Menu
Master of Engineering in
Data Science
Program Description
The Master of Engineering of Data Science degree program uses real-world problems and situations to prepare graduates for roles as strategic thought leaders who leverage predictive modeling to drive decision making. Students will develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics. The aim of this degree is to engage students with the practice of problem analysis and decision-making with data literacy. Students will gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.
Why this Program?
- Upgrading your skills and expertise in data literacy to respond to the demand in the age of data era and digital transformation
- Contributions and applications to real-life problem scenario
- Scholarship and internship opportunities
Who should apply?
Are you...
- Passionate about data?
- A listener and thinker who would love to understand and solve business problem?
- Having technical skills for coding and analyzing data?
- Holding a bachelor’s degree in related fields of science, engineering, business related fields who want to upgrade their data literacy skills?
Then this program is for you.
Program Pathways
This program has 2 pathways:
Course-Research Pathway: this pathway is for students who are interested in pursuing career in professional job and academic job as researchers. The students in this pathway will only do the thesis only.
Research-based Pathway: this pathway is for students are interested in pursuing career only in the academic area as researchers. The students in this pathway will be required to produce publication and do the thesis.
Program Curriculum
Both Course-Research and Research-based pathway has total of 54 credits for graduation. However, each pathway has different curriculum organizations as described below.
Course-Research Pathway
| Coursework | 42 Credits |
|---|---|
|
Core Course |
12 Credits |
|
Specialized Course |
18 Credits |
|
Elective Course |
8 Credits |
|
Research Course |
4 Credits |
| Research Activities | 12 credits |
|---|---|
|
Thesis |
12 credits |
Research-based Pathway
| Coursework | 12 credits |
|---|---|
|
Specialized Course |
6 credits |
|
Research Course |
6 credits |
| Research Activities | 12 credits |
|---|---|
|
Primary Research Proposal and Defense |
9 credits |
|
Details Research Proposal and Defense |
3 credits |
|
Seminar |
3 credits |
|
Presentation in Scientific Conference |
3 credits |
|
Journal Publication |
6 credits |
|
Thesis |
18 credits |
List of Courses
| Core Course | |
|---|---|
|
Programming for Data Science |
3 Credits |
|
Introduction to Machine Learning |
3 Credits |
|
Exploratory Data Analysis and Unsupervised Learning |
2 Credits |
|
IT Project Management |
1.5 Credits |
|
Advanced Machine Learning |
3 credits |
| Specialized Course | |
|---|---|
|
Information Web Retrieval Analytics |
3 Credits |
|
Deep Learning |
3 Credits |
|
Introduction to Parallel and Distributed Computing (Cloud Computing) |
3 Credits |
|
Big Data Architecture |
2 credits |
|
Data Warehouse and Visualization |
2 credits |
|
Simulation Method and Bayesian Inference |
3 credits |
|
Entrepreneurship |
2 credits |
| Elective Courses | |
|---|---|
|
Natural Language Processing |
3 Credits |
|
Data Science Capstone Project |
3 Credits |
|
Data Security |
3 Credits |
|
Blockchain |
3 Credits |
|
Cryptography and Security |
2 Credits |
|
Software Engineering |
2 Credits |
|
Mathematics for Machine Learning |
3 credits |
|
Mathematical Statistics |
2 credits |
|
Discrete Mathematics |
2 credits |
|
Database |
2 credits |
| Research Oriented Courses | |
|---|---|
|
Research Methodology |
3 Credits |
|
Scientific Communication |
2 Credits |
|
Applied Statistics |
2 credits |
|
Mini Project |
2 Credits |
|
Graduate Seminar |
2 credits |
Study Program
- Course Research Pathway
| Year 1 | |||
|---|---|---|---|
|
Semester 1 |
Semester 2 |
||
|
Course/Activity |
Credit |
Course/Activity |
Credit |
|
IT Project Management |
1.5 |
Introduction to Parallel and Distributed Programming (Cloud Computing) |
3 |
|
Programming for Data Science |
3 |
Time Series Analysis and Forecasting |
3 |
|
Information Web Retrieval Analytics |
3 |
Natural Language Processing (Elective Course 1) |
3 |
|
Exploratory Data Analysis and Unsupervised Learning |
2 |
Blockchain (Elective Course 2) |
3 |
|
Introduction to Machine Learning |
3 |
|
|
|
Total |
12.5 |
Total |
12 |
| Year 2 | |||
|---|---|---|---|
|
Semester 1 |
Semester 2 |
||
|
Course/Activity |
Credit |
Course/Activity |
Credit |
|
Advanced Machine Learning |
3 |
Master Thesis and Defense |
12 |
|
Deep Learning |
3 |
|
|
|
Big Data Architecture |
2 |
|
|
|
Data Warehouse and Visualization |
2 |
|
|
|
Simulation Method & Bayesian inference |
3 |
|
|
|
Research Methodology |
2 |
|
|
|
Entrepreneurship |
2 |
|
|
|
Total |
17 |
Total |
12 |
- Research-based Pathway
| Year 1 | |||
|---|---|---|---|
|
Semester 1 |
Semester 2 |
||
|
Course/Activity |
Credit |
Course/Activity |
Credit |
|
Specialized Courses |
Min. 7 |
Primary Research Proposal and Defense |
9 |
|
Research Methodology |
2 |
|
|
|
Graduate Seminar 1 |
1 |
|
|
|
Total |
10 |
Total |
9 |
| Year 2 | |||
|---|---|---|---|
|
Semester 1 |
Semester 2 |
||
|
Course/Activity |
Credit |
Course/Activity |
Credit |
|
Scientific Communication |
2 |
Presentation in Scientific Conference |
3 |
|
Project Management for Researcher |
2 |
Journal Publication |
6 |
|
Detailed Research Proposal and Defense |
3 |
Master Thesis and Defense |
18 |
|
Graduate Seminar 2 |
1 |
|
|
|
Graduate Seminar 3 |
1 |
|
|
|
Total |
9 |
Total |
27 |
Career Opportunities
The graduates may pursue their career paths in both academics and professionals:
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning and AI Engineer
- Business Intelligent Analyst
- Database Administrator
- Big Data Specialist
- Statistician
Research Areas at M-DAS
| Applications of Data Science in | |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Lecturers
Dr. PHAUK Sokkhey
- Lecturer of Department of Applied Mathematics and Statistics (AMS), ITC
- Coordinator of Master Program of Data Science, ITC
Research Focus: Machine learning, Data Analytics, Data Mining, and Predictive Modelling
Dr. SIM Tepmony
- Director of Graduate School, ITC
Research Focus: Applied Mathematics, Statistics, Economics, and Machine learning.
Mr. CHAN Sophal
- Full time lecturer at Department of Applied Mathematics and Statistics (AMS), ITC
- Data Science and AI coach at SabaiCode
- Data Science Consultant at Z1 Data
Research Focus: Machine learning, Deep Learning, Computer Vision, Data Mining, and Artificial Intelligent
Mr. NHIM Malai
- Lecturer at Department of Applied Mathematics and Statistics (AMS), ITC
Research Focus: Machine learning, Statistics, Meta-analysis, and Biostatistics
Admission
Application is opened started from June until September.
An applicant must have the following qualifications:
v Baccalaureate or Equivalent Diploma
v Engineer’s Degree or bachelor’s degree in computer science, Information Technology, or related fields
v Certificate of English Proficiency
v Minimum GPA of 2.5 on 4.0 Scale
Download application form at: https://bit.ly/itcmaster2022-23
Tuition Fee
According to the Announcement of Graduate School of ITC 2022, the tuition fee is 1200USD per year or 650 USD per semester.
More Information
Graduate School of ITC:
Coordinator of the program:
- Dr. PHAUK Sokkhey
- phauk.sokkhey@itc.edu.kh
- +855 12 939 310
