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Home > Courses > NU Postgraduate Diploma > Post-Graduate Diploma in Cybersecurity
Post-Graduate Diploma in Cybersecurity
Secure the Digital World. Build Your Future in Cyber Security!
The Post-Graduate Diploma in Cyber Security equips students with essential knowledge of cyber threats, information security, network protection, digital forensics, cyber laws, and risk management. Through a comprehensive curriculum covering cybercrime investigation, data privacy, threat prevention, and practical project or internship experience, the programme prepares learners to understand cybersecurity challenges and contribute to protecting digital systems and information. Graduates can explore career opportunities across IT companies, financial institutions, government organizations, NGOs, and other sectors.
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Acquire a skill and become job-ready with professional certifications
- 40 Credit Hours
- 12 Months
- 10 Subjects
- Instructor-Led
- On-Campus
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- Master Cybersecurity, Risk Management, and Threat Prevention
- Explore Digital Forensics, Cybercrime Investigation, and Security
- Build Career-Ready Skills in Cybersecurity and Digital Protection
- Develop skills for real career growth
- Learn from experts active in their field
- Learn by working on real-world problems
- Structured guidance ensuring continuous learning
The Post-Graduate Diploma in Cyber Security is designed to help students understand how to protect computer systems, networks, and digital information from cyber threats and online attacks. The programme covers a wide range of topics, including cyber criminology, cryptography and data security, network and communication security, cyber security laws and policies, cybercrime investigation, and digital forensics. Students also explore digital privacy, cyber risk management, cyber terrorism, and financial cybercrime to understand the different challenges of keeping individuals and organizations safe in the digital world.
Throughout the programme, students learn to identify cyber threats, understand system vulnerabilities, protect sensitive information, assess security risks, and explore methods for preventing and investigating cybercrime. The curriculum also introduces cyber laws and security strategies in Bangladesh and other countries, helping students understand the legal and practical challenges of cybersecurity. The final project or internship allows students to apply their knowledge to real-world situations and gain practical experience in a relevant organization.
This programme is suitable for graduates and individuals who want to build their knowledge of cybersecurity and explore opportunities in this growing field. Upon completion, graduates can pursue career opportunities in IT companies, banks and financial institutions, telecom companies, government organizations, NGOs, and other sectors where protecting digital systems and information is essential.
Throughout the programme, students learn to identify cyber threats, understand system vulnerabilities, protect sensitive information, assess security risks, and explore methods for preventing and investigating cybercrime. The curriculum also introduces cyber laws and security strategies in Bangladesh and other countries, helping students understand the legal and practical challenges of cybersecurity. The final project or internship allows students to apply their knowledge to real-world situations and gain practical experience in a relevant organization.
This programme is suitable for graduates and individuals who want to build their knowledge of cybersecurity and explore opportunities in this growing field. Upon completion, graduates can pursue career opportunities in IT companies, banks and financial institutions, telecom companies, government organizations, NGOs, and other sectors where protecting digital systems and information is essential.
Course Objective
- Understand the fundamental concepts, classifications, causes, and impacts of cybercrime in contemporary society
- Develop foundational knowledge of cryptography, data security, information protection, and digital privacy
- Understand network and communication security principles, including common vulnerabilities, threats, and protective measures
- Examine cyber security laws, policies, regulations, and strategies relevant to Bangladesh and international contexts
- Understand the principles and procedures of cybercrime investigation, digital forensics, and digital evidence management
- Identify cyber security threats, assess vulnerabilities, and understand appropriate prevention, mitigation, and incident response measures
- Apply cyber security risk management concepts to identify, evaluate, document, and address organizational risks
- Analyze cyber economic crimes, financial cybercrime patterns, and approaches to crime prevention and management
- Understand threats associated with social media, online platforms, cyber terrorism, and the misuse of digital technologies
- Apply academic knowledge and analytical skills to cyber security-related problems through a capstone project or internship
What you'll learn
Through the PGD in Cyber Security programme, you will gain essential knowledge and practical skills to understand cyber threats, protect digital information, and manage security risks. The programme covers key areas of cybersecurity, helping you build a strong foundation for further learning and career development in this field.
By the end of this programme, you will be able to:
By the end of this programme, you will be able to:
- Explain major forms of cybercrime, cyber security threats, and their potential effects on individuals, organizations, and society
- Demonstrate foundational understanding of cryptography, data protection, network security, and digital privacy principles
- Identify common cyber threats, system vulnerabilities, and security risks, and explain appropriate preventive or remedial measures
- Explain the principles of cybercrime investigation, digital forensics, evidence preservation, and incident response
- Analyze cyber security risks, relevant legal and policy considerations, and strategies for protecting digital systems and information
- Apply critical thinking, analytical reasoning, and problem-solving skills to cyber security related case studies, projects, or internship assignments
Learning Path
Semester 01 - 20 Credits
Credits : 4
Course Overview and Context:
This course will provide essential knowledge on the basic understanding of the emerging data analytics field. The student will learn the data exploration methods, dimensional analysis, and some extent of data visualization. The theoretical knowledge and practical lessons learned from this course will equip the students to absorb their skill sin their respective industries significantly.
Course Outcomes:
Course Overview and Context:
This course will provide essential knowledge on the basic understanding of the emerging data analytics field. The student will learn the data exploration methods, dimensional analysis, and some extent of data visualization. The theoretical knowledge and practical lessons learned from this course will equip the students to absorb their skill sin their respective industries significantly.
Course Outcomes:
- Introduction to Data Analytics: Data overview; State of the practice in Analytics; Key roles for the new Big data ecosystem
- Data Analytics Lifecycle: Overview of data analytic lifecycle; Various phases of data analytics life cycle; Global innovation network and analysis (GINA)
- Data Mining Process: Definition; Data extraction as a process; Data mining models- CRISP-DM, SEMMA; Steps in the data mining process; Data mining process in a data warehouse, Applications of data mining; Data mining challenges
- Review of Basic Data Analytics Methods: Descriptive analytics, Diagnostic analytics, Predictive analytics; Prescriptive analytics
- Business Intelligence Trends and Big Data Trends: What is big data and why it is important; 5V’s of big data; Goals of big data; Big data architecture, analytics & techniques; Future trends of big data
Credits : 4
Course Overview and Context:
This course introduces students to computing science and programming. It includes fundamental concepts and terminologies of computing science, program design, and fundamental building blocks for programming in a high-level language. At the end of this course, the successful students will be able to: explain the process of program design and development; design a GUI by using objects (forms and controls) and managing properties of controls; design a program to solve a well-defined problem; and implement a program from a design using structured programming.
Course Outcomes:
Course Overview and Context:
This course introduces students to computing science and programming. It includes fundamental concepts and terminologies of computing science, program design, and fundamental building blocks for programming in a high-level language. At the end of this course, the successful students will be able to: explain the process of program design and development; design a GUI by using objects (forms and controls) and managing properties of controls; design a program to solve a well-defined problem; and implement a program from a design using structured programming.
Course Outcomes:
- Programming in general: Program development cycle; Programming tools – flowcharts, pseudo code
- Programming in C#, NET: Forms, controls, properties, events; Numbers, variables, constants; Strings; Input and output using text boxes; Built-in functions – numeric functions, strings functions
- Programming in Python: Basics, Data types and Control structures, Modularization and Classes
- Methods: Value returning and void; Scope of variables; Value and reference parameters
- Decisions: Relational and logical operators; Switch statements
- Structure: Repetitions, Arrays, Classes, and Fundamental algorithms in C# and Python
- Files: Reading and writing text files in C# and Python
Credits : 4
Course Overview and Context:
This course will provide knowledge regarding the advanced tools and techniques that are used for various data types. The concentration will be given to the application of suggestive tools in sophisticated software packages rather than enormous theories crunching. Students will learn the possible solutions for the situations of the respective problem for which the data was generated.
Course Outcomes:
Course Overview and Context:
This course will provide knowledge regarding the advanced tools and techniques that are used for various data types. The concentration will be given to the application of suggestive tools in sophisticated software packages rather than enormous theories crunching. Students will learn the possible solutions for the situations of the respective problem for which the data was generated.
Course Outcomes:
- Overview of Data Analytic Tools
- Descriptive Measures of Data (Basic statistical measures): Types and measurement scales of data; Measures of central tendency; Measures of dispersion
- Inferential Decision from Data (basic estimation and test of hypothesis): Basics of inferential statistics; Power of test and p-value; Confidence intervals and tests of means and proportions
- Correlation and Regression: Simple and multiple correlations; Simple and Multivariate linear regression; Logistic Regression; Additional Regression Models
- Classification: Overview; Decision Trees; Naïve Bayes; Diagnostics of Classifiers
- Time Series Analysis: Decomposition of time series data; Plotting time series data; Box-Jenkins Methodology; ARMA models; ARIMA models
- Text analysis: Text Analysis Steps; Collecting Raw Text; Representing Text; Term Frequency-Inverse Document Frequency (TFIDF); Categorizing Documents by Topics; Determining Sentiments
Credits : 4
Course Overview and Context:
This course will provide the student with knowledge of database concepts. Emphasis will be placed on database design, development, and querying using DBS installed on both LAN and cloud environments. Concepts covered include ER modeling, normalization, and database design theory. Hands-on activities will allow students to create, maintain and query various databases. An introduction and overview of stored procedures, triggers, and data warehousing will also be covered.
Course Outcomes:
Course Overview and Context:
This course will provide the student with knowledge of database concepts. Emphasis will be placed on database design, development, and querying using DBS installed on both LAN and cloud environments. Concepts covered include ER modeling, normalization, and database design theory. Hands-on activities will allow students to create, maintain and query various databases. An introduction and overview of stored procedures, triggers, and data warehousing will also be covered.
Course Outcomes:
- Overview: Purpose of Database Systems; Database Languages; Database Design; Database Engine; Database and Application Architecture; History of Database Systems
- Relational Models: Structure of Relational Databases; Database Schema; Relational Query Languages; The Relational Algebra; Introduction to SQL; Intermediate SQL; Advanced SQL
- Database Design: Database Design Using E-R Model; Relational Database Design
- Application Design and Development: Complex Data Types; Application Development
- Big Data Analytics: Big Data; Data Analytics
- Storage Management and Indexing: Physical Storage Systems; Data Storage Structures; Indexing
- Query Processing and Optimization: Query Processing; Query Optimization
- Transaction Management: Transactions; Concurrency Control; Recovery Systems
- Parallel and Distributed Databases: Database-System Architecture; Parallel and Distributed Storage; Parallel and Distributed Query Processing; Parallel and Distributed Transaction Processing
- Advanced Topics: Advanced Indexing Techniques; Block chain Databases
Credits : 4
Course Overview and Context:
This course will provide the student with knowledge of various data collection and data analysis software packages and their uses from database concepts. Emphasis will be placed on hands-on activities that will allow students to create, maintain and explore different queries and questions from various datasets.
Course Outcomes:
Course Overview and Context:
This course will provide the student with knowledge of various data collection and data analysis software packages and their uses from database concepts. Emphasis will be placed on hands-on activities that will allow students to create, maintain and explore different queries and questions from various datasets.
Course Outcomes:
- Overview: Purpose of Database Systems; Database Languages; Database Design; Database Engine; Database and Application Architecture; History of Database Systems
- Relational Models: Structure of Relational Databases; Database Schema; Relational Query Languages; The Relational Algebra; Introduction to SQL; Intermediate SQL; Advanced SQL
- Database Design: Database Design Using E-R Model; Relational Database Design
- Application Design and Development: Complex Data Types; Application Development
- Big Data Analytics: Big Data; Data Analytics
- Storage Management and Indexing: Physical Storage Systems; Data Storage Structures; Indexing
- Query Processing and Optimization: Query Processing; Query Optimization
- Transaction Management: Transactions; Concurrency Control; Recovery Systems
- Parallel and Distributed Databases: Database-System Architecture; Parallel and Distributed Storage; Parallel and Distributed Query Processing; Parallel and Distributed Transaction Processing
- Advanced Topics: Advanced Indexing Techniques; Block chain Databases
Semester 02 - 20 Credits
Credits : 4
Course Overview and Context:
This course introduces data literacy required as a key twenty-first-century skill. Students will learn the nature of data across different domains and the concepts and skills of data visualization by understanding, questioning, and problematizing how data are generated, analyzed, and used. Students will be able to apply its concepts and skills to visualize their own data, interpret the findings, and examine the impacts of the data-driven decision.
Course Outcomes:
Course Overview and Context:
This course introduces data literacy required as a key twenty-first-century skill. Students will learn the nature of data across different domains and the concepts and skills of data visualization by understanding, questioning, and problematizing how data are generated, analyzed, and used. Students will be able to apply its concepts and skills to visualize their own data, interpret the findings, and examine the impacts of the data-driven decision.
Course Outcomes:
- Introduction: Introduction to data visualization; Brief history of data visualization
- Principles: Data graphics; Static graphics; Data visualization through their graphic presentation; Graph-theoretic graphs, High-dimensional data visualization; Multivariate data glyphs: principles and practice; Linked data views; Visualizing trees and forests
- Methodologies: Linked micro-map plots; Grand tours, projection pursuit guided tours, and manual control; Multidimensional scaling; Multivariate visualization by density estimation; Structured sets of graphs; Regression by parts: fitting visually interpretable models with GUIDE; Smoothing techniques for visualization; Visualizing cluster analysis and finite mixture models; Visualizing contingency table; Mosaic plots and their variants; Matrix visualization; Web-based statistical graphics using XML technologies
- Application: Reconstruction, visualization, and analysis of medical images; Exploratory graphics of a financial dataset, Graphical data representation in bankruptcy analysis, Visualization tools for insurance risk process
Credits : 4
Course Overview and Context:
This course will provide an understanding of data governance policy and ethical considerations while working as a data analyst. The students will be able to critically interpret the governance and regulatory frameworks associated with the capture, processing, and stewardship of data. They will also gain skills to critically interpret the roles and responsibilities of data security, privacy, and data protection. Learners will also build their capacity to analyze and evaluate the intersection of data and ethics in socio-technical environments.
Course Outcomes:
Course Overview and Context:
This course will provide an understanding of data governance policy and ethical considerations while working as a data analyst. The students will be able to critically interpret the governance and regulatory frameworks associated with the capture, processing, and stewardship of data. They will also gain skills to critically interpret the roles and responsibilities of data security, privacy, and data protection. Learners will also build their capacity to analyze and evaluate the intersection of data and ethics in socio-technical environments.
Course Outcomes:
- Data Governance: Data quality and provenance; Data management-Roles and responsibilities. Management of data policies, processes, and procedures; Data integrity & security; Risk management; Models and tools for data governance
- Privacy and Data Protection: The right to privacy – constitutional and statutory protections, privacy, and the European Convention on Human Rights and EU Charter of Fundamental Rights; Common law protection; Data Protection Regulation Scope, processing of personal data, legitimate bases; Principles of data protection; Sensitive data; Issues of consent; Rights, supervision and enforcement; Data Protection in practice includes international transfers, surveillance, cloud computing, and auditing; The current reform of the area
- Ethical Issues Pertaining to Data: Ethics and Computing – examining moral problems when using the Internet – spam, censorship and free speech, the anonymity offered by the Internet; Ethical issues that arising from the increasing use and pervasiveness of information technology and socio-technical systems; Health technology; Pervasive monitoring and tracking; Image, video and sound capture; Identity; Perpetuity of data storage; Trans nationality; Copyright; IOT
Credits : 4
Course Overview and Context:
In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, explore data using statistics and visualizations, transform data for further modeling, model data using machine learning algorithms to predict data patterns, and evaluate these model-based predictions.
Course Outcomes:
Course Overview and Context:
In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, explore data using statistics and visualizations, transform data for further modeling, model data using machine learning algorithms to predict data patterns, and evaluate these model-based predictions.
Course Outcomes:
- Supervised learning Vs. Unsupervised learning
- Maximum likelihood
- Naive Bayes
- Logistic regression
- Gradient descent
- Regularization
- Information theory
- Decision trees
- Representation learning / PCA
- Clustering
- Latent variable models
- Neural networks
Credits : 4
Course Overview and Context:
This course will connect students’ education in the program and their work experiences to their post-graduation goals, whether that is obtaining a new job, earning a promotion, and/or freelancing. The goal is for students to focus on professional development topics so they feel confident in marketing their skills after graduation and have concrete materials that will make them stand out to employers and/or clients.
Course Outcomes:
Course Overview and Context:
This course will connect students’ education in the program and their work experiences to their post-graduation goals, whether that is obtaining a new job, earning a promotion, and/or freelancing. The goal is for students to focus on professional development topics so they feel confident in marketing their skills after graduation and have concrete materials that will make them stand out to employers and/or clients.
Course Outcomes:
- Career Development Theories; Conducting Career Research.
- Networking; Networking Tools; E-Portfolios.
- Conducting a Targeted Job Search; Resumes and Cover Letters.
- Interviewing; Effective job presentation.
- Decision Making; Best Practices in the Workplace.
- Setting Business Goals; Working with Clients.
- Client Documents and Forms – Proposals, Bids, Contracts, Policies, and Procedures; Creating Forms (ex. Needs Assessments).
- Project Organization; Requests for Proposals.
Credits : 4
Students will get an opportunity to conduct innovative research for Data Analytics in an industry of their choice. This course will provide students with the required breadth to jumpstart their careers in the Data Analytics field. During the first quarter of the Second semester, students will be asked to select a specialized topic according to his/her interest and submit a thesis/experimental project report during the semester final examination. The work will be supervised by an internal supervisor and the submitted thesis/report will be evaluated by an expert in a similar research field.
Students will get an opportunity to conduct innovative research for Data Analytics in an industry of their choice. This course will provide students with the required breadth to jumpstart their careers in the Data Analytics field. During the first quarter of the Second semester, students will be asked to select a specialized topic according to his/her interest and submit a thesis/experimental project report during the semester final examination. The work will be supervised by an internal supervisor and the submitted thesis/report will be evaluated by an expert in a similar research field.
Career Opportunity
- Junior Cyber security Analyst
- Information Security Analyst
- SOC Analyst Trainee
- IT Security Support Associate
- Network Security Assistant
- Digital Forensics Assistant
- Cybercrime Investigation Assistant
- Digital Evidence Assistant
- Cyber Incident Response Assistant
- Fraud Investigation Assistant
- Junior Cyber Risk Analyst
- Information Security Compliance Assistant
- IT Risk Management Assistant
- Data Privacy Assistant
- IT Audit Assistant
- Junior Network Security Administrator
- IT Support Executive
- Network Support Associate
- IT Operations Associate
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Frequently Asked Questions
The program carries a total of 40 credits spread across two semesters. Semester 1 consists of 5 core courses (20 credits total), and Semester 2 consists of 4 core courses plus a capstone project/internship (20 credits total). Each course accounts for 4 credits.
Yes. The curriculum includes dedicated modules covering local legislation and frameworks, such as the Digital Security Act (2018), the Information & Communication Technology Act (2006), the Pornography Control Act (2012), the Bangladesh Telecommunication Act (2001), and the National Cybersecurity Strategy of Bangladesh. It also covers local cybercrime patterns, investigative challenges, and court proceedings within Bangladesh.
The PGD in Cyber Security program spans 1 year (12 months) divided into 2 academic semesters. To graduate, students must complete a total of 40 credits (20 credits in Semester 1 and 20 credits in Semester 2).
The total cost for the PGD in Cyber Security program is BDT 41,500 per year (excluding National University registration and examination fees).
(Note: Additional fees for National University Registration & Examinations are billed separately.)
- Admission Form, Academic Calendar & ID Card Fee: BDT 1,500
- Admission Fee: BDT 10,000
- Monthly Fee: BDT 2,500 per month
- Total Program Cost: BDT 41,500
(Note: Additional fees for National University Registration & Examinations are billed separately.)


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