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

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:           34C
• B. Core Courses:                                                            78C
• C. Elective Courses:                                                    18C

GED Courses (34C): 

• 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 (4C)
• The following course must be taken:
  CS191- Introduction to Programming (4)
• 
  
• 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 (78C):

• 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 (15C)
• CS410– Deep Learning (3)
• 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
  
  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

Legends:

BES001:                               Any ONE course in Part (ii) of the General Education (GED) courses.

QSE001–QSE002:               Any TWO course in Part (iv) of the GED courses.

OPT001–OPT004:              Any FOUR optional GED courses approved by the University.

ELV 001–ELV003:              Any THREE elective courses from GROUP A of the Elective Modules.

ELV004 –ELV006:              Any THREE elective courses from GROUP B of the Elective Modules

N.B. The course DSA499 (Research Project) is spread over three semesters [1 credit (DSA499A) + 2 credits (DSA499B) + 3 credits (DSA499C)] of the 4^th year. Although the registration for this course will be done in SEM 1 of 4^th year, the grade for this course will be finally given at the end of SEM 3 of 4^th year

• Outcome Based Education (OBE), BSc in DSA

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

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

Mapping of Core Courses with Program Learning Outcomes (PLOs) 

Y – year, S – semester  

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