In the dynamic landscape of Singapore’s data-driven industries, data analysts have become pivotal in guiding strategic decisions, regardless of industry. From finance to healthcare, retail to logistics, organisations big and small are increasingly reliant on data to drive decision-making processes. For instance, in finance, data analysts play an indispensable role in risk assessment, fraud detection, and market analysis. In healthcare, data analysis optimises patient care and allocation.
For aspiring data analysts, the journey to mastery involves accruing years of experience, proficiency in specialised skills and embracing a mindset geared towards decoding the language of data.
With the rising cost of living and crazy inflation rates, it’s natural for us to be curious about the earning potential of data analysts, as we all seek financial stability amid these uncertain times. Well, you’d be happy to hear that the career trajectory of a data analyst comes with promising financial rewards.
Entry-level positions typically offer salaries ranging from SGD 50,000 to SGD 70,000 per annum. After gaining a few years of experience, mid-level analysts can expect their compensation to range from SGD 70,000 to SGD 90,000 annually. As the analyst progresses to a senior-level data analyst, the anticipated annual salary ranges from SGD 90,000 to SGD 120,000. With grit and lots of hard work, a lead-analyst can look forward to an annual salary range of SGD 120,000 to SGD 180,000.
Sounds good, doesn’t it? Of course, there are no free lunches in the world. At each level, there are required skills and expertise, which we’ll be touching more on below.
Image source: Nodeflair
Career Progression: Years of Experience and Skills Required
1. Entry-Level Data Analyst (0-2 years)
Experience: Fresh graduates or those making a career transition often start here to gain foundational (but important) experience through internships or entry-level positions. Check out our previous article, where we provided insights on kickstarting a successful career in data analytics.
Skills Required: At this level, analysts are expected to have a strong foundation in technical skills. For instance, proficiency in SQL is necessary for data querying and manipulation. Excel proficiency will be helpful in organising and analysing data, while data visualisation tools like Tableau (sound familiar?) or Power BI helps to present insights effectively.
In addition, basic statistical knowledge (e.g., mean, median, and standard deviation) is imperative to help decode and interpret findings.
2. Mid-Level Data Analyst (2-5 years)
Experience: Having acquired a deeper understanding of the job requirements, analysts at this stage start handling more complex projects. They may also begin specialising in specific domains.
Skills Required: Advanced proficiency in data querying languages such as SQL, and programming languages like Python or R will be necessary for data manipulation and analysis. It is also essential for mid-level analysts to be well-versed with statistical analysis techniques, including regression, hypothesis testing, and clustering.
At this level, analysts will engage in data modeling activities, which calls for expertise in building predictive models and evaluating model performance. It will also be advantageous if analysts are familiar with machine learning concepts and algorithms.
3. Senior-Level Data Analyst (5+ years)
Experience: Seasoned analysts possess a wealth of experience in managing vast and diverse datasets, as well as complex analytical problems. They often take a lead role in projects, mentoring junior team members, and collaborating closely with stakeholders.
- Skills Required: To thrive as a senior data analyst, they often showcase mastery in data manipulation, including complex joints, subqueries, and optimising queries for large data sets.
A senior data analyst is also expected to be proficient in advanced statistical methods such as time series analysis, multivariate analysis, and experimental design. They should be able to extract meaningful insights from complex statistical models.
Beyond building predictive models, senior data analysts should also be able to interpret and communicate the implications of such models clearly and effectively.
Expertise in specific industries or domains become crucial here. Having an understanding of the nuances of the industry’s data and business processes will also be helpful in providing customised and relevant insights.
4. Lead-Level Data Analyst (> 5 years)
Experience: Typically requiring at least 5 years of experience, lead data analysts go beyond plain expertise. They often demonstrate self-initiative and problem defining abilities.
Skills Required: Apart from technical mastery, lead data analysts are expected to display strategic thinking and problem-defining abilities. At their level, they should be able to identify gaps and problem spaces within the organisation that have the potential to generate significant Return on Investments (ROI).
Needless to say, leadership skills become paramount at this stage. It’s crucial that lead analysts are equipped to lead teams, guide projects, and that they possess a vision for how data analysis can drive business outcomes.
Lastly, lead analysts should also be able to communicate complex insights in a simple and actionable manner, as this is vital for leading as teams and influencing decision-makers
As data analysts progress in their careers, the evolution of their skill sets plays a pivotal role in career advancement. Beyond technical skills, there will be growing emphasis on soft skills like leadership, strategic thinking, and effective communication as analysts move into senior positions. To rise above competition, domain-specific knowledge will be useful in providing customised insights tailored to the industry’s requirements.
This constant evolution and advancement of skills ensures that data analysts remain at the forefront of leveraging data for decision-making, guiding organisations towards success in the rapidly evolving market.
Embracing Learning: Data Analytics Nanodegree at Heicoders Academy
Inspired and ready to embark on an exhilarating journey ahead? Heicoders Academy DA100: Data Analytics with SQL and Tableau is the perfect stepping stone for aspiring data analysts. This course equips beginners with a strong foundation in data analytics, such as generating insights via data visualisations with Tableau, interacting with databases using MySQL, and deploying data analytics dashboard on the cloud.
What’s better than one certificate? Three certificates! After the completion of DA100, learners may choose to pursue a Data Analytics Nanodegree by completing two other courses, AI100: Python Programming and Data Visualisations, and AI200: Applied Machine Learning.
In the former (AI100), learners will gain a strong foundation in Python programming and acquire intuition on how to harness actionable insights from real world data. Learners will also get the opportunity to build impressive tabular, graphical and geospatial visualisations using their Python development skills.
In the latter (AI200), learners will go one step further and develop interactive graphical visualisations, train and evaluate machine learning models for classification, regression and clustering problems. They will also grasp the intuition and differences across supervised and unsupervised machine learning algorithms.
If some of these terms sound familiar, it’s because they are skills required by analysts in the various stages of their career, as we mentioned above.
Luckily for interested parties, for DA100, AI100, and AI200, all Singaporeans and PRs can enjoy up to 70% subsidy of course feeds under the IBF Standards Training Scheme (IBF-STS). Remaining course fees can then be offset either fully or partially with SkillsFuture Credits or UTAP subsidy for NTUC Union members.
Please refer to the tables below for DA100, AI100 and AI200 course fees.
DA100 Course Fees
AI100/AI200 Course Fees
The journey of a data analyst in Singapore ranges from basic knowledge to becoming a leader that shapes and molds an organisation’s goals. As the demand for data-driven decision making grows, the career pathway for data analysts continues to promise opportunities and innovation. Embracing this journey involves a commitment to acquiring specialised skills, staying adaptable in the ever-changing tech landscape, and demonstrating leadership qualities that redefine what it means to decode the language of data in today’s world.