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Do you want to become a data analyst in Singapore? If the answer is yes, then you’re in the right place!
I personally have become a data analyst with no relevant experience and want to share this career-switching journey with you!
In this blog post, I will cover five simple steps that will help you become a data analyst in Singapore.
I’ll also list out the skills that are required to be a data analyst, as well as how long it takes to become one in Singapore.
So read on and get started today!
How to Become a Data Analyst in Singapore?
To become a data analyst in Singapore, you’ll need to go through a few essential steps.
As the road to becoming a data analyst isn’t easy, I’ll share more of how you can do it with the least effort possible and the shortest time frame.
1. Obtain the Necessary Education and Qualifications
To become a data analyst in Singapore, many believe that one must first obtain a degree or diploma in a related field such as mathematics, data science, or business analytics.
However, I beg to differ!
I didn’t have a degree that was related to mathematics or analytics. I was a biology major and yet I still managed to get a job as a data analyst at Singapore General Hospital.
Of course, we all have to admit that obtaining a degree is very important to find a decent job in Singapore, and a data analyst is no exception.
I personally have taken a total of 3 data analytics courses throughout my time learning to become a data analyst. I took a fully online class, a hybrid class, and a class conducted fully in person.
Whichever you choose, taking a course will definitely help to speed up your learning process compared to self-learning from YouTube or other free online classes. That’s because it will provide you with more structure and guidance for the complete basics such as data types, how to code, etc.
2. Learn the Required Skills
Learning how to become a data analyst in Singapore is only possible if you have the skill set required for this job.
My previous employer for my data analyst job required me to know how to code in R, work with Tableau dashboards, and understand statistics.
The key skill needed in data analytics is coding, specifically learning how to write scripts in either Python or R. In addition, knowledge of SQL and how to work with databases is also essential.
On top of that, understanding how to use data visualization software such as Tableau or PowerBI will give you an advantage over other candidates.
Depending on the company that you want to work at, you’ll use different types of analytics software. For example, SMEs and small startups will mostly work with messy Excel sheets, so you’ll need to know advanced Excel formulas, Macros, and actions such as how to create Pivot Tables.
If you’re working for a large Multi-National Corporation (MNC), you’re more likely to work with SQL to query from their databases and a programming language to wrangle the data. You may use Tableau for the data visualizations.
More traditional companies may use Power BI as it integrates well with the Microsoft suite of products.
My personal recommendation is to focus on learning how to code well in R and Python because they will take the longest time to learn.
Tableau, Excel, and Power BI are much easier software to learn as you don’t need to understand computer programming languages to use them.
3. Build Your Online Portfolio
Build an impressive portfolio that showcases your analytical capabilities and technical knowledge.
An online portfolio can take different forms, but are not limited to one:
- LinkedIn Profile
- Personal website portfolio
- Rpubs profile
- R shiny apps
- Streamlit apps using Python
- Tableau Public
In fact, I recommend that you develop or create your own personal website portfolio and link it to your other data analytics projects in it.
Your personal website portfolio is also a great place to showcase your data analytics skills and how you have solved solutions through data-driven decision-making.
Don’t forget to add case studies of how you have used different software, tools, or techniques. You can also include samples of your work such as dashboards, reports, presentations, etc!
Doing data mining projects isn’t just a one-time thing – make it live longer by publishing it on the internet for employers to see.
In fact, when I was apply to jobs when I first graduated, many employers looked at my online portfolio to see the projects that I’ve done to evaluate my skills in data analytics.
I highly recommend having them prepared before you even go for an interview!
4. Acquire Hands-on Experience
Data analytics involves working with real-world datasets, so taking internships and volunteering opportunities can be invaluable for gaining the skills needed to succeed in this field.
You will be able to utilize these projects to hone your skill set in data analytics, gain valuable insights, and also how to present it in a meaningful way.
In addition, participating in hackathons or Kaggle competitions is a great way for getting hands-on experience with how data analysts interact and solve real-world problems.
If you have the luxury of taking a data analytics internship as a student, take it! In fact, I did a total of 5 internships to get the experience I needed.
Of course, you may not need that many to land a data analyst job. I just needed to slowly build my confidence in my skills so I did some full-time and part-time internships.
Here’s what I did for my 5 internships:
- First data science research experience project at my university
- First internship with Duke-NUS profession at a data science lab
- Second internship with Agency for Integrated Care (AIC) as a data analytics and quality intern
- Data analytics winter intern at Infocomm Media Development Authority (IMDA)
- Part-time data analytics intern at IMDA
These internships gave me the necessary analytics skills I needed to start my career in data analytics.
After each internship, I made sure to have proof that I have learned and applied applications for a particular technical software such as R, Python, or Tableau.
I slowly iterated this learning and internship process until I was confident in my skills.
This proved to be crucial for landing my data analyst job later on because I was able to show how I have leveraged the data analytics skills and knowledge that I gained from my internships during my interview sessions with employers.
5. Network and Apply for a Data Analyst Job
The final step is to network and apply for data analyst jobs! It’s important that you remain active on LinkedIn by joining groups related to data analytics, attending events, and connecting with industry professionals.
Make sure to tailor your resume according to the roles you are applying for, highlight how you will be able to make valuable contributions based on what you’ve learned from internships or experiences, and how you can apply them to the job.
Don’t forget to use your connections too! Networking is key to getting a job and you should always utilize your contacts to help you get that dream data analyst job.
Throughout my whole time at my university (NTU), I made sure to stay connected with like-minded individuals within my school and alumni from the same course as myself.
I also made sure to link up and connect with other budding and aspiring data analysts who wanted to enter the same industry as I did.
From there, we learned to share insights with each other, which made the learning process much more enjoyable!
Becoming a data analyst takes some time, but it will be worth it in the end! Just remember to stay positive and work hard at improving your skillset 🙂
What Data Analytics Skills to Learn?
There are a few nuanced data analytics skills that employers look out for when hiring data analytics talent.
Here are some of the key data analytics skills that you should hone and master to become a successful data analyst in Singapore:
1. Data wrangling and Cleaning
Data wrangling and cleaning is an important skill for data analysts because it is the process of taking raw data, transforming it into a usable format, and cleaning any inconsistencies so that it can be used in further analysis.
Cleaning data ensures the accuracy and reliability of the results. It also helps to reduce the amount of time needed for analysis by removing unnecessary observations or variables.
Data wrangling and cleaning also provide better quality insights since data is more organized and consistent, which can lead to more accurate predictions based on the data.
2. Data Visualization and Storytelling
As a data analyst, you’ll need to know how to create dashboards using tools such as Tableau or Power BI which effectively communicate insights to stakeholders.
Data visualization is an important skill for data analysts because it not only helps to quickly identify patterns from the data but also makes complex problems understandable by stakeholders.
It’s essential that you learn how to create compelling visuals that tell a story and how to use different charts and graphs to help visualize your insights better.
3. Machine learning & Artificial Intelligence
You’ll also need to know how to use different machine learning and AI models and algorithms to gain predictive insights from data.
These skills will help you build powerful data models that can be used to predict customer behavior, forecast sales, and more.
It’s important to have the ability to work with different machine learning libraries in Python such as scikit-learn or TensorFlow so that you can create accurate models using a wide range of data sources.
4. Statistical Analysis
Statistics is important as a data analyst because you’re always looking to see if the data findings are significant. You need to know how to perform descriptive, predictive, and inferential statistics using software such as R or Python.
You’ll also need to be able to use different statistical tests such as chi-square and ANOVA in order to draw conclusions from the data.
In addition, you should know how to identify relationships between variables and how to create meaningful visualizations of results.
5. Business Intelligence & Analytics
Query languages such as SQL and NoSQL are also essential for data analysts who want to work with large databases.
You should be comfortable writing SQL queries and know how to use a database management system such as Oracle, MySQL, or Microsoft SQL Server in order to extract data from different sources.
Business intelligence and analytics skills will also help you interpret the data better and provide actionable insights to stakeholders.
How Long Does It Take To Become a Data Analyst in Singapore?
It takes about one year to become a data analyst in Singapore. During this time, you should be learning the skills mentioned above and applying them in practice.
You may need to take some online courses and certifications to get up to speed with the latest technologies, but it will definitely be worth it in the end.
Once you have the necessary skills, it’s important to gain some experience in data analytics by working on projects or completing internships in order to increase your chances of landing a job.
Overall, if you put in the effort and work hard, there is no reason why you can’t become a successful data analyst within one year.
With a solid foundation in data analytics and the right mindset, you’ll be ready to start your career as a data analyst in Singapore.
Good luck and jiayou!
Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.
Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.
To unwind, Justin enjoys gaming and reading.