Course Overview

GMP and HACCP in Food System: Essentials for Food Safety and Quality Management

⏱ Duration: 18 Hours (Equivalent to 1 Credit Hour) | Mode of Delivery: Offline (In-person classroom lectures and practical sessions)

Instructor(s):

• Md. Harun-Ar Rashid: Former Head of QC and R&D, Eurasia Food Processing Industry; expert in HACCP, GMP, food safety.
• Mohaiminul Hasan Shakil: Lead Auditor-Food, Bureau Veritas-Bangladesh Pvt Limited; specialist in food safety auditing.

Course Description

GMP and HACCP in Food System: Essentials for Food Safety and Quality Management (MC-34-014) meets the global demand for certified food safety professionals, driven by stringent regulatory standards and sustainable production goals. This course equips participants with practical skills to implement Good Manufacturing Practices (GMP) and Hazard Analysis and Critical Control Points (HACCP) in food production. Through case studies, hands-on assignments, and expert-led sessions, learners will master risk assessment, compliance, and auditing, preparing them for roles in quality assurance, food safety, and regulatory agencies.

Evidence of Demand

• There is a growing global demand for certified food safety professionals due to stringent regulatory standards (Codex Alimentarius, ISO 22000), export compliance requirements, and sustainable production goals.
• Food industries increasingly require GMP & HACCP-trained personnel for QA/QC and compliance roles.

Purpose and Objectives

This course aims to train participants in GMP and HACCP principles, enabling them to ensure food safety, quality assurance, regulatory compliance, and effective risk management in food production systems.

Course Content & Class Plan (Modules)

• Module 1: Intro to Food Safety Systems: Codex Alimentarius, ISO 22000, global standards.
• Module 2: Fundamentals of GMP: Facility design, hygiene, personnel training, documentation control.
• Module 3: HACCP Framework: 7 principles, hazard analysis, CCP identification.
• Module 4: Risk Assessment & Controls: Biological, chemical, physical hazards; preventive measures.
• Module 5: Case Studies & Applications: Implementation challenges, compliance audits.
• Module 6: Assessment & Certification Preparation: HACCP plan drafting & presentation.

Practical & Field Work

• GMP Checklists: Executing GMP checklist exercises for facility design and hygiene. * HACCP Drafting: Hands-on drafting of HACCP plans.
• Risk Simulation: Participating in risk assessment simulations for biological, chemical, and physical hazards.
• Case Analysis: Analyzing real-world case studies regarding implementation and compliance.
• Presentations: Engaging in presentation workshops for HACCP plan reviews.

Learning Outcomes

After completing the course, participants will be able to:

• Design and implement GMP & HACCP systems.
• Conduct hazard analysis and identify CCPs.
• Perform risk assessments in food production.
• Document, audit, and improve food safety systems.
• Qualify for professional roles in food safety management.

Target Audience & Requirements

Target Audience:

• Food industry professionals.
• QA/QC personnel.
• Food safety officers.
• Food science students & researchers.
• Food & beverage entrepreneurs.

Entry Requirements:

• Basic knowledge of food safety/food science or relevant industrial experience.
• Minimum Age: 18 Years.

Career Pathways

Learners can follow this progression pathway: QA/QC Executive → HACCP Coordinator → Food Safety Officer → Lead Auditor (ISO 22000/HACCP) → Regulatory Agency Roles → Advanced Certification in Food Safety Management.

Assessment Criteria

• Final Presentation – 40%
• HACCP Plan Assignment – 20%
• Quizzes – 20%
• Participation – 20%

Tools & Resources

• Classroom with projector and presentation equipment.
• HACCP plan templates and printed GMP checklists.
• Codex guidelines and ISO reference materials.
• Case study documents and a whiteboard.

Financial Information

• Tentative Course Fee: 4,000 BDT per participant.

Course Overview

The Complete Research Guide and Publication Roadmap

⏱ Duration: 21 Hours. | Mode of Delivery: In-person

Instructor(s)

1. Md Mizanur Rahman: Lecturer (Senior Scale), Department of CSE, Daffodil International University.
2. RKO Shahed Hossain Khan: Founder and Chairman of SkillChefs Private Limited.

Course Description

This professional course is designed to equip learners with the core research skills and the technical foundation in Python, Machine Learning, and Deep Learning required to begin a publishable research project. The course is structured in a natural research flow: learners first build the research foundation, then immediately learn the data analysis tools, and finally come back to the writing and publication preparation steps.

Evidence of Demand

• In Bangladesh, undergraduate and graduate students often complete their degrees without ever publishing a research paper due to a lack of training in practical workflows.
• Globally, scholarship admissions, higher studies opportunities, and competitive job markets increasingly value applicants who already have research publications.
• In emerging fields, hands-on skills with Python and ML libraries have become a baseline requirement rather than an advantage.
• Students heavily struggle with early steps like domain selection, literature review, and methodology design, creating a steady demand for structured, mentorship-based programs.

Purpose and Objectives

• Purpose: To walk the learner from the absolute basics of research to building a complete research foundation, while simultaneously providing the Python and Machine Learning skills needed to execute a modern data-driven project.
• Objectives:
• Understand the full research workflow, from domain selection to manuscript preparation.
• Read, analyze, and review journal articles in a systematic and critical way.
• Identify a meaningful research gap and design a hypothesis around it.
• Work confidently with Python, key data libraries, and Machine Learning/Deep Learning techniques.
• Write each section of a research paper, including the introduction, methodology, results, abstract, and conclusion.

Course Content & Class Plan (Modules)

• Class 01: Orientation, overall discussion of research, necessary platforms, domain selection.
• Class 02: Article search strategy (journal ranking, selection strategy, article access, predatory journals), reading strategy of an article, literature review.
• Class 03: Identifying research gap, designing methodology, building hypothesis.
• Class 04: Data collection tools (Kobo Toolbox), data description, and diagram design tools.
• Class 05: Systematic literature review writing.
• Class 06: Introduction writing (background knowledge, highlighting relevancy, objectives and contributions).
• Class 07: Methodology writing, result analysis and discussion writing.
• Class 08: Basic Python, development environment, installation, Hello World, conditions, loops, string operations, functions, list, tuple, set, dictionary.
• Class 09: Introduction to Python libraries (NumPy, Pandas), Matplotlib, Seaborn, OpenCV and PIL.
• Class 10: Introduction to Machine Learning, regression (linear and logistic), classification and ML classifier implementation.
• Class 11: Feature extraction and selection, ensemble Machine Learning.
• Class 12: Introduction to Deep Learning, transfer learning model implementation, CNN model, hyperparameter tuning.
• Class 13: Abstract and conclusion writing, referencing and reference management tools (Mendeley, Zotero), plagiarism and AI checking tools.
• Class 14: Journal selection, manuscript preparation (LaTeX and Word), article submission, address review, and research proposal making.

Practical & Field Work

• Data Collection & Mapping: Practical use of tools like Kobo Toolbox and diagram design tools.
• Programming Exercises: Writing Python code utilizing core data structures, loops, and conditions.
• Model Implementation: Applying regression, classification models, and Deep Learning (CNNs) on real datasets.
• Manuscript Preparation: Hands-on writing practice for academic sections and formatting using tools like LaTeX and Word.

Learning Outcomes

Upon completion, participants will be able to:

• Research Workflow: Choose a research domain, search for quality articles avoiding predatory journals, and identify clear research gaps.
• Methodology Design: Build testable hypotheses and use appropriate data collection tools.
• Academic Writing: Write systematic literature reviews and fully structured paper sections while using referencing and plagiarism tools appropriately.
• Programming Foundation: Set up a Python environment and visualize data using NumPy, Pandas, Matplotlib, and Seaborn.
• Machine Learning Skills: Apply regression/classification models, perform feature extraction, and understand basic Deep Learning frameworks.

Target Audience & Requirements

• Target Audience:
• Undergraduate and graduate students of any discipline, or fresh graduates.
• Early career researchers, university teachers, and research assistants.
• Professionals interested in transitioning into data-driven research.
• Entry Requirements:
• Currently studying or completed an undergraduate degree in any discipline (B.Sc in Computer Science and Engineering noted for 3rd year, final year, or graduate).
• Basic computer literacy is expected, but no prior research or programming experience is required.
• Minimum Age: 21 years.

Career Pathways

• Continue to advanced phases of the Complete Research Guide (Advance Statistics and Paper Writing/Publication).
• Apply for research assistant positions in universities and research labs.
• Begin working on independent research papers for journal or conference submission.
• Pursue higher studies (MS or PhD) abroad with a stronger research profile.
• Move into data analyst, junior ML engineer, or research analyst roles in the industry.

Tools & Resources

• Well-equipped classroom and lab room for Python, Machine Learning, and Deep Learning classes.
• Data collection and management tools (Kobo Toolbox, Mendeley, Zotero).
• Development environments and Python libraries (NumPy, Pandas, OpenCV, PIL, etc.).
• Manuscript preparation software (LaTeX and Word) and AI/plagiarism checkers.

Assessment Criteria

• Class attendance and participation in live sessions.
• Weekly Quizzes and practical assignment session performance.
• A short research proposal draft prepared by the end of the course.
• Final evaluation through a viva or oral discussion on the prepared proposal.

Financial Information

• Tentative Course Fee: 2,000 BDT.

Course Overview

Fundamentals of Astronomy and Astrophysics

Instructor(s):

• Arif Ahmed: Professor of Practice, Dept. of MCT, DIU.
• Ahmad Al-Imtiaz: Researcher in Observational Astronomy.

⏱ Duration: 12 Weeks | 36 Hours Total (3 Hours per week) | Mode: Blended (Online theory + In-person observation sessions)

Course Description

This course provides a solid foundation in the theoretical aspects of astrophysics and the practical application of observational tools. It is designed to foster scientific thinking and inspire learners toward advanced space science studies by offering interdisciplinary insights linking physics, mathematics, and technology.

Evidence of Demand

An online survey showed that over 90% of respondents expressed strong interest in astronomy and astrophysics-related training. With growing global curiosity about space and increasing accessibility to tools like telescopes and astrophotography, there is a rising demand for structured foundational knowledge in Bangladesh and beyond.

Course Objectives

This course aims to:

• Build a solid foundation covering the solar system, stars, galaxies, and the universe.
• Teach practical skills in observational astronomy and astrophotography.
• Encourage scientific thinking, data interpretation, and analysis.
• Inspire learners toward careers/education in astrophysics.
• Provide interdisciplinary insights linking physics, math, and technology.

Course Content & Class Plan (Modules)

Tentative Syllabus (Highlights):

• Weeks 1-2 / Module 1: Introduction to Astronomy and Astrophysics – Overview, history, and key concepts.
• Weeks 3-4 / Module 2: Solar System and Planetary Science – Planets, moons, orbits, and analyzing orbital data.
• Weeks 5-6 / Module 3: Stellar Astronomy – Stars, the Hertzsprung-Russell (HR) Diagram, and stellar evolution.
• Weeks 7-8 / Module 4: Galaxies and Cosmology – Galaxy types, Big Bang theory, and Dark Matter.
• Weeks 9-10 / Module 5: Observational Astronomy – Telescopes and Astrophotography basics.
• Weeks 11-12 / Module 6: Advanced Topics – Black Holes, Relativity, and Exoplanets.

Practical & Field Work

• Observation Sessions: Hands-on telescope sessions and observation projects.
• Astrophotography: Practical applications using CCD cameras, DeepSkyStacker, and PixInsight.
• Data Analysis: Plotting HR diagrams, analyzing orbital data, and interpreting observational outputs.

Learning Outcomes

Upon completion, participants will be able to:

• Understand key astronomical and astrophysical concepts.
• Identify and describe celestial objects and phenomena.
• Use telescopes and basic astrophotography tools effectively.
• Interpret observational data and produce astrophotography outputs.
• Recognize the interdisciplinary nature of space science.

Target Audience & Requirements

Target Audience:

• Undergraduate students in physics, engineering, and related fields.
• Educators, amateur astronomers, and science communicators.
• Space enthusiasts seeking structured learning.

Entry Requirements:

• Minimum Qualifications: High school-level understanding of physics and mathematics.
• Experience: No prior experience in astronomy is required.
• Minimum Age: 18 years or older.

Career Pathways

After completing this micro-credential, learners may progress to:

• Advanced Study: Preparation for astrophysics, cosmology, or observational astronomy programs.
• Professional Roles: Careers in space research, science education, and science communication.
• Outreach & Hobby: Practical applications in astrophotography and telescope-based outreach programs.

Assessment Criteria

• Projects: Astrophotography projects and observation logs – 40%.
• Quizzes & Assignments: Regular knowledge checks – 30%.
• Final Exam: Comprehensive assessment – 30%.

Tools & Resources

• Equipment: Telescopes, CCD cameras, and tripods.
• Software: Astrophotography tools such as DeepSkyStacker and PixInsight.
• Facilities & Materials: Classroom with a projector and internet access, lecture slides, star charts, and reference books.
• Class Size: 25-30 participants.

Financial Information, Remuneration & Certification

• Tentative Course Fee: 5,000 BDT (For Promotional offer).
• Promotional offer Fee : 3,000 BDT (30% OFF)