How to Learn Data Analytics in Singapore

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Do you live on this sunny island of Singapore and want to learn data analytics?

If so, you’re in luck!

I have learnt data analytics from scratch (that’s right, zero knowledge!), and I wrote this article to divulge all the secrets of how I became a data analyst.

In this blog post, I will outline all the steps you need to take to learn data analytics from scratch. We will also provide information on the courses available in Singapore, and how to build a data analytics portfolio.

I’ll write this article simply so that even if you’re a beginner, you can follow along easily πŸ™‚

And no, you won’t need a computer science degree to land a job; just some data analytics training will do!

Without further ado, let’s start!

Data Analytics Singapore: How to Learn?

Many people think that it isn’t easy to learn things online, but it’s actually really feasible!

Here are some steps that I recommend that you take if you want to learn data analytics in Singapore.

1. Watch YouTube Videos on Data Analytics

I know this is not the common first step in learning data analytics, but trust me on this!

First things first, you’ll want to go watch some content on YouTube on what goes on in the life of a data analyst.

By studying data analytics, you’ll begin as a junior data analyst so it’s good to start with the end in mind!

When watching these videos, understand their daily life and make sure you know what you’re signing up for before you even begin studying data analytics!

Because the life of the data analyst can be really technical and requires a high complexity of logical reasoning, if you’re not that type of person, you’ll feel tired easily.

While many may say that data analysts are cool, the reality is that you’re going to spend lots of time solving very time-consuming data problems for long hours.

Next up, you need to understand the basics of data analytics and get a sense of what data analysis entails.

Watch a few tutorials online to learn more about this.

This is also an excellent opportunity to familiarise yourself with data science concepts like data cleaning, data engineering, and data visualisation.

2. Understand Data Analytics Tools & Techniques

Now that you have some basic knowledge of data analysis, it’s time to start learning data analytics tools and techniques.

The data analytics toolset will vary depending on the industry you wish to work in.

For example, data analysts working in finance may want to learn R or Python, while data scientists studying machine learning might opt for TensorFlow or PyTorch.

These data analytics tools are essential for data analysts to understand data and make sense of it, so it’s important that you learn them.

There are plenty of free resources online, such as courses on Coursera or Udemy, which can teach you the basics of these data analytics tools.

Personally, I began with R instead of Python due to my background in biology during my uni days.

However, I do recommend that you begin with learning Python instead of R for its use cases and expandability to data science if you want to learn that in the future.

Beyond just programming languages, I want to encourage you to learn the common data analytics tools:

  • Excel
  • Tableau
  • Power BI.

While many tech companies will talk about using SQL, many Singaporean companies will prefer to hire candidates who can do quick and good analyses using everyday tools such as Excel.

This means you need to learn common Excel tools such as Pivot Tables, simple macros, and Vlookup functions.

Tableau is one of the nicer-looking data visualisation tools that you can use. I personally have learnt this tool on my own and from data analytics courses in Singapore, both online and in person.

As many Singaporean employers are looking to build beautiful-looking Tableau dashboards to monitor metrics and the health of their businesses, you need to pick up this skill!

3. Practice Analyzing With Sample Datasets

Now that you’ve learnt data analysis tools and techniques, it’s time to put them into practice.

There are plenty of online data sets available to help you practice data analytics.

Kaggle is a great site to find datasets so hop on there and get practicing!

You can also use data from your everyday life such as tracking your own health data, data from the news, or data from your own personal spending habits to practice data analytics.

These data sets can help you hone your data analytics skills and build a portfolio that employers would love to see.

So dive in and start building those data analysis portfolios!

Personally, I used Kaggle to find some interesting data sets for a start. As you may be a beginner, I suggest finding something that may interest you first, or something that you have experience with within your job currently.

Don’t go for the more complex data sets with super dirty data or those for machine learning algorithms yet.

4. Take a Data Analytics Course in Singapore

I highly recommend you take a data analytics course in Singapore.

Trust me on this: Taking a data analytics course can really supercharge your learning process!

Making a job switch in Singapore isn’t easy, and you need to do it fast. A data analytics course in Singapore will help to be the catalyst for the process.

These data analytics courses in Singapore will teach you important data science topics such as data wrangling, statistical modeling, and machine learning – topics that are often not covered in the basic data analytics tutorial videos found online.

Most course fees can be claimed from the IBF Standards Training Scheme and the remaining course fees are usually paid through SkillFuture Credit.

When attending these data analytics courses, you can spend some IBF credit from your employer and the rest can be covered using your SkillsFuture credit.

However, I admit that when I took my courses, it was part of the CITREP+ scheme and my course fees were fully funded by the IMDA.

As each data analytics course will be a little different, find out what works for your financial situation.

Personally, I have attended 3 data analytics courses

  • One fully online using Udemy
  • One taught live online through Zoom with an instructor
  • One taught live, in person with an instructor

In my experience, you should never attend classes taught live by Singaporeans without any prior exposure to data analytics.

You will really get rekt due to how fast they can teach!

For courses with any coding in them, it can get technical really fast, which is why I recommended you learn data analytics tools first.

Even I could barely keep up with the curriculum despite having some experience creating my own data analytics projects at the time!

I still recommend attending a data analytics course with an open mind! Learn as much as you can from them!

Also do take the capstone project from the courses seriously; they may need you to pass it for successful completion, which is the eligibility criteria for SkillFuture Credit claims.

My capstone project was on video game data, and I did it on Tableau. I encourage you to try something fun as you learn the key concepts of data analytics.

5. Reinforce Your Learning With Blogs, More YouTube, and Books

Now that you have taken a data analytics course, you will be able to reinforce your learning by reading data analytics blogs and watching data science videos.

If you’re into data analytics and from Singapore, you can consider following my blog, I will be posting many topics within data analytics that you may find useful here πŸ™‚

Other than myself, there are many people who constantly post on Towards Data Science and blogs. You can go have a read there.

They are known for their technical blog posts, which you can follow along with to replicate their projects.

Remember this: copying someone’s code and modifying it for your own projects is normal, and you should get used to it!

In fact, the above meme has been going around and is well-accepted by the coding community.

6. Look for Opportunities to Use Your Data Analytics Skills

I will divide this section into two sections:

  1. Students learning data analytics with an irrelevant major
  2. Mid-career working professionals looking for a job switch


For students learning to delve into the data analytics world with a degree that’s not related, you’re not alone! There are MANY students looking to do the same among your peers.

It’s not unusual to upskill yourself with data analytics skills to enhance your employability.

Also, you’re also walking the path that I took a few years back when I was a student! I was one of you πŸ™‚

I was a biology student at NTU, but I wanted out because it wasn’t for me, but data analytics always attracted me.

I encourage you to look for ways to grow your skills as a student! You can start by volunteering at an association that accepts data analytics volunteers or as an intern during your holidays.

I for one, did a total of 4 data analytics internships during my uni days, which helped me gain confidence in coding and my analytical skills.

You can start by writing to professors to help out in their research labs. Look for a data science or data analytics professor to help out. I wrote to Duke-NUS and even some at NTU for opportunities.

Mid-career adults:

Since you may not have the luxury of doing internships or volunteering due to time constraints, you need to find a way to analyze the data that you are already receiving from your workplace.

That means that if you’re working in a hospital, find a way to analyze the wait time at the clinic. If you’re doing finance, analyze financial trends from raw data. If you’re doing engineering analyze the performance of your devices using Tableau dashboards to quickly visualize trends.

If you have more free time, make sure to do more projects on your own with data sets, as you’ll need to gain as much exposure to working with data as possible.

7. Develop a Data Analytics Portfolio that Stands Out to Singapore Employers

Singaporean employers love to know that you can analyze data well. You need to demonstrate it. Here’s your chance to prove your experience in data analytics.

I emphasize on demonstrating work experience because the data analytics certificate you got from the course you took is pretty much meaningless to employers.

Employers look out for relevant data analytics experience. They want to know that you can solve the problems that they throw at you!

As data analytics becomes increasingly popular in Singapore, you need to be able to stand out from the rest of your peers.

This means having a data analytics portfolio that impresses employers.

Your data analytics portfolio should have all the analytics projects you’ve done in the past. I recommend using a digital portfolio to demonstrate your projects.

My recommendations:

  • Create a Tableau public account and publish your data visualisation/dashboards there
  • Deploy your data analytics web apps on Shiny
  • Build a portfolio website that has your resume
  • Update and build up your LinkedIn profile

What’s the Difference Between Data Science and Data Analytics?

Data science and data analytics are two terms that often get confused with each other.

Data science is a combination of data analysis, software engineering, data visualization, and machine learning in order to extract meaningful insights from data sources.

It involves finding patterns in data sets and using predictive models to make predictions about future events.

On the other hand, data analytics is the process of collecting, organizing, and analyzing data to gain insights into trends or patterns.

It involves using data visualization tools to present information in a visually appealing way so that stakeholders can make informed decisions.

Data analytics focuses on descriptive and diagnostic analysis which means it looks at what’s happening now and why it’s happening.

Data science, on the other hand, is more concerned with predictive and prescriptive analysis which means it’s focused on predicting what will happen in the future and how to take advantage of this information.

The key difference between data analytics and data science is that data analytics focuses on descriptive analysis while data science involves predictive modeling.

Generally, data science is more difficult to learn than data analytics.

Final Thoughts

At the end of the day, data analytics in Singapore is all about your ability to analyze and present data with problem-solving and creativity.

It’s important to be up to date with data analysis tools, techniques, and trends. In the field of data analytics and business analytics, it’s always important to stay curious and keep learning.

Learning analytics requires time and effort, but once you understand the fundamentals of data analysis and data visualization, you’ll have a better understanding of data science which will help you make better decisions.

With the right data analytics course, you will learn how to turn raw data into valuable insights at a much faster pace.

These are some tips I hope will help you excel in data analytics in Singapore. It’s never too late to learn data analytics and build up your data analytics expertise! Best of luck!

Let me know if you need help with data mining/data analytics/ business intelligence projects. πŸ™‚

Remember, as computer science is fast, keep learning new data analytics skills to stay relevant along the way!


Justin Chia

Justin is the author of Justjooz and is a data analyst and AI expert. He is also a Nanyang Technological University (NTU) alumni, majoring in Biological Sciences.

He regularly posts AI and analytics content on LinkedIn, and writes a weekly newsletter, The Juicer, on AI, analytics, tech, and personal development.

To unwind, Justin enjoys gaming and reading.

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