The terms ‘data analyst’ and ‘data analytics’ have become extremely relevant in today’s day and age – where numbers and large amounts of data have become the norm in many industries. With each and every year passing, analysing data has grown to become a crucial task for most businesses, as data is able to reveal many things to a company.
For example, the data collected from an e-commerce website could reveal more about one’s consumer base: their average age, their gender, interests, average time spent on the website, popular purchases, and more. When analysed, these large amounts of data can then be used to improve a company’s performance – which could potentially lead to more sales and a better consumer experience.
Read on to find out more about data analytics and what it’s like to be a data analyst.
What Is Data Analytics?
To understand this term, let’s first break it down. According to Oxford Languages, ‘data’ can be defined as: ‘facts and statistics collected together for reference or analysis,’ and ‘analytics’ can be defined as: ‘the systematic computational analysis of data or statistics.’
With that, we can deduce that ‘data analytics’ refers to a systematic computational analysis of facts and statistics collected together. To put it simply, data analytics is all about collecting, organising, and sorting through large amounts of data with different tools and systems to gain key insights. And as previously mentioned, these insights can then be used to make better decisions for companies.
What Are The Four Types of Data Analytics?
The four basic types of data analytics are as follows:
Descriptive Analytics: This is the most basic and simple type of analytics. It’s used to answer the question: “What happened?” – describing trends and relationships. Data visualisation tools like Tableau and Excel are frequently used to communicate descriptive analytics.
Diagnostic Analytics: This type focuses on answering the question: “Why did this happen?”. It focuses on finding correlations and the root of problems. For instance, finding the cause of an increase in sales of a particular product.
Predictive Analytics: This type answers: “What could happen in the future?”. Using historical data and analysing trends, one can make informed projections on the future of the industry, which in turn can affect company decisions.
Prescriptive Analytics: This type answers: “What should we do next?”. Its main goal is to use data to find the best course of action. For this type, all relevant factors need to be considered and analysed, which then leads to actionable insights.
What’s The Difference Between Data Analytics And Data Science?
Though the terms sound similar, they differ in many ways. Data science is a blanket term involving gaining insights from large sets of raw data using computer science, machine learning, algorithms and statistics – with the main focus being on finding new areas of study and new questions to be answered.
On the other hand, data analytics is more about finding solutions to concerns – using large amounts of data to produce insights that can be applied quickly.
So, even though both fields use and analyse large amounts of data, their purposes are vastly different. Data science focuses on finding correlations and new areas of study, while data analytics is a branch of data science that emphasises more on searching for answers in existing pools of information and data.
What Does A Data Analyst Do?
As a data analyst, you’ll be expected to obtain, organise and analyse large data sets on market research, sales, and more (depending on the industry/company you work for). Data analysts are key in ensuring that large amounts of data are properly processed and examined through, and insights are accurately obtained.
Some key skills/tools you may be expected to have/be able to use as a data analyst includes the following (not exhaustive):
- Structured Query Language (SQL)
- Microsoft Excel
- Critical thinking skills
- Presentation skills
Is Data Analytics A Good Career?
According to the Singapore Economic Development Board, the data analytics industry plays a central role in Singapore’s economy, even stating that there are “studies indicating that it contributes at least S$1 billion (US$730 million) each year.”
On top of this, many job sites indicate the ever-present demand for data analysts, with Indeed showing over 7,000 job listings, Linkedin showing over 12,000, and JobStreet showing over 23,000 (as of June 2022).
Overall, the job outlook seems to be positive due to the high and growing demand for data analysts. To further delve into this, let’s take a look at the average salary of a data analyst.
How Much Is A Data Analyst’s Salary?
The job website Indeed shows that the average base salary for a data analyst in Singapore is S$5,215 per month, as of June 2022.
Similar to other jobs, the salary of a data analyst also differs with different companies and years of experience. On the same website, it also shows how the average salary for a data analyst at Meta is S$11,757 per month, while the salary at Google is S$9,783 per month.
The pay differs globally too, with Indeed stating that the average salary for a data analyst is $6,624 per month in Australia, and $4,639 in the USA.
How Can I Start My Data Analytics Journey?
To get started, be sure to learn the aforementioned tools (Excel, SQL, Tableau) to obtain the necessary skillset. On top of learning these tools, opt to certify your expertise with a Data Analytics course, where you can learn industry tools from expert data analysts and receive feedback and support from professionals.
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