Data science major in Malaysia
It offers exciting opportunities for creativity and innovation. If you excel at mathematics, pay attention to detail, and have a passion for solving difficult problems using technology, then you are on the right path to this exciting field!
In this specialty
You will combine data mining, algorithm development, and statistical analysis skills to strategically understand and analyze big information. With the vast amounts of data stored daily in data warehouses, you will develop systems and tools that enable organizations to effectively use this data to achieve their operational goals and increase its value.
By using deep analysis techniques, you can develop accurate predictive models that help companies understand customer behavior and guide business strategies more effectively. For example, businesses can use data to improve the online shopping experience through personalized product recommendations and precise ad targeting.
University admission and admission requirements
Entry requirements vary by university, but usually the criteria for a bachelor's degree are:
- High school certificate with a minimum grade of 65% or a preparatory year with 2.5 out of 4 as the lowest grade
- To study data science in Malaysia, you may need to take the IELTS (International English Language Testing System) test to prove your English proficiency. Specific requirements may vary depending on the institution and program you are applying to, so it is important to check with the university or college directly to confirm their English language requirements.
- Most institutions in Malaysia require a minimum general band score 5.5 or 6.0 In the IELTS exam for undergraduate and postgraduate programmes, respectively. However, some institutions may have higher requirements, so it is important to check with each institution to confirm their specific requirements.
- Some institutions may also have minimum score requirements for each individual component of the exam, such as the Listening, Reading, Writing and Speaking sections.
In general, it is important to prepare thoroughly for the IELTS exam to achieve the required scores, which may include practicing sample test questions, taking preparation courses, and working with a teacher or language coach to improve your English skills.
Duration of study to study specialization in Malaysia:
Usually, a bachelor's degree takes 3 years. While the duration of study for a master's degree is from one to two years.
The most important Arab and international universities for studying data science specialization
International universities:
- Massachusetts Institute of Technology (MIT) – United States
- Stanford University - United States
- University of California, Berkeley – United States
- Harvard University - United States
- ETH Zurich – Switzerland
Arab universities:
- King Abdullah University of Science and Technology (KAUST) – Kingdom of Saudi Arabia
- King Abdulaziz University (King Abdulaziz University) – Saudi Arabia
- American University of Beirut (American University of Beirut) - Lebanon
- Qatar University (Qatar University) – Qatar
- University of Jordan (University of Jordan) – Jordan
Programs and fees at universities
University Name | Class | Academic duration | Annual tuition fees | more details |
APU Asia Pacific University | BSc (Hons) in Computer Science with a specialism in Data Analytics | 3 years | $7,350 | more details |
Taylor University | Bachelor of Computer Science (Hons) in Data Science / BACHELOR OF COMPUTER SCIENCE (HONOURS) DATA SCIENCE | 8 years | $8,820 | more details |
UCSI University | Bachelor of Computer Science in Data Science with Honors | 3 years | $5,805 | more details |
Sunway Sunway University | Bachelor of Information Systems (Hons) (Data Analytics) / Bachelor of Information Systems (Honours) (Data Analytics) | 3 years | $8,443 | more details |
Universiti Sains Malaysia USM | Master of Science (Data Science and Analytics) / Master of Science (Data Science and Analytics) | 1.5 years | $2,970 | more details |
Monash Monash University | Bachelor of Computer Science | 3 years | $10,364 | more details |
Skills you need to excel in data science
To become a successful data scientist, you need a combination of technical and soft skills that enable you to effectively analyze data and make strategic decisions based on the conclusions you extract. Here are some basic skills you need:
- Analytical skills: Your ability to analyze data and extract important patterns and trends from it.
- Programming skills: There are many programming languages used in data analysis, such as Python, R, and SQL. You must be able to program well to parse, clean and transform data.
- Deep understanding of statistics and mathematics: Data analysis requires a good understanding of advanced statistical and mathematical concepts such as probability, statistical inference, and linear algebra.
- Skills in databases and data storage: Must be able to use databases to store and retrieve data effectively.
- Ability to communicate and present data: Must be able to effectively communicate findings and recommendations to assist in decision making.
- Curiosity and desire to learn: You must be curious and willing to constantly learn and keep up with developments in the field of data science.
- Good time management: Data analysis can be time consuming and requires good work organization and prioritization.
- Ability to work in a team: Data work usually involves collaborating with multiple teams, so you must be able to work effectively within a team.
Responsibilities and tasks of a data scientist
A data scientist's responsibilities vary depending on the company, the project, and the exact role the data scientist plays. However, here is a general list of some common responsibilities:
- Data collection: Collect data from various sources such as databases, websites, log files, etc.
- Data cleaning: Cleaning and analyzing data to ensure its accuracy and completeness, and to deal with missing values and errors.
- Data Analysis: Using statistical and programming tools and techniques to analyze data and extract patterns, trends, and effective analyses.
- Building prediction models: Developing and training machine learning and artificial intelligence models to predict future behaviors or trends based on available data.
- Developing tools and techniques: Developing new tools and techniques or improving existing tools to analyze data more effectively and effectively.
- Decision guidance: Providing reports and recommendations based on graphical analyzes to help leaders and decision makers make strategic decisions.
- Communication and interaction: Communicate with work teams and contribute to discussions about data, analyses, and recommendations.
- Ensure compliance and security: Ensure that data collection and analysis are conducted in accordance with ethical standards and strict privacy and security laws.
- Continuous Learning: Follow developments in the field of data science and acquire new skills and techniques to stay ahead in the field.