Studying the Data Science Degree in Malaysia

In the past few years, there has been an enormous increase in demand for Data Scientists in Malaysia and globally, across almost every industry sector, from tech and finance to energy, retail, healthcare and cybersecurity.

As the volume, diversity and complexity of data being gathered continue to increase, the key challenge facing companies is how to make sense of data, and more importantly how to use data to inform business decisions.

To solve this problem, organisations need data scientists who not only are highly skilled in a wide range of statistical and data analysis tools, but who can go far beyond classical statistics and machine learning to gain real insights from data.

Data science is a combination of different disciplines such as data inference, algorithm development, and technology so that we can solve analytically complex problems. Huge amounts of raw information, streaming in and stored in enterprise data warehouses. We will need advanced systems and capabilities to sort out the data and analyse it to be used to create value for business.

Data scientists combine statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently to find patterns, along with the activities of cleansing, preparing, and aligning the data.

Basically there are 2 types of degree programmes at Malaysian private universities for students who want to pursue an undergraduate programme in Data Science – one is Maths-based and the other is computer science based. Students may go for a Statistics degree programme and specialise in the Maths part of the Data Science career. On the other hand, a computer science based data science programme will cover mostly the programming languages and computing subjects. In addition, students will also study some statistics and business analysis.

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Who Should Study Data Science in Malaysia?

Students who love Mathematics, computer science, research and analysis are excellent candidates to study Data Science.

A common personality trait of data scientists is they are deep thinkers with intense intellectual curiosity. Data science is all about being inquisitive – asking new questions, making new discoveries, and learning new things.

Ask data scientists most obsessed with their work what drives them in their job, and they will not say “money”. The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity.

Deriving complex reads from data is beyond just making an observation, it is about uncovering “truth” that lies hidden beneath the large amount of data.

Problem solving is not a task, but an intellectually-stimulating journey to a solution. Data scientists are passionate about what they do, and reap great satisfaction in taking on challenge.

Data Science skills

• Problem-solving
• Attention to details
• Software development
• Machine learning
• Proficiency in big data tools: Hadoop and Spark
• Programming abilities: Python, R, Scala
• Expertise in SQL, Cassandra, MongoDB
• Knowledge of visualisation tools: QlikView, Tableau

What is the Entry Requirements to Study Data Science?

Students after SPM or IGCSE/O-Levels may go for the Foundation in Computing & Technology for 1 year before continuing on to the 3-year undergraduate programme.

With 3 credits in SPM or IGCSE/O-Levels including Maths, students may go for the 2-year Diploma in Information & Communications Technology and then enter into Year 2 of the Data Science degreee.

Alternatively, students who want an affordable and yet top ranked computer science private university in Malaysia may choose to take the Foundation in Information Technology  (IT) or Diploma in Information Technology (IT).

Pre-University students with the relevant results in STPM, A-Levels, SACEi, CPU, AUSMAT, etc. can enter directly into Year 1 of the Data Science or Data Analytics degree programmes.

What will you study in Data Science at a private university in Malaysia?

Data science degrees provide students with the technical skills they need to analyze data and develop actionable conclusions from those assessments. The coursework of such programs typically emphasizes programming, statistics, math and some elements of social science.

Data Science focuses on designing and developing solutions to draw useful insights from the availability of large volumes of data, known as Big Data.

Students will receive fundamental training in computer science theories and learn techniques on the processing of Big Data for analytics that can be impactful to business.

In this major students will develop a strong foundation in the statistical aspects of data analysis (data collection, data mining, modelling and inference) and the principles of computer science (algorithms, data structures, data management and machine learning).

What are some of the Subjects in a Data Science Undergraduate Programme at a Private University in Malaysia?

• Discrete Mathematics
• Intermediate Statistics
• Database Systems
• Principles of Data Mining
• Data Security
• Data Structures and Algorithms
• Software Development
• Introduction to Management
• System Analysis & Design
• Fundamentals of Software Development
• Mathematical Concepts for Computing
• Operating Systems & Computer Architecture
• Introduction to Networking
• Introduction to Databases
• Introduction to C Programming
• Introduction to Data Analytics
• Behavioural Science and Marketing Analytics
• Computing Theory
• Data Structures
• Concurrent Programming
• System & Network Administration
• Computer Systems & Low Level Techniques
• Probability & Statistical Modelling
• Data Mining & Predictive Modelling
• Object Oriented Development with Java
• System Development Methods
• Professional & Enterprise Development
• Creativity & Innovation
• Research Methods for Computing & Technology
• Algorithmics
• Real-Time Systems
• Emergent Technology
• Text Analytics & Sentiment Analysis
• Business Intelligence Systems
• Database Security
• Optimisation Concepts for Data Science
• Investigation in Computer Science
• Computer Science Projects
• Calculus
• Programming Fundamentals
• Discrete Structures & Probability
• Professional Development
• Computational Methods
• Object Oriented Programming & Data Structures
• Computer Architecture & Organisations
• Database Fundamentals
• Research Methodology in Computer Science
• Software Engineering Fundamentals
• Operating Systems
• Computer Networks
• Object Oriented Analysis & Design
• Algorithm Design & Analysis
• Statistical Data Analysis
• Data Visualisation

Programming Language (Python / R)

Between the two, Python is the most preferred coding language and is adopted by most Data Scientists. It is easy to understand, versatile, and supports various in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many more.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.

Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Although Python can be used to build pretty much anything, it really shines when it comes to working on technologies like Artificial Intelligence, Machine Learning, Data Analytics. Python also proves to be useful for web development, creating enterprise applications, and GUIs for applications.

On the other hand, R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R offers a wide variety of statistics-related libraries and provides a favorable environment for statistical computing and design. In addition, the R programming language gets used by many quantitative analysts as a programming tool since it’s useful for data importing and cleaning.

Statistics

For becoming a Data Scientist, having knowledge of statistics and probability is essential. Having a good grasp of Maths will help data scientists interpret large data sets, get insights from them, and analyze them better.

SQL

Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing join operations, etc., on the data and extract in raw format. This data will then further be cleaned either in Microsoft Excel or by using Python libraries.

Data Cleaning

When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy. One should also know how to manipulate data using Microsoft Excel.

In Microsoft Excel, you should know basic data filtering or sorting, Functions or Formulas, Vlookup, Pivot table and charts, and Tables, etc.

Exploratory Data Analysis

Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from them with the help of various graphical and statistical methods, including:

1. Data Analysis using Pandas and Numpy
2. Data Manipulation
3. Data Visualization

Machine Learning Algorithms

According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning algorithms and should be able to predict and come with the most optimum solution to solve any problem.

What Qualifications do I need to work in Data Science in Malaysia?

Job-seekers for data science roles require baseline higher education, holding a Bachelor’s Degree at a minimum. Employers however value short courses and MOOCs in resumes as they reflect active lifelong learning and commitment. Graduates may opt for the Masters programme.

Earning a master’s in data science can help you gain a broad skill set that can be applied to a vast number of tech-related careers, such as data engineering, data architecture, or computer programming. It helps you in acquiring skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly.

While a PhD is not a prerequisite to becoming a data scientist, advanced education is valued. Soft skills such as critical and creative thinking are sought after. Finding an individual that is strong in all the competencies for a data scientist is very rare: the formation of teams with complementary skill sets can address this challenge. Ultimately, employers desire team members who will add value to the ‘bottom line’ of a business through delivering actionable insights.