Machine Learning Data Science

NGUYEN THI NGUYET

Data Scientist

Personal Information

Female

Q 29-09-2002

Contact

Zalo +84-86-727*-***

Nguyen Thi Nguyet

# **.********@*****.***

ï linkedin.com/in/nguyetnt

§ github.com/Moon2909

Address

Long Thanh My, District 9, Vietnam

Objective

I am a recent graduate in Data Science with a strong passion for artificial intelligence and data science. I am seeking a position as a Data Scientist, where I can apply my knowledge of statistics and machine learning to analyze data, develop models, and uncover patterns. I am eager to contribute to the development of AI products based on user needs and leverage the latest AI research to drive positive business outcomes. My goal is to deepen my knowledge and skills while contributing to meaningful projects in a collaborative environment. Education

University of Information Technology, Bachelor of Data Science Oct 2020 – Nov 2024

• GPA: 8.04/10.0

• Relevant Coursework:

– Mathematics and Statistics: Discrete Structures; Linear Algebra; Calculus, Probability and Statistics; Advanced Statistics and Probability; Design and Analysis of Experiments.

– Data Science and Machine Learning: Getting and Cleaning Data; Data Analysis and Visualization; Statistical Machine Learning; Deep Learning in Data Science; Big Data Analytics.

– Specialized Applications: Social Media Mining; Medical Image Processing; Natural Language Processing for Data Science; Recommender Systems; Cloud Computing.

– Graduation Project: Thesis.

Projects

Imputation Methods for Continuous Missing Values and LSTM Multivariate Forecasting in Time Series Data of Traffic Flow November 2022

• Objective: Explored imputation techniques and LSTM forecasting on multivariate time series data.

• Methods: Linear Interpolation, SMA, EMA, SMWA, KNN, MICE, DTWBI, LSTM.

• Technologies: Python (Pandas, NumPy, Scikit-learn), R (Tidyverse, Caret), Excel.

• Responsibilities: Collected data, conducted EDA focusing on trend analysis, applied SMWA imputation, and created Excel dashboards.

Detecting Real-Time Anomalies in Stock Time Series Data May 2023

• Objective: Developed a system for detecting anomalies in real-time stock data.

• Methods: Linear Interpolation, DTWBI, LEIAD, basic ML models.

• Technologies: Python (Scikit-learn, Snorkel), Apache Kafka, Apache Spark.

• Responsibilities: Researched and implemented models, collected, labeled, and processed data, conducted EDA, and trained and tested the system. Fruit and Vegetable Detection Using Object Detection Models July 2024

• Objective: Developed a system to detect fruits and vegetables in real-time.

• Methods: Faster R-CNN (ResNet-101), YOLOv5-S, YOLOv9-C, YOLOv10-L, MTDA.

• Technologies: Python (Torch, Detectron2), Apache Kafka, Apache Spark..

• Responsibilities: Collected and labeled data, applied data augmentation techniques, trained and fine-tuned models, and evaluated performance models. Publications

Automatic Textual Normalization for Hate Speech Detection In: Intelligent Systems Design and Applications

ISBN: 978-3-031-64779-6

DOI: 10.1007/978-3-031-64779-6_1

2024

Achievement

Student Science and Technology Project, Batch 2, 2023 Position: 21st in the list of approved projects

December 2023

Technologies

Programming: C++, Python, R, SQL.

Frameworks:

Numpy, Pandas, Matplotlib, Seaborn (Data Processing & Visualization); TensorFlow, Scikit-learn, Keras, PyTorch (Machine Learning); Apache Spark, Hadoop, Kafka (Big Data); Streamlit (Web Development). Other Tools: Microsoft Word, Excel, PowerPoint, LaTeX (Overleaf), Git, Power BI, Azure.

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