UH answer

Please state the total number of credits included in your degree. Also state the number of credits required for this type of degree at your institution. If your institution does not use a credit system, please use the system of your institution (for example study hours). Please describe briefly the system used in your institution (for example 1 credit = 30 hours of study) (150 characters)

Total credit: 144
In Vietnam, a credit = 42.5 hours of study
Total hours: 144 * 42.5 = 6120 hours


Please state the amount of computer science and data science studies in your previous degree (a numerical value, a sum of the course credits/sizes) (150 characters)

80 credits (including Graduation Thesis (10 credits))
3400 hours


Please state the amount of mathematics and statistics studies in your previous degree (numerical value, sum of the course credits/sizes) (150 characters)

27 credits (including 2 Physics courses: Mechanical and Thermal Physics and Electrical & Optical Physics)
1147.5 hours


Category 1, programming skills and languages: (600 characters)
Please list all programming languages you have studied, and for each language name one or two courses that covered that particular language. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

1. C: Introduction to Informatics 4
 Learn the basic concept of programming in C and how to implement simple data structures and algorithms

2. C++: Advanced Programming
 Learn the basic concept of Object-oriented programming in C++ and advanced programming techniques

3. Java: Object-Oriented Programming
 Understand advanced concepts of Object-oriented programming such as inheritance and polymorphism and the general process of software engineering

4. Python: Machine Learning, Artificial Intelligence
 Use Python and various libraries (numpy, scipy) to solve Data Science problems


Category 2, data structures and algorithms: (600 characters)
Please list at most two most advanced or relevant courses. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

1. Data Structures and Algorithms
 - Learn the theory and implement data structures (linked list, stack, queue, tree and heap)
 - Describe and implement algorithms (sorting, searching)
 - Learn Graph Theory (find the shortest path)
 - Apply data structures and algorithms to practical problem in real life


Category 3, statistics and probability theory: (600 characters)
Please list at most four most advanced or relevant courses. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

1. Information Theory
 - Learn the basic concept of Information Theory (entropy, mutual information)
 - Understand Shannon's model of communication
 - Understand the influence of Information Theory to Statistics, Economics or Machine Learning

2. Probability and Statistics
 - Understand the concept of probability (randomness, discrete and continuous probability distribution, Bayes theorem)
 - Learn statistics theory (hypotheses, interval estimation, significance)


Category 4, mathematics for data science: (600 characters)
Please list at most four most advanced courses in linear algebra, differential calculus and integral calculus. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

1. Algebra
 - Learn the concept of linear algebra (sets and maps, groups, fields and rings)
 - Learn the theory of matrices and linear equations

2. Calculus 1
 - Understand the theory of functions and sequences, derivatives and differential equations
 - Learn about series in general and Fourier series

3. Calculus 2
 - Expand the theory of functions (multivariable functions, derivatives of multivariable functions)
 - Learn about integrals (line integral, surface integerals, contour integrals)


Category 5, other courses in computer science: (600 characters)
Please list at most four most advanced or relevant courses, primarly on computer organization and architecture, operating systems, and databases. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

1. Computer Network
 - Understand the operation of a computer network in general
 - Learn the Internet architecture and OSI model
 - Learn basic network applications (Web, FTP, DNS) and popular protocols

2. Database
 - Learn the theory of building a database (architecture, model and optimization)
 - Study SQL language and learn how to use MySQL and PostgreSQL
 - Learn about database normalization and normal forms

3. Principles of Operating System
 - Learn about the roles and tasks of an operating system
 - Understand the theory of caching, deadlock, memory management


Category 6, other courses in statistics and mathematics: (600 characters)
Please list at most four most advanced or relevant courses. Describe each course’s main content. A good description identifies 3-5 main concepts (or themes) from the course and the most important things you learned about them.

Discrete Mathematics
 - Study logic, set theory, combinatorics and number theory
 - Study graph theory, boolean algebra and game theory
 - Solve basic probability problems


Category 7, application areas: (600 characters)
If you have significant studies in other subjects that are relevant for your data science studies, e.g., in areas where you would like to apply data science (e.g. life sciences, natural sciences, humanities, business), then please list here the subjects you studied, the number of credits and a very brief description of the contents of your studies.

Data Mining
 - Understand the process of a Data Mining problem: data pre-processing, data mining and validation
 - Learn data clustering and classification methods: Naive Bayes, Neural Network, SVM, K-Means, HAC, DIANA
 - Solve real-life problems: recommendation systems


Scientific writing:
If your degree contains a thesis, explain briefly your thesis writing process and the required structure of the thesis. You should be able to explain the thesis process and requirements even, if you are writing your thesis during spring 2019. If your degree does not contain a thesis, explain the largest scientific writing task included in your degree (700 characters)

Thesis: Real-time Face Recognition in surveillance videos

Structure:
- Introduction to the face recognition problem
- Background theory of face recognition (including state-of-the-art methods and face encoding methods)
- My contribution to the problem (propose a face recognition system, build data processing tools for raw data from videos and the data collection process)
- Experiments and results on the dataset of videos of over 700 people in 7 days
- Conclusion and future work

The accuracy for the framework is over 93% with the speed of 0.119 seconds on a normal computer.