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Final Project - Data Science


You have completed four courses of Data Science Program. Having started with Introduction to Data Science course, you learned about Data Science's concepts, methodology, algorithms and applications of Data Science. In the second course, Data Analysis, you acquired how to query data from database, process data using Python and analyze data with visualization. Then, in the Machine Learning course, you learned  the basics of machine learning and its application in different fields such as health care, banking, telecommunication and about the most effective machine learning techniques. Also in the Deep Learning course, you acquired knowledge on the modern neural networks and their applications in computer vision and natural language understanding.

In this final project, you will have a chance to engage in a real-world project and actually experience the tasks of a data scientist first-hand. For the project requirements, you will describe the Business Understanding in a report. Then you will use your analysis skill to understand provided data to find the best features. After that, you will apply machine learning to build a predictive model. Finally, you will learn how to make a RestFul API that makes prediction with new data.


Course code: DSP305x 
Course name:

Final Project - Data Science

Credits: 3
Estimated Time: 6 weeks. 


  • Comprehensively manipulate data science life cycle to a real problem.
  • Apply analytical and visualization skills to analyze the data and complete feature selection.
  • Choose the suitable Machine Learning algorithms and reasonable evaluation metrics for a real problem.
  • Apply a great number of techniques to improve the model accuracy.
  • Understand and apply Flask to create an API for a data science project.
  • Be able to write a data science report in details.


  • Guide 1: Project Overview    
  • Guide 2: Project Details          
  • Guide 3: Project Instruction       
  • Guide 4: Project Rubrics         
  • Guide 5: Project Schedule Guide
  • Guide 6: Project Submission & Defense



M.S. Vu Thuong Huyen

  • Data Scientist at FPT Software Company Limited – a subsidiary of FPT Corporation
  • Master of Software engineering, VNU University of Engineering and Technology
  • Bachelor of Engineering, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology
  • Research fields: Machine learning, Deep learning, Reinforcement Learning, Natural Language Processing…
  • Profile online: 


Course Reviewer



Course Tester



Ph.D. Dang Hoang Vu

  • FPT Science Director
  • Ph.D. in Mathematics, University of Cambridge
  • Core member of R&D activities in FPT Corporation
  • Main responsibility in analytics side of FPT’s Data Management Platform and data science research

B.A. Ho Quoc Bao

  • Research Assistant, Exchange Master Student in Micro and Nano Technology, University of South Eastern Norway 
  • Master Student in Telecommunication Engineering, HCMUT
  • Bachelor of Electronics and Telecommunications Engineering, HCMUT
  • Research fields: Signal Processing, Modelling, Machine Learning, Optical cable, Ultrasound Signal
  • Online profile:

 Program Reviewers

 Assoc. Prof. Tu Minh Phuong

Ph.D. Nguyen Van Vinh

Ph.D. Tran The Trung

  • Dean of IT Faculty, Posts and
    Telecommunications Institute of Technology (PTIT)
  • Expert & technological consultant in AI & machine learning
  • Head of Machine Learning &
    Application laboratory in PTIT
  • Lecturer & core member of AI Lab, University of Technology - VNU
  • AI expert & consultant for DPS, Fsoft
  • Ph.D. in Computer Science, Japan Advanced Institute of Science &
  • Bachelor’s degree in IT, University of Science, VNU
  • Director of FPT Technology
    Research Institute, FPT University
  • Ph.D. in Computational Physics, UVSQ Université de Versailles Saint-Quentin-en-Yvelines
  • M.S. in Astrophysics, Pierre & Marie Curie University
  • B.S. in Theoretical & Mathematical Physics, University of Melbourne

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