CV

Contact Information

Name Sabuj C. Bhowmick
Professional Title MSc Data Analytics Student | Data Science | Machine Learning | Computational Biology | Applied Statistics
Email sabuj606@gmail.com
Location Turku,

Professional Summary

MSc Data Analytics student at the University of Turku, Finland, with an interdisciplinary background in statistics, mathematical sciences, public health, IoT engineering, machine learning, and computational research. My work focuses on reproducible, interpretable, and methodologically rigorous data-driven solutions across data science, applied statistics, bioinformatics, computational biology, public health analytics, time-series forecasting, IoT-based data systems, and research-oriented software development.

Experience

  • 2023 - 2023

    Kuopio, Finland

    Project Worker, 5G to Boost Digitization
    Savonia University of Applied Sciences
    Worked on applied data analysis, software development, and machine learning tasks in a 5G- and IoT-related digitalization project.
    • Developed data-driven workflows for solar energy production analysis and forecasting.
    • Applied statistical and machine learning methods to support forecasting-oriented analysis.
    • Integrated weather data sources and contributed to technical documentation and reporting.
    • Used R, Python, Power BI, and machine learning libraries.
  • 2023 - 2024

    Finland

    Intern, Starlink Internet Feasibility Testing
    Savonia University of Applied Sciences / Voimatel Project
    Evaluated satellite internet feasibility for remote operation use cases, comparing Starlink connectivity with existing mobile network alternatives.
    • Set up and tested Starlink equipment for remote connectivity scenarios.
    • Compared latency, bandwidth, and signal reliability in practical field conditions.
    • Prepared documentation and contributed to applied IoT and remote connectivity evaluation.

Education

  • - Present

    Turku, Finland

    MSc
    University of Turku
    Data Analytics
    • Machine learning, data analytics, statistical modeling, data-intensive systems, research-oriented computing
    • Current MSc studies focused on data analytics, machine learning, statistical modeling, and computational methods.
    • Developing expertise in reproducible data science, model validation, and research-oriented software workflows.
  • - 2019

    Norway

    MSc
    Norwegian University of Science and Technology
    Mathematical Sciences
    • Graduate-level training in mathematical sciences, analytical reasoning, and quantitative modeling.
  • - 2015

    Oslo, Norway

    MPhil
    University of Oslo
    International Community Health
    • Research-focused training in public health, epidemiological thinking, and quantitative health research.
  • - 2011

    Dhaka, Bangladesh

    MSc
    University of Dhaka
    Statistics
    • Advanced academic training in statistical methods, probability, inference, and applied data analysis.
  • - 2010

    Dhaka, Bangladesh

    BSc
    University of Dhaka
    Statistics
    • Foundation in statistics, mathematics, data analysis, and quantitative research methods.

Skills

Programming and Data Analysis (Advanced): Python, R, SQL, Bash, Git, GitHub, Quarto, R Markdown, LaTeX
Machine Learning (Intermediate to Advanced): scikit-learn, XGBoost, random forest, support vector machines, regression, classification, cross-validation, model evaluation
Statistics and Modeling (Advanced): applied statistics, regression, hypothesis testing, time-series analysis, biostatistics, epidemiological analysis
Bioinformatics and Computational Biology (Developing / Research-focused): Bioconductor, microbiome analysis, multiomics integration, Joint RPCA, biological data workflows

Projects

  • Solar Energy Forecasting

    Comparative analysis of statistical and machine learning models for solar energy production forecasting.

    • Compared ARIMA, ARMA, linear regression, random forest, support vector regression, and XGBoost.
    • Used MAE, MSE, and RMSE for model evaluation.
    • Focused on feature engineering, validation, model comparison, and reproducible reporting.
  • Joint RPCA and Multiomics Integration

    Research-oriented implementation and learning project focused on Joint Robust Principal Component Analysis for multiomics and biological data integration.

    • Explored R-based workflows for dimensionality reduction and biological data integration.
    • Focused on reproducibility, preprocessing, method interpretation, and structured implementation.

Languages

Bangla : Native
English : Professional working proficiency
Finnish : Basic / developing

Interests

Scientific Computing and Reproducible Research: reproducible workflows, research software, computational analysis, publication-quality reporting
Data Science for Health and Biological Systems: bioinformatics, computational biology, public health analytics, biostatistics, machine learning