Course Overview

Operation & Maintenance of Industrial Substations and Generating Stations (Batch 02)

⏱ Duration: 12 Weeks | 36 Contact Hours (3 Hours per Week)

Evidence of Demand

With the rapid expansion of industrial zones, power plants, EPZs, Hi-Tech Parks, and modern electrical infrastructure in Bangladesh, the demand for skilled professionals in substation and generator operation & maintenance is growing significantly.

Although many engineers and technicians graduate with strong theoretical knowledge, there remains a critical skills gap in hands-on operation, fault diagnosis, safety compliance, and maintenance of live electrical systems such as 33/11 kV substations and industrial generating stations.

Industries now seek professionals who can operate equipment confidently, follow safety standards, reduce downtime, and ensure uninterrupted power supply. This micro-credential course directly responds to that industry demand by delivering practical, site-based, and industry-aligned training.

Purpose and Objectives

This course aims to:

• Provide hands-on training in the operation and maintenance of industrial substations and generating stations

• Develop practical skills in handling transformers, switchgear, LT panels, generators, and protection systems

• Enhance understanding of industrial load management, grounding, and safety protocols

• Familiarize learners with real-world equipment, testing methods, and troubleshooting techniques

Tentative Syllabus (Highlights)

• Introduction to 33/11 kV and 11/0.415 kV substations

• Substation layout, HT & LT switchgear

• Power transformers: operation, protection, and maintenance

• Industrial LT distribution systems & load management

• Diesel and gas generator installation, operation & maintenance

• Battery banks, DC systems, grounding & lightning protection

• Electrical safety standards (BNBC, IEC, NFPA)

• Industrial site visits and final practical evaluation

Learning Outcomes

After completing the course, participants will be able to:

• Identify and explain components of industrial substations and generating stations

• Interpret single-line diagrams and protection systems

• Perform routine inspection and preventive maintenance

• Operate generators safely and manage industrial loads

• Apply electrical safety practices and hazard mitigation techniques

• Conduct basic testing of transformers, batteries, and grounding systems

Target Audience

• Diploma and BSc-level engineering students (EEE, Power, Mechanical)

• Substation and generator operators or technicians

• Junior field engineers and maintenance personnel

• Professionals from factories, power plants, EPZs, and EPC companies

Entry Qualifications

• Diploma in Engineering (Electrical/Power/Mechanical) OR

• BSc in EEE, Mechanical, or related fields (students or graduates)

• Basic understanding of electrical systems and safety

• Prior industrial experience is an advantage but not mandatory

Progression Pathway

After completing this micro-credential, learners may:

• Work as Substation Maintenance Technician, Generator Operator, Electrical Safety Officer, or Maintenance Supervisor

• Progress to advanced certifications in:

  • Power System Protection

  • SCADA & Automation

  • High-Voltage Switching & Safety

• Apply skills directly to industrial jobs, internships, or apprenticeships

Assessment Criteria

• Class participation & attendance – 10%

• Weekly quizzes / viva – 20%

• Practical skill evaluation / group project – 40%

• Field visit report – 10%

• Final practical evaluation & demonstration – 20%

Resource Requirements

Facilities & Equipment

• Multimedia-equipped classrooms

• Functional 33/11 kV and 11/0.415 kV substations

• Industrial generator stations

• Battery banks, DC systems, and protection panels

Tools, Instruments & Materials

• Insulation Resistance (IR) testers

• Earth resistance testers

• Transformer oil testing kits

• Multimeters, clamp meters

• PPE, LOTO kits, relay testing equipment

Learning Materials

• Course manual & handbook

• Weekly presentation slides

• Maintenance checklists

• Field visit logbook

• Safety standards documentation (BNBC, IEC, NFPA)
  
  
  

Standard Course Fee:

The following discounts currently apply:

• Daffodil Students: Tk 2,000/-
• Non-Daffodil Students: Tk 3,000/- (60% Discount Promotional Offer)

Course Overview

Certificate Course in Diabetic Nutrition (Batch 02)

⏱ Duration: 14 hours (7 sessions, 2 hours each) spread across a 6-week schedule | Mode of Delivery: Hybrid (in-person + online) 

Instructor(s) : 

Course Description

This course addresses the urgent need for specialized nutrition expertise in Bangladesh, where diabetes prevalence is 14.2% and rising (IDF, 2023). It equips healthcare professionals with evidence-based skills to manage diabetes through medical nutrition therapy (MNT), covering diagnosis, meal planning, and complication prevention. With hands-on case studies and practical exercises, participants will enhance patient care and meet industry demands for roles in diabetes clinics and community health programs.

Evidence of Demand

• Bangladesh has a 14.2% diabetes prevalence (IDF, 2023), projected to reach 15% by 2030, with 43.5% undiagnosed cases.
• There is an urgent need for professionals skilled in Medical Nutrition Therapy (MNT).
• Hospitals and clinics are increasingly seeking trained dietitians and nurses (as seen on platforms like bdjobs.com).

Purpose and Objectives

Purpose: To provide healthcare professionals with practical skills in diabetic nutrition for effective patient counseling and improved health outcomes.

Objectives:

• Understand diabetes types and glucose homeostasis. * Apply Medical Nutrition Therapy (MNT) for glycemic control.
• Design culturally relevant meal plans (carb counting, GI/GL).
• Manage nutrition for special populations.
• Counsel patients effectively and stay updated on emerging trends.

Course Content & Class Plan (Modules)

• Module 1: Foundations of Diabetes & Nutrition: Diabetes types, epidemiology, basic nutrition, food labels, insulin physiology.
• Module 2: Medical Nutrition Therapy (MNT): MNT goals, carb counting, GI/GL, protein/fat management, meal planning.
• Module 3: Special Considerations & Complications: Medication-nutrition interactions, exercise, complications (nephropathy, CVD), special populations.
• Module 4: Counseling, Monitoring & Trends: Nutritional assessment, behavioral counseling, emerging trends (gut microbiome, fasting), case studies.
• Module 5: Precision Management & Lifecycle Transitions (CGM technology, pregnancy, and geriatric care).

Practical & Field Work

• Label Analysis: Practical exercises during the foundations module.
• Meal Plan Design: Hands-on activities applying ADA guidelines for carb counting and GI/GL management.
• Case Study Review: Analyzing clinical cases regarding complications and special populations.
• Counseling Role-Play: Practical application of behavioral counseling techniques.

Learning Outcomes

Upon completion, participants will be able to:

• Define "healthy food" for diabetes management (ADA-aligned).
• Identify foods to avoid/enjoy for glycemic control.
• Apply simple cooking tips for diabetic-friendly meals.
• Recognize toxins' impact on health and mitigation strategies.
• Adopt lifestyle changes to enhance diabetes control.

   Target Audience & Requirements

Target Audience:

• Dietitians, nutritionists, nurses, community health workers.
• Physician assistants, AYUSH practitioners, BDS/MDS professionals.
• Any professionals aiming to specialize in diabetes care.

Entry Requirements:

• Undergraduate degree or equivalent in health-related fields.
• Minimum Age: 20 years.

Career Pathways

• Graduates can pursue enhanced roles in diabetes clinics and endocrinology departments.
• Work as Diabetes Educators (with additional certifications like CDCES).
• Lead community health programs.

Tools & Resources

• Access to clinical guidelines (ADA).
• IDF reports and research articles.
• Sample food labels.
• Online learning platform for hybrid sessions.

Assessment Criteria

• 30% Laboratory / practical exercises.
• 25% Assignments / case studies.
• 20% Quizzes / knowledge checks.
• 15% Final project / research report.
• 10% Final examination.

Financial Information

• Tentative Course Fee: 8,000 BDT.

• Students: 3,000 BDT.
• Professionals: 5,000 BDT.

Course Overview

Hands-on Professional Training on HPLC: Your Gateway to Analytical Excellence - (Batch 4)

Course Code: DIU-MCA-MC29003
Course Title: Hands on Professional Training on HPLC: Your Gateway to Analytical Excellence
Duration: 40 hours (5 days) 
Mode: In-person (interactive lectures, hands-on lab sessions, data analysis workshops) 
Class Size: 20-25 participants 
Course Equivalent: 2 Credit Hours

Course Fee: Students: 5,000 BDT; Professionals: 6,000 BDT

Course Description 
Hands on Professional Training on HPLC: Your Gateway to Analytical Excellence (MC29001) meets the soaring demand for skilled High-Performance Liquid Chromatography (HPLC) professionals, with over 70% of analytical job postings in pharmaceuticals, biotechnology, and food safety requiring HPLC expertise. This intensive 5-day course offers hands-on training in operating HPLC systems, method development, sample analysis (pharmaceuticals, foods, herbal products), and troubleshooting. Aligned with industry standards, it prepares participants for quality control, research, or regulatory roles in high-growth sectors. 🌟

Syllabus:

• Day 1: Intro to HPLC – Instrumentation, principles, applications. Activities: System setup demo. Resources: HPLC manuals.
• Day 2: Mobile Phase & Sample Prep – Solvent selection, sample preparation. Activities: Prep lab. Resources: Solvent guides.
• Day 3: Sample Analysis – Pharmaceuticals, foods, herbal products. Activities: Sample analysis lab. Resources: Sample protocols.
• Day 4: Method Development & Data Processing – Method optimization, chromatogram analysis. Activities: Data analysis workshop. Resources: Software tutorials.
• Day 5: Troubleshooting & Maintenance – Issue resolution, system care. Activities: Troubleshooting case study. Resources: Maintenance checklists.

Target Audience 

• 3rd-year+ students in Pharmacy, Genetic Engineering, Biotechnology, Nutrition, Food Engineering, Agricultural Science.

• Graduates and early-career professionals seeking analytical skills.

Entry Requirements 

• Foundational knowledge in analytical chemistry.

• 3rd-year+ students in Health or Medical Sciences.

• Minimum age: 20 years.

Career Pathways 

• Analytical Chemist, Quality Control Analyst.

• Research Scientist in pharma, food, biotech.

• Roles in advanced health/life sciences research.

• Professional Certificate in HPLC Analytical Techniques.

Course Overview

Enterprise AI: Designing, Deploying, and Scaling AI Solutions for Business Transformation

Lead Instructor: Professor Dr. Emdad Khan (CEO, InternetSpeech Inc. USA)

Co-Instructor: Md. Ridoy Sarkar (Lecturer, Daffodil International University)

⏱ Duration: 10 Weeks | 48 Hours (Total)

Course Description

This course is designed to bridge the critical gap between technical AI skills and business value creation. Moving beyond experimental models, this program equips professionals to architect, govern, and scale AI solutions across large organizations. It focuses on integrating AI with legacy systems (ERP, CRM), ensuring compliance, and optimizing ROI for sustainable business transformation.

Evidence of Demand

With the rapid rise of AI and Digital Transformation, organizations are moving beyond experimental AI models to scalable, governed enterprise solutions. There is a critical gap for professionals who can bridge technical AI skills (Data Science, LLMs) with business value creation (ROI, Compliance, Integration). Industry demands architects who can deploy AI across supply chains, HR, and finance at scale.

Purpose and Objectives

The purpose of this course is to equip students with the skills to architect, govern, and scale AI solutions in large organizations. Objectives:

• Understand Enterprise AI architecture & Digital Twins.
• Learn to integrate AI with legacy systems (ERP, CRM).
• Master AI Governance, Security, and Compliance.
• Develop strategies for ROI optimization and scaling.

Course Content & Class Plan (Modules)

Tentative Syllabus:

• Module 1: Intro to Enterprise AI (Context & Maturity Models)
• Module 2: Generative AI & LLMs (RAG, Knowledge Assistants)
• Module 3: Enterprise Data Foundations (Data Lakes, Governance)
• Module 4: AI Solution Architecture (Cloud vs. On-Prem, Microservices)
• Module 5: Enterprise AI Use Cases & Case Studies (Finance, Health, Mfg)
• Module 6: AI & Agentic Workflow Development (Lifecycle, Feature Eng.)
• Module 7: MLOps & Model Lifecycle Management (CI/CD, Monitoring)
• Module 8: AI Integration with Enterprise Systems (ERP/CRM APIs)
• Module 9: AI Governance, Security & Compliance (Privacy, Ethics, Transparency)
• Module 10: Decision Intelligence & Analytics (Predictive vs. Prescriptive)
• Module 11: Scaling & Performance Optimization (Cost/Reliability)
• Module 12: Capstone Project (Design & Implementation)

Practical & Field Work

Note: Practical assessments constitute approximately 70% of the course grade.

• Labs & System Design: Hands-on tasks involving MLOps tools (Docker, MLflow), visualization (PowerBI/Tableau), and Python environments.
• Capstone Project: Students will design and implement a complete Enterprise AI solution, including defining the business problem, creating the architecture, and providing a live demonstration.
• Examples include: AI-Powered Academic Administration Systems, Enterprise Knowledge Assistants (RAG-based), and Predictive Analytics Dashboards.

Learning Outcomes

Upon completion, students will be able to:

• Strategize: Identify AI opportunities for business optimization.
• Architect: Design scalable Cloud/Hybrid AI systems.
• Integrate: Connect AI with enterprise apps via APIs.
• Govern: Implement security and compliance frameworks.
• Evaluate: Measure AI success via ROI and impact.
• Scale: Deploy sustainable systems using MLOps.

Target Audience

• CSE/SWE/CIS/ITM Students (3rd/4th Year)
• Data Scientists & ML Engineers
• Solution Architects & Project Managers
• Industry Professionals seeking AI leadership roles

Entry Requirements

• Minimum Age: 20 Years
• Basic Programming: Python recommended
• Foundational Knowledge: Introduction to AI/Machine Learning and fundamentals of Databases & Information Systems
• Resource Requirements: Internet/Cloud Access

Career Pathways

• Enterprise AI Architect
• AI Product Manager
• MLOps Engineer
• AI Transformation & Digital Transformation Consultant
• Chief AI Officer (CAIO) track

Tools & Resources

• Programming: Python Environment (Jupyter/Colab)
• MLOps Tools: Docker, Kubernetes, MLflow, Jenkins/GitHub Actions
• Cloud Platforms: AWS / Azure / Google Cloud (Conceptual & Hands-on)
• ML Frameworks: Scikit-learn, TensorFlow, PyTorch
• Visualization: PowerBI / Tableau

Assessment Criteria

• Case studies & assignments: 30%
• Labs & system design tasks: 30%
• Final project: 40%

Standard Course Fee: Tk 15,000/-

The following discounts currently apply:

• Daffodil Students: Tk 5,000/- (67% Discount Promotional Offer)
• Non-Daffodil Students: Tk 7,500/- 

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)

Course Overview

Community-Based Management of Acute Malnutrition (CMAM)

⏱ Duration: 6 Weeks | 16 Contact Hours | Mode: Blended learning (online and in-person workshops)

Instructor

• Syeda Sumaiya Nasrin: Manager- Knowledge Management and Donor Reporting, Health and Nutrition, Humanitarian Crisis Management Programme (HCMP).
• Lipi Rani Mondal: Lecturer (Senior-scale), Department of Nutrition and Food Engineering, Daffodil International University.

Course Description

The purpose of CMAM is to maximize coverage and access to life-saving treatment for acute malnutrition by providing decentralized, home-based care before medical complications develop.

Evidence of Demand

• The demand for a specialized CMAM course in Bangladesh is driven by critical gaps in public health infrastructure, high malnutrition burdens, and the urgent need for trained community-level responders.
• Severe Burden of Malnutrition: Bangladesh records a maternal mortality ratio of 173 per 100,000 live births and an infant mortality rate of 28 per 1,000 live births (World Bank, 2021). Frontline training is time-sensitive, as the first 1,000 days of an infant's life are vital for long-term developmental outcomes (WHO, 2021).
• Socioeconomic Barriers: Decentralizing acute wasting treatment to villages helps bypass economic barriers, as approximately 24% of the population survives below the poverty line (BBS, 2020), making hospital journeys financially impossible for many. Community strategies also reduce urban health system pressures and transmission risks.
• Evidence-Based Protocols: Standardized, community-oriented protocols significantly drive down infant wasting mortality, and systematic clinical evaluations (like MUAC metrics) ensure cases are tracked uniformly to prevent relapse.
• Empowering Frontline Infrastructure: Active social mobilization and dedicated field worker tracking drastically optimize longitudinal patient adherence.

Course Objectives

This course aims to:

• Early Detection: Screen children early using Mid-Upper Arm Circumference (MUAC).
• Outpatient Care: Treat severe acute malnutrition (SAM) at home using RUTF.
• Inpatient Stabilization: Provide 24-hour clinical care for complicated malnutrition cases.
• Prevention: Manage moderate acute malnutrition (MAM) to prevent severe wasting.
• Integration: Embed malnutrition treatment into existing primary healthcare systems.

Course Content & Class Plan (Modules)

• Week 1: Foundations of Acute Malnutrition & The CMAM Framework: Global and national burdens; differentiating wasting, stunting, and underweight; shifting to decentralized CMAM.
• Week 2: Community Mobilization & Active Case-Finding: Strategies for community engagement; barrier identification; stakeholder mapping and trust-building.
• Week 3: Outpatient Therapeutic Program (OTP) Management: Admission/discharge criteria for SAM without complications; routine medical protocols.
• Week 4: Inpatient Care & Management of Complicated SAM: Stabilization Center (SC) criteria; treating hypoglycemia, hypothermia, and dehydration.
• Week 5: Managing Moderate Acute Malnutrition (MAM) & IYCF: Targeted SFP protocols; Infant and Young Child Feeding (IYCF) practices; maternal nutrition/mental health.
• Week 6: M&E, Reporting, and Course Wrap-Up: Monitoring indicators (cure, default, death rates); HMIS data integration.

Practical & Field Work (60% of Course)

• Navigating national integrated management guidelines and CMAM core components.
• Hands-on practice using MUAC tapes, assessing bilateral pitting edema, and role-playing referral conversations.
• Conducting Ready-to-Use Therapeutic Food (RUTF) appetite tests and filling OTP treatment cards.
• Calculating and preparing therapeutic milks (F-75 and F-100) and drafting feeding schedules.
• Counseling caregivers on nutrition using local foods and designing IYCF-nutrition sessions.
• Compiling monthly facility nutrition reports and supply chain forecasting for RUTF.

Learning Outcomes

After completing the course, participants will be able to:

• Identify and classify acute malnutrition forms (SAM, MAM, stunting) using standard WHO anthropometric indicators.
• Perform accurate physical measurements (MUAC taping, grading pitting edema) and standard appetite tests.
• Prescribe correct routine medical therapies and formulate F-75/F-100 feeding protocols.
• Execute proper triage, referral, and discharge decisions.
• Design localized mobilization strategies and counsel mothers on IYCF and postpartum mental well-being.
• Maintain accurate facility records, calculate program indicators, and forecast supply-chain requirements.

Target Audience & Requirements

Target Audience:

• Community health workers supporting mothers and infants.
• Healthcare professionals (nurses, midwives) focused on maternal/child health.
• Nutritionists, dietitians, NGO staff in underserved communities, public health students, and policy makers.
• Class Size: Maximum 25 participants.

Entry Requirements:

• A background in health, nutrition, or social sciences (e.g., diploma or degree).
• Relevant work experience in community health, maternal and child health, or nutrition.
• Basic understanding of nutrition and health concepts.
• Minimum Age: The lowest age to enroll is 20 years.

Career Pathways

• Roles as Community Health Workers and management positions with UNICEF, WFP, BRAC, ACF.
• Specialized training at icddr,b.
• Policy advocacy with the Ministry of Health.
• Research contributions at icddr,b.

Tools & Resources

• MUAC measuring tapes and infant weighing scales.
• Sample growth charts (W/A and W/L).
• Case scenarios (printed descriptions of hypothetical infants).
• Assessment checklist and access to nutritional assessment tools.
• Online learning platform and instructional materials (readings, case studies).

Assessment Criteria

• Assignments, Quizzes, Group/Community Project.
• Week 1 online knowledge check.
• Case study review on program defaulting.
• Practical OSCE (Objective Structured Clinical Examination).
• Midterm exam on inpatient protocols and final comprehensive exam with field logbook submission.

Certification

Successful completion of assessments, practical OSCEs, exams, and field logbook submissions will result in the awarding of a micro-credential certificate.

Financial Information

• Tentative Course Fee: 2,500 BDT.

Course Overview

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.