Academic Journey in Data Science

Combining software engineering fundamentals with machine learning research to tackle real-world problems.

Curriculum Highlights View Projects
B.Sc. in Software Engineering · Data Science Major

Daffodil International University · CGPA: 3.72 / 4.00 · 2022 – Present · Dhaka, Bangladesh.

Academic Highlights

Key milestones that define my path from foundational science to applied AI research.

Daffodil International University · 2022 – Present

B.Sc. in Software Engineering, Data Science Major.

Immersed in advanced machine learning, deep learning, and data-driven research while maintaining a 3.72 CGPA.

  • Conducting NLP research around RAG, intelligent agents, and LLM evaluation strategies.
  • Exploring quantum computing fundamentals to anticipate next-generation computational models.
  • Collaborating on applied ML projects focused on healthcare, misinformation, and forecasting.
View related projects

College Foundations

Science-focused higher secondary studies that strengthened analytical reasoning and experiment design.

Karnfuli Government College

Higher Secondary Certificate in Science · 2021 · GPA 4.42 / 5.00 · Chattogram, Bangladesh.

  • Science-focused college curriculum that built analytical and experimental rigor.
  • Participated in lab work and competitions, fueling curiosity for data-driven insights.
Explore recognitions

Secondary School Foundations

Secondary schooling that fostered scientific curiosity and laid the groundwork for data-driven thinking.

P.D.B Secondary School

Secondary School Certificate in Science · 2018 · GPA 4.56 / 5.00 · Chattogram, Bangladesh.

  • Engaged in science projects and competitions that emphasized disciplined experimentation.
  • Developed foundational skills in mathematics and physics, motivating future data science pursuits.
Explore recognitions

Focus Areas & Strengths

Disciplines reinforced through coursework, research labs, and independent exploration.

Machine Learning & Deep Learning

Designing and training models that span MLPs, CNNs, ensemble methods, and responsible evaluation.

Natural Language Processing

Exploring RAG pipelines, intelligent agents, and LLM fine-tuning for trustworthy language systems.

Cloud & MLOps

Leveraging GCP, AWS, Docker, and Kubernetes to operationalize ML workloads efficiently.

Mathematics & Statistics

Applying probability, statistics, and optimization to ground machine learning experimentation.