them for forecast and predictions.

PLO5: Design/Development of solutions: Design solutions for big data and design system components or processes that meet the specified needs in various fields.

PLO6-Computing with Modern Tools: Use modern computing tools and facilities, and improvise one where appropriate, to manage and analyze data effectively.

PLO7-Research: Apply data analysis tools in dynamic experimental studies and research fields, e.g., IT, health and medical sciences, business and economics, agriculture and environment, and administration etc to create new knowledge and solve world issues.

PLO8-Ethics: Apply the ethical practices and commit to professional ethics, responsibilities, and norms for data management decisions.

 PLO9-Life-long learning: Recognize excellence in work and, engage in independent and life-long learning in the broadcast context of technological change.

Mapping of Program Educational Objectives (PEOs) with Mission of the University

PEOs	Mission 1	Mission 2	Mission 3	Mission 4
PEO1	√
PEO2		√
PEO3			√	√

Mapping of Program Learning Outcomes (PLOs) with Program Educational Objectives (PEOs)

PLOs	PEO1	PEO2	PEO3
PLO1	√
PLO2		√
PLO3			√
PLO4			√
PLO5		√
PLO6		√
PLO7			√
PLO8	√
PLO9	√

Mapping of Core Courses with Program Learning Outcomes (PLOs) 

Y – year, S – semester  

CLOs	PLO1	PLO2	PLO3	PLO4	PLO5	PLO6	PLO7	PLO8	PLO9
CLO1	√
CLO2			√
CLO3					√
CLO4			√

Mapping of the course learning outcomes (CLOs) with the teaching-learning & assessment strategy 

CLOs	CLO description	Domain/Level	Assessment tool
CLO1	Demonstrate fundamental understanding of the AI and its foundations.	Understand	Term and final examination
CLO2	Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.	Apply	Assignments, Term and final examination
CLO3	Demonstrate awareness and a fundamental understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.	Apply	Assignments, Term and final examination
CLO4	Demonstrate proficiency in applying scientific method to models of machine learning.	Analyze	Assignments, Term and final examination

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