Apply To Data Analytics Courses
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Table of Contents
1. What is Data Analytics?
In simple terms, Data Analytics involves collecting, organizing, and examining information to identify patterns, answer questions, and make predictions. It’s an interdisciplinary field that uses skills from Mathematics, Coding, and Machine Learning to organize raw data and spot trends.
While less advanced than Data Science, a Data Analytics course teaches analysis techniques and the use of tools like Excel, SQL, Python, or R.
Why It Matters To The World?
- Instead of relying on guesswork, businesses and individuals can make decisions based on evidence from relevant data. It also speeds up decision-making by offering quick access to information.
- You can also be part of the bigger picture, influencing public policy decisions, managing natural resources, and improving health care.
- Data Analytics courses help businesses identify issues with their current systems and fix them for better profitability in the long run.
What does a Data Analyst do? Here’s a video for you. Here’s another video on skills you need to pick up.
2. Data Analytics vs Data Science?
While some people might confuse the two, it’s worth noting that Data Analytics is not the same as Data Science. Instead, the latter is a broader field that includes Data Analytics too, requiring high technical expertise. Here’s a breakdown of key differences between the two fields:
| DATA ANALYTICS | DATA SCIENCE |
| Recommended Education | |
| Bachelor’s Degree | Master’s or PhD |
| Tools & Techniques | |
| SQL, Excel, or Data Visualization Software | Machine Learning Frameworks, Deep Learning, Python, and R. |
| Scope | |
| Mainly focusing on existing data to build models and identify trends to make projection scenarios. | Broader scope focusing on developing intelligent algorithms that are predictive with existing and real-data insights. |
| Necessary Skillset | |
| Data Manipulation Data Cleaning Visualization Skills | Statistical Modeling Machine Learning Programming |
3. Where To Study Data Analytics Courses In Malaysia?
Here are our recommendations of top universities to study Business Analytics or Data Analytics courses in Malaysia.
| University | Course |
| 1. Asia Pacific University (APU) | BSC (HONS) IN COMPUTER SCIENCE WITH A SPECIALISM IN DATA ANALYTICS |
| 2. HELP University | Bachelor of Business Analytics (Honours) Bachelor of Information Technology (Hons) Data Analytics |
| 4. University of Southampton Malaysia | Bachelor of Science Business Analytics (3+0) with UoSM, UK |
| 3. TARUMT | Bachelor of Business (Honours) in Business Analytics |
| 6. PSB Academy | Diploma in Business – Analytics |
4. Data Analytics Course Pathways & Entry Requirements
There are 2 pathways one can consider to qualify for a career in the Data Analytics field.
Pathway 1: SPM/O-Levels ➔ Pre-University/Foundation ➔ Data Analytics Degree
The more common pathway is to enter a Pre-University course (A-Level, SACE, STPM) or a Foundation course offered by the university of your choice.
As an SPM / O-Level leaver, the entry requirement is 5 credits, including Mathematics or Additional Mathematics, to enter Pre-U or Foundation.
A Pre-University course or Foundation in Computing typically takes 1 to 2 years to complete and will qualify you to enter a Degree in Data Analytics.
A Data Analytics Degree will usually take 3 years to complete. Here are the general requirements to enter with qualifications from popular pre-university courses or a foundation course:
| Qualifications | General Requirements |
| A-Level | 2D |
| AUSMAT/SACE | ATAR 65 |
| CIMP | 65% |
| STPM | 2C |
| UEC | 5B |
| Foundation | CGPA 2.0 |
Note: Actual minimum requirements will differ between different universities. Generally, these qualifications also require you to have a Mathematics or an Additional Mathematics subject taken and achieve the minimum results.
In total, this pathway takes 4 years to complete.
Pathway 2: SPM/O-Levels ➔ Diploma ➔ Data Analytics Degree
Another possible pathway is to secure a Diploma qualification before pursuing a Degree.
Data Analytics is an intersection between Data Science and Business Statistics and Analytics. It is generally not as deep as Data Science but still requires technical skills to dissect raw data to make sense of business trends and extrapolate possible outcomes.
Since a Data Analytics Degree still requires a basic level of mathematical, statistical, and programming skills, it is recommended that you take a Diploma in IT or a Diploma in Computer Science to gain a solid foundation first. In Malaysia, there is no Diploma focusing specifically on Data Analytics.
As an SPM / O-Level leaver, the entry requirement is 3 credits, including Mathematics or Additional Mathematics, to enter a Diploma.
A Diploma in IT or equivalent normally requires 2 years to complete and a minimum CGPA of 2.0 to enter a Degree.
How Can Uni Enrol Help?
Uni Enrol’s experienced counsellors help you compare the pros and cons of studying a Data Analytics Course at different universities and different locations.
5. What You Learn In A Data Analytics Course
When taking your undergraduate Data Analytics Course, here are some common subjects you’ll see.
- Algorithm Design & Analysis
- Algorithms & Data Structures
- Business Analytics
- Business Communication
- Computational Methods
- Data Management
- Data Mining and Predictive Modelling
- Data Visualization & Analytics
- Data Warehouse Technology
- Database Management
- Digital Marketing
- Digital Thinking and Innovation
- Discrete Mathematics
- Fundamentals of Entrepreneurship
- Fundamentals of Management
- Human Computer Interaction
- Information Security Management
- Introduction to Artificial Intelligence
- Introduction to Data Science
- Introduction to Research Methods
- Investigations in Data Analytics
- Object Oriented Analysis & Design
- Professional Development
- Project in Data Analytics
- Statistical Modelling and Analysis
- Sustainable Society
- Systems Analysis and Design
- Text Analytics and Sentiment Analysis
- User Experience (UX), User Interface (UI)
6. Why Should I Study Data Analytics?
Almost all industries today heavily rely on data-driven decisions, and pursuing a Data Analytics course equips you with the right skills to make it possible. Here’s a more detailed view of why you should consider this career path:
- Diverse Job Roles – A Data Analytics course doesn’t limit you to one industry or a few job titles. Compared to other jobs, it lets you explore your areas of interest and skills. For example, it equips you with skills required for roles like business intelligence analyst, marketing analyst, and more.
- Tackle Real-world Business Problems – Unlike many theoretical fields, Data Analytics is immediately applicable in real-world business scenarios. It helps you think critically to identify issues and solve them with the power of data.
- Future-Resilient Career Pathway – Technological advancements are making many jobs obsolete, but a career in Data Analysis is highly relevant now and in the future. It is a field that leverages on artificial intelligence to produce enhanced analytics results.
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RIASEC Compatibility To A Data Analytics Course
The John Holland Theory of Career Choice states that in choosing a career, people will choose one similar to their personality to ensure job satisfaction, job performance, and career success. The test generates 6 different personality types:
- Realistic (R)
- Investigative (I)
- Artistic (A)
- Social (S)
- Enterprising (E)
- Conventional (C)
With the RIASEC Test, the 6 personalities can generate up to 720 combination possibilities to provide career compatibility. You can try the test yourself here.
When considering a Data Analytics course, here are the Top 3 personality traits you should ideally have:
INVESTIGATIVE
- Data Analysis requires strong investigative skills and curiosity to ask questions, find patterns, and draw logical conclusions. If this seems to be your dominant trait, it’s worth pursuing a Data Analytics course.
REALISTIC
- More than theory, data analysis deals with hands-on and practical material. It takes a realistic mind to deal with complex tools like SQL, Python, and Excel and manipulate data. Strong scores on this trait make you eligible to handle data management easily.
CONVENTIONAL
- As a data analyst, you’ll have to work with large datasets. With a conventional personality, you can follow structured workflows, organize information, and work with numbers efficiently to excel in the field.
7. Career Opportunities For Data Analytics
Fortunately, there are various career paths to consider with a Data Analytics Course. To save you the legwork, we’ve put together a list of possible career paths to choose from:
| Areas of Opportunity | Types of Roles |
| Core Data Analytics Roles | Data Analyst, Business Intelligence (BI) Analytics, Data Scientist , Quantitative Analyst (Quant) |
| Industry-Specific Roles | Marketing Analyst, Healthcare Data Analyst, Financial Analyst, Retail & E-commerce Analyst |
| Advanced Data & Tech Roles | Data Engineer, Machine Learning Engineer, Big Data Engineer, AI/Deep Learning Specialist |
| Managerial & Consulting Roles | Analytics Consultant, Product Analyst/Manager, Data Governance & Compliance Specialist |
Recommended Course Guides
Looking for other related courses? Here are various business-related course guides you should check out:
If you’re still unsure about what to study, we have many other Course Guides that will provide further insight into the subject matter. Share with your friends!
We also took the liberty to compile all our University Guides and Course Fees Guides so you don’t have to go anywhere else!
About the Author:
Ken Hoong is the co-founder of Uni Enrol and a contributing writer. Drawing on his close work with Uni Enrol’s counsellors, he brings keen insight into Malaysia’s evolving private higher education landscape and the shifting preferences of students in learning and career choices.
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