Quick facts about data analytics careers
Data analysts (Data Scientists) are specialists in Big Data, applied mathematics and computer programming with Machine Learning and Deep Learning, working in commerce, healthcare, manufacturing, finance and more. They apply models, algorithms (modeling methods) and charts to manage, mine, analyse and forecast, and to make important decisions for the goal under consideration based on collected data — thereby evaluating work already done and setting strategies and directions for future development.

Job growth: the projected job growth for market research analysts (another term for data analysts) in the period 2014–2024 is 19%, based on data from the US Bureau of Labor Statistics. That is a significant number of newly created positions.
- Salary: data analysts are well paid even if they do not continue into data science or engineering! How much do data analysts earn? According to Payscale, data analysts receive an annual salary of $60,000 – $180,000 (an average of US$131,389 according to 2019 figures from indeed.com).
- Data Scientist ranked #4 in Indeed’s Best Jobs in the U.S. Average Base Salary, 2019.
- Competitive advantage: this is a field where supply is low and demand is high. It is a very HOT field today, and you can work in any area such as Healthcare, Finance, Business, Banking, Marketing, Government, Fast Food, Retail IT, Industry, etc. Your foundation in Data Analytics with Machine Learning and Deep Learning is a strong competitive advantage that brings you a high income, a work environment with global interaction, and a solid base for doing well in AI-related jobs in the future.
- Data Analyst ranked #38 in Glassdoor’s 50 Best Jobs in America, 2018

Student benefits
This program is for third-year students and above, bachelors, lecturers, engineers and others from different fields who want to learn in order to work, research and apply Data Analytics in their jobs, and who are looking for the very “hot” job opportunities related to Data Analytics with Big Data, Data Mining, Machine Learning and Deep Learning at any company in the world.
- Students are fully equipped with the latest Data Analytics knowledge.
- Students are taught entirely in English in an academic environment that follows US university education standards.
- Students are taught by leading professors and lecturers in Data Analytics.
- Students undertake internships at companies and technology firms in Vietnam or abroad.
- Students take part in events, workshops, conferences, forums and Data Analytics competitions, and are mentored by professors at US universities and by specialists and engineers working at companies in the United States such as Microsoft, Amazon, Google, Apple, TSC, etc.
- Students are taught interview skills to prepare for meeting recruiters, and are introduced to recruiting companies.
- Students are able to join real projects and online projects, or contribute and collaborate with Data Scientists, Big Data, Machine Learning, Deep Learning and AI Engineers around the world.
- After completing the program, students can apply their skills and work right away at any technology company recruiting Data Scientists, Data Analytics specialists, Data Engineers, Big Data specialists, Data Analysts, Data Mining specialists, and Machine Learning and Deep Learning Engineers.
- A high salary of $1,000 to $3,000 per month awaits you.
Minimum admission requirements
Third-year students and above, bachelors, lecturers, engineers and others in majors such as Economics, Foreign Trade, Finance, Natural Sciences, Informatics, IT, etc.
English level B or higher obtained no more than two years before the application date, or good practical listening, speaking, reading and writing skills and a pass in a direct interview at the institute.
Priority is given to students with English test scores obtained no more than two years before the application date, meeting these standards:
- UK IELTS 5.0 or higher in all four skills: listening, speaking, reading and writing.
- US-standard TOEFL iBT score of 60 or higher.
- TOEFL ITP (for graduate students of Vietnamese universities, issued by IIG Vietnam) of 500 or higher.
Priority is given to students with US graduate and doctoral entrance scores, such as a GMAT score of 600 or higher, or a GRE score of 150 or higher in the verbal and quantitative sections.
Computer literate, with knowledge of at least one programming language.
Priority is given to students who have won prizes in Olympiads in Natural Sciences and Informatics.
Courses in the program
Total: 36 graduate credits.
The program has 4 semesters; each semester has 3 courses; each course is worth 3 graduate credits.
Planned start date: 5/2020
Classes are held in the evening on Tuesdays and Thursdays from 18:00 to 21:00.
SEMESTER 1
9 graduate credits — core curriculum
| Course # | Course title | Credits | Software | Timeframe |
|---|---|---|---|---|
| STAT 500 | Applied Statistics for Data Analytics | 3 | Calculators | 8 x 3 hours |
| DATA-ML 881 | Data-Driven Decision Making – Machine Learning | 3 | R & Rattle language | 16 x 3 hours |
| DATA-BA 521 | Database Design Concepts | 3 | Oracle SQL | 8 x 3 hours |
SEMESTER 2
9 graduate credits — required courses
| Course # | Course title | Credits | Software | Timeframe |
|---|---|---|---|---|
| DATA-PA 575 | Foundations of Predictive Analytics | 3 | IBM SPSS- Modeler 18 | 16 x 3 hours |
| DATA-MN 545 | Data Mining | 3 | KNIME, IBM-Modeler 18 | 8 x 3 hours |
| DATA-WA 825 | Large Database and Warehouse | 3 | Hadoop, Apache Hive, Pig, Hbase, KNIME | 16 x 3 hours |
SEMESTER 3
9 graduate credits — base courses
| Course # | Course title | Credits | Software | Timeframe |
|---|---|---|---|---|
| DATA-DV 871 | Data Visualization | 3 | Tableau | 8 x 3 hours |
| DATA-CC 822 | Data Collection and Cleaning | 3 | R & Rattle | 8 x 3 hours |
| DATA-DL 897 | Deep Learning | 3 | Python, RStudio | 8 x 3 hours |
SEMESTER 4
3 graduate credits — research requirement
| Course # | Course title | Credits | Software | Timeframe |
|---|---|---|---|---|
| RI-596 | Research Independent and practice at companies | 3 | Any software | 8 x 3 hours |
6 graduate credits — master thesis
| Course # | Course title | Credits | Software | Timeframe |
|---|---|---|---|---|
| MASTER 888 | Design and Implementation of Analytics Systems | 6 | Docker, Spark, Scala, Rstudio | 8 x 3 hours and take home |
