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Published by Afterschool.my on Jan 24, 2023, 10:12 pm
Forget Medicine, Accounting, or Engineering…if you want a job that will be high in demand (and high pay) for the upcoming years, then you need to start shifting your focus!
According to the World Economic Forum (WEF), 8 out of 10 most demanding jobs are from the Information Technology sector, with Data Analyst and Data Scientist sitting at the top spot for the most in-demands job by 2025.
Not really that surprising, right?
As technology trickles into the broader economy, demands for IT-related jobs will only grow bigger and bigger over time.
Still on the fence about this profession? Read on to find out more about the thriving field of data analytics, if you are interested to study data analytics in Malaysia.
Data Analytics is the process of collecting, analysing, and interpreting large data sets. This pile of information can be used to make better decisions and predictions about different aspects of a business.
Think of solving mysteries. Just as a detective uses clues and evidence to solve a crime, data analysts use data and information to uncover insights and solve problems.
Chua Rou Lin, who is currently in her final year of studying Bachelor (Hons) in Computer Science with Specialism in Data Analytics at APU says, “Basically, data analytics is the process of finding mass data. From analysing those data, we can predict future trends and identify the customer demands for businesses to perform more effectively and maximise profit.”
You might not realise that whatever you are navigating on the internet right now, could be derived from Data Analytics. Let us assume, you are looking for a specific item on Google. After a while, you will realise that there will be Ads related to those items when you open other browsers or e-commerce platforms. This is an example of what data analytics can do for businesses. In marketing, Big Data is used to identify patterns and trends in user behaviour. Then from those insights, they can use the information to optimise marketing campaigns, create targeted advertising and improve overall customer experience.
Though this is a common area, data analytics is not only used for marketing. The range is extensive.
Data analytics is a powerful tool that can be used across businesses, in many different industries. The ability to collect, analyse and interpret large sets of data allows organisations to make data-driven decisions and gain valuable insights into their operations.
A major area besides marketing is where data analytics is used in finance. Banks and other financial institutions use data analytics to track and analyse market trends, assess credit risks, and detect fraudulent activities.
Aside from that, data analytics is also used in healthcare to improve patient outcomes and optimise healthcare operations. Hospitals use analytics to track patient data, such as medical history and treatment outcomes, to improve patient care and identify potential health risks.
Data analytics is also used in manufacturing and logistics to optimise operations and improve efficiency. Companies use analytics to track production data, in order to identify areas for improvement and to optimise production schedules. Logistics companies use analytics to track shipping and delivery data, optimise routes and reduce costs.
Many other fields use data analytics, such as Human resources, Supply Chain, Energy and Utilities, Government, and more.
From this, we can see that data analytics is a wide-ranging tool that many businesses deemed as essential nowadays.
The skills and concepts that students gain while studying data analytics will help them get ready for a career in this industry.
One of the key concepts that students will learn is the data analysis process, which involves collecting and cleaning data, exploring and visualising data, and modelling and interpreting data.
Another important area of study for students of data analytics is data visualisation. Students will learn how to create effective visualisations to communicate data insights and findings to others. They will gain an understanding of various data visualisations, including bar charts, scatter plots, and heat maps, and how to use them to effectively communicate data insights.
In addition, students will learn about data mining, which is the process of discovering patterns and relationships in large datasets. This can be used to predict future trends and make data-driven decisions. They will also learn about machine learning which is a subset of artificial intelligence and how it is used to analyse data, identify patterns and make predictions.
Chua Rou Lin explains, “In Machine learning, you are required to analyse previous raw data, then from that data, we can identify the data patterns to create predictions from predictive models in the future.”
When you’re studying this course, having basic coding and programming skills is also essential; but you’re not required to dive too deep on an advanced level.
“As a data analyst, you still need to have coding knowledge to create data. Having basic knowledge about programming language, and understanding concepts like logic and function is already sufficient for you as a data analyst.”
So far, among the programming languages she’s been exposed to, Chua Rou Lin says that Python is her preferred language. “I like to use Python because it is a more object-oriented language, and it also supports various libraries for data visualisation, data manipulation, and also data modelling.”
Data security and privacy is another critical component of data analytics. Students need to learn about the importance of data security and how to ensure the protection of sensitive data. They will also be taught how to safeguard sensitive information and the ethical and legal ramifications of dealing with data.
To Chua Rou Lin, her experience in learning data analytics has been nothing short of inspiring.
“I feel that every time I solve a problem, or I find a solution, it gives me a sense of gratification which further motivates me to grow in this field.”
The updated learning modules provided by APU also largely contribute to her understanding of the scope of studies. Reflecting on her internship experience recently, she had an epiphany that everything she learned could be easily applied in real-life situations.
Chua Rou Lin also discussed her use of data visualisation tools. During her previous semester, she had to conduct a case study from the raw data provided, where a scenario is given. Through that scenario, she needs to understand the perspective of certain predictions and trends that will be derived from the data. In this project, she uses Power BI as a data visualisation tool.
“Power BI is quite similar to Tableau. The visualisation tool is quite easy to use, you just need to deploy the data into Power BI, then you can perform very simple steps to visualise what you want.”
The SAP 4th Generation IR 4.0 Lab at APU functions as both a workstation and a studio for data analytics, with complete software and hardware materials that serve the purpose of data science and data analytics studies.
Aside from that, APU has a Joint Certification as part of the qualifications of the programme. They have partnered with SAS institute of USA and TIBCO USA in providing professional certification for any students that qualify for the programme, either for undergraduate or postgraduate level.
To sum it up, students who major in data analytics will develop a broad range of skills and knowledge that will be beneficial for them in many different fields. With their newly acquired skills, graduates will be well-equipped to work in a variety of sectors, such as banking, healthcare, and technology.
What industries are currently using data analytics the most?
Based on our research, the top industries currently hiring data analytics professionals includes:-
1) Banking and Securities
2) Media & Entertainment
3) Pharma & Healthcare
5) Government and Public sector services
What kind of job titles and roles are in high demand for data analytics professionals?
There are a wide variety of job titles and roles that are in high demand for data analytics professionals. Some of the most common roles include:
1) Data Analyst: This role involves collecting, cleaning, and analyzing large sets of data to identify patterns, trends, and insights that can inform business decisions.
2) Business Intelligence (BI) Analyst: A BI Analyst is responsible for creating and maintaining dashboards, reports and other visualizations that help businesses track key performance indicators (KPIs) and make data-driven decisions.
3) Data Scientist: A data scientist is a professional who is responsible for using advanced analytical techniques, statistical models and machine learning algorithms to extract insights from data.
4) Machine Learning Engineer: This role involves designing and implementing machine learning models, and optimizing them for production.
5) Big Data Engineer: This role is responsible for designing and maintaining the infrastructure and architecture required to store, process, and analyze large amounts of data.
6) Data Engineer: This role is responsible for designing and implementing the infrastructure and systems that allow organizations to store, process, and analyze data.
7) Data Governance Analyst: This role is responsible for ensuring that data is accurate, consistent, and compliant with laws and regulations.
8) Data Visualization Analyst: This role is responsible for creating interactive visualizations and dashboards to help businesses explore and understand their data.
These are some of the most common professional titles and roles for data analyst jobs, but the field is constantly evolving so there could be other new roles that emerge in the future.
How much can someone expect to earn in a career in data analytics?
The earning potential for a career in data analytics in Malaysia can vary widely depending on factors such as job title, experience, location, and industry. However, on average, data analytics professionals in Malaysia can expect to earn a relatively high salary.
Data analysts salary in Malaysia is on average MYR 48,000 per year. Data scientists and machine learning engineers can earn an average salary of MYR 80,000 to MYR 120,000 per year.
As with any profession, salaries can vary depending on experience, location, and industry. For example, data analytics professionals working in the technology or finance industry tend to earn higher salaries than those working in other industries. A data analyst in a small town might earn less than a data analyst in a big city. Also, the salary in a start-up company will be different than a big company.
What are the key skills and qualifications needed to succeed in a career in data analytics?
To succeed in a career in data analytics, there are certain key skills and qualifications that are needed. Some of the most important include:
Strong analytical skills: The ability to collect, organize, and analyze large sets of complex data is crucial for success in data analytics.
Technical skills: Proficiency in programming languages such as Python, R, and SQL is essential for data analytics, as is the ability to use data visualization tools such as Tableau, Power BI, and Excel.
Familiarity with statistical methods and machine learning: Understanding statistical concepts and having experience with machine learning algorithms is important for making sense of large data sets and uncovering insights.
Business acumen: Data analytics professionals need to be able to understand and communicate the business implications of their findings in order to be effective.
Strong communication and presentation skills: Data analytics professionals need to be able to explain their findings and recommendations to non-technical stakeholders in a clear and concise way.
Flexibility and adaptability: The field of data analytics is constantly evolving, so being able to adapt to new technologies and methodologies is essential for success.
It's also worth noting that, depending on the specific role, certifications such as CCNA, Cisco Cybersecurity Operations, and Microsoft Professional Program in Data Science can be beneficial.
How do I get started in a career in data analytics?
Getting started in a career in data analytics can involve a few different steps:
Develop a strong foundation in math and statistics: Understanding basic mathematical concepts and statistical methods is essential for success in data analytics. If you haven't already, consider taking classes in calculus, linear algebra, and statistics.
Learn the basics of programming: Familiarity with at least one programming language, such as Python or R, is essential for data analytics. You can start by learning the basics of programming through online tutorials or classes.
Learn data visualization and data manipulation tools: Tools such as Tableau, Power BI and Excel are important for data analytics. You can start learning by using them to create simple visualizations of data.
Get hands-on experience: There are many ways to gain hands-on experience in data analytics, such as participating in data analytics competitions, working on personal projects, or interning at a company.
Start networking and building your professional network: Attend networking events, join professional organizations, and connect with other data analytics professionals.
Consider obtaining a certification: Obtaining a certification such as CCNA, Cisco Cybersecurity Operations, and Microsoft Professional Program in Data Science can be beneficial to show potential employers that you have a certain level of expertise and it's a way to stand out from the crowd.
Look for Entry-level job opportunities: Once you have the foundational knowledge and some experience, start applying to entry-level data analytics roles.
Keep in mind that starting a career in data analytics is a journey and it takes time, patience and effort to get the right set of skills, knowledge and experience. It's important to keep learning and growing your skillset in order to stay competitive in the field.
If you're keen to study Data Analytics, then enrolling in APU is your best bet! Just contact our student counsellors, and they'll help out with your application. To proceed, click on the banner below:
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