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4 minute read

What Does a Data Analyst Do and the Skills Required

Think you have what it takes to become a data analyst? Here, we explore the qualifications, industries and skills involved in this intriguing career path.

If you love to develop creative solutions for challenging technical problems, undertaking a data analyst qualification is the ideal move for your career. With this in-demand role needed across virtually every industry that uses data to make its big decisions, carving out a productive career is possible with the right skillset.

However, you’ll have to be comfortable working in a rapidly evolving space. As emerging technologies like artificial intelligence and machine learning affect how we process information, this fascinating sector will undergo numerous changes in the future. To determine whether the role of data analyst makes sense for you, we’ve explored the job requirements in detail below.

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How to get qualified as a data analyst in Singapore

Thinking about getting qualified as a data analyst? You have numerous opportunities in Singapore to undertake a respected degree that provides analytics knowledge and basic big data principles to help you flourish in your career. For example, the National University of Singapore offers a four-year Bachelor of Science (Honours) with a Major in Data Science and Analytics.

Several high-quality private institutions also offer diplomas in data analytics and data science. At the London School of Business & Finance, you can complete an eight-month Diploma in Data Analytics, offering a solid foundation of relevant skills while preparing you for continued professional development. Although a degree isn’t strictly needed to work as a data analyst, it will make your journey easier.

What industries need data analysts?

Data analytics is a thriving career choice for Singaporeans, with the country quickly becoming a leading regional data hub for Southeast Asia. With big data becoming increasingly important to most industries, ranging from finance and telecommunications to healthcare, finding a data analyst role in an area you care strongly about is more and more likely.

For example, the finance industry uses data analysis for countless tasks, including financial planning services, fraud detection, and loan management. Meanwhile, data analysts are crucial in the healthcare sector as organisations attempt to streamline services and draw fascinating insights from the wealth of information they collect.

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Essential data analyst skills

Before deciding on a sector for your career as a data analyst, it’s a good idea to understand the skills most important to your success. From extensive technical abilities to a selection of soft skills, these core talents will help you establish a solid professional reputation.

1. SQL

SQL or Structured Query Language is a domain-specific language that allows you to communicate with databases. With these vast directories potentially storing tremendous amounts of data, SQL enables you to retrieve this information with incredible speed and precision. You can add information or pull data from the database to achieve your objectives with fluency in the language.

SQL is often considered the advanced version of Microsoft Excel with even more powerful features. With virtually all companies keeping their records in online databases, SQL is perhaps the most essential skill for data analysts to learn. By knowing how to communicate with datasets productively, you’ll have the ability to identify fascinating insights that drive business goals.

2. Statistical programming

Statistical programming is another vital skill where data analysts use computation techniques to conduct their research into specific sets of information. Like SQL, you’ll need to learn specific programming to complete this analysis, with R and Python often considered the most important options.

By using code to analyse data, you’re creating a reproducible method that others can use to discover the same answers. This means you can easily collaborate with other analytical professionals to investigate ideas and develop powerful solutions to problems. Adding R and Python to your skillset is paramount.

3. Machine learning

As part of artificial intelligence, machine learning has become one of the most discussed innovations in data analytics. By taking the time to learn the fundamental components, tools and concepts, you’ll have the ability to process historical data using tailored algorithms to produce a machine learning model.

Using this model, data analysts can make informed predictions about the future. For example, machine learning can predict what products people with specific demographics will most likely buy. Alongside a wealth of other possibilities, understanding the basics of machine learning will make you a valuable resource as this field grows in prominence.

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