Course Overview

The Complete Research Guide and Publication Roadmap (Batch 02)

⏱ 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.