The Faculty of Mathematics - Mechanical - Informatics has selected the Data Science training program of the University of Michigan, USA as a model program to build its training program because the training objectives of this program are suitable. conditions and requirements for human resource training in this field in the context of Vietnam in general and in line with the strength of the University of Natural Sciences in particular.

The overall goal of the Data Science program is to train human resources who are highly competitive in the labor market in the period of regional and world economic integration.

Data science is a rapidly growing field that offers students exciting career paths and advanced learning opportunities. The Data Science major provides students with a foundation of knowledge based on three areas: computer science, statistics and mathematics to analyze and process big and complex data. Students majoring in Data Science will be equipped with knowledge of computer programming, database management systems, machine learning models, statistical analysis, and computational methods in data science and data science. data interpretation method.

Below is the content of the training program:

General knowledge by field

1. Compulsory course

   • INM1000 Introduction to Informatics

2. Elective course

   • MAT1060 Introduction to Data Analysis
   • PHY1070 Introduction to Internet of Things
   • PHY1020 Introduction to Robotics

General knowledge of the industry

• PHY1100 Mechanics – Thermodymiacs
• PHY1103 Electromagnetism – Optics

General knowledge of the industry group

1. Compulsory courses

   • MAT2400 Linear Algebra
   • MAT2501 Calculus 1
   • MAT2502 Calculus 2
   • MAT2503 Calculus 3
   • MAT2403 Differential Equations
   • MAT2034 Numerical Analysis
   • MAT2323 Probability and Statistics
   • MAT2407 Optimization
   • MAT2315 Research Methodology
   • MAT2506 Soft skill

2. Elective courses

   • MAT2316 C/C++ Programming
   • MAT2317 Java Programming
   • MAT2318 Python programming
   • MAT2319 Julia programming

Industry knowledge

1. Compulsory courses

   • MAT3500 Discrete Mathematics
   • MAT3557 Linux Programming Environment
   • MAT3372 Software Components
   • MAT3514 Data Structures and Algorithms
   • MAT3507 Databases
   • MAT3378 Management of big and complex data
   • MAT3148 Prallel computing
   • MAT3379 Applied Regression Analysis
   • MAT3533 Machine learning
   • MAT3380 Seminar: Selected topics on Data Science
   • MAT3381 Project in Data Science

2. Elective courses

   1. Elective on software skills

      • MAT3382 Programming for Data Science
      • MAT3383 Information Visualization
      • MAT3384 Autonomous Robots

   2. Computer science electives

      • MAT3385 Web Database and Information Systems
      • MAT3504 Algorithms Design and Analysis
      • MAT3508 Introduction to Artificial Intelligence

   3. Statistics and Data Mining

      • MAT3534 Data mining
      • MAT3386 Computational Methods in Statistics and Data Science
      • MAT3387 Survey Sampling Techniques
      • MAT3388  Analysis of Time Series
      • MAT3389 Introduction to Design of Experiments

   4. Elective about Data Science applications

      • MAT3390 Introduction to Bioinformatics
      • MAT3391 Introduction to GIS
      • MAT3392 Big data in risk management of natural disasters
      • MAT3393 Data mining in Chemistry
      • MAT3394 Mathematical Ecology
      • MAT3562 Computer Vision
      • MAT3395 Game Theory
      • MAT3535 Information Retrieval
      • MAT3399 Natural Language Processing with Deep Learning

Internship and graduation knowledge block

• MAT4083 Graduation thesis

Alternative Courses Graduation Thesis

• MAT3397 Selected topics on data science application
• MAT3398 Topics in Modeling and Data Analysis