Unpacking Data Analytics: Everything You Need to Know

Data analytics might seem like a complex and mysterious field. But fear not! This guide will walk you through everything you need to know, from the basics to potential careers and essential tools. Whether you're a curious beginner or looking to switch careers, you're in the right place.
 

What Is Data Analytics?

At its core, data analytics is the process of examining data sets to conclude the information they contain. It helps us make sense of vast amounts of data, uncover patterns, and gain valuable insights. These insights can then drive better decision-making in various aspects of business, science, and more.
 

Differences between Data Analysis and Other Analytical Methods

Data Analytics vs Exploratory Data Analytics

Exploratory data analytics focuses on discovering patterns and relationships in data. It's like going on a treasure hunt in your data. Data analytics, on the other hand, is broader. It uses various techniques, including those from exploratory analysis, to answer specific questions and solve problems.
 

Data Analytics vs Data Science Analytics

Data science is a more comprehensive field. It combines data analytics with machine learning, statistics, and domain knowledge. Data analytics, while related, often has a more immediate focus on understanding and interpreting data for practical decision-making.

Read more: Data Science vs Data Analytics - Key Differences You Must Know
 

Data Analytics vs Big Data Analytics

Big data analytics deals specifically with large and complex data sets. Data analytics can handle smaller data, too. The techniques may overlap, but big data analytics often requires specialised tools and infrastructure due to the volume and complexity of the data.

Read More: Big Data Explained: Applications, Careers, and Tools You Need to Know
 

Data Analytics vs AI Data Analytics

AI data analytics uses artificial intelligence techniques to analyse data. Regular data analytics can rely on more traditional statistical and computational methods. However, as AI becomes more integrated, the lines are blurring.

To better understand the differences between these five data analysis methods, we've summarised a table for your reference.
 

Aspect	Data Analytics	Exploratory Data Analytics (EDA)	Data Science Analytics	Big Data Analytics	AI Data Analytics
Primary Focus	Extract insights from structured data	Discover patterns/relationships in raw data	Build predictive models & algorithms	Process massive, complex datasets	Automate insights using machine learning
Main Question	"What happened and why?"	"What patterns exist here?"	"What will happen and why?"	"How to handle data too big for traditional tools?"	"How can machines learn from this data?"
Data Type	Structured (cleaned, organised)	Raw/unprocessed	Both structured & unstructured	Extremely large volumes (TB/PB)	Any data suitable for machine learning
Tools Used	Excel, Tableau, SQL	Python (Pandas, Matplotlib), R	Python (Scikit-learn), TensorFlow	Hadoop, Spark, NoSQL	TensorFlow, PyTorch, AutoML
Output	Reports, dashboards, KPIs	Visualisations, hypotheses	Predictive models, algorithms	Processed data pipelines	Self-learning models
Time Orientation	Past & present	Present (initial analysis)	Future predictions	Real-time & historical	Adaptive/continuous learning
Skill Level	Beginner-friendly	Intermediate statistical knowledge	Advanced programming/maths	Distributed systems expertise	Machine learning/deep learning expertise
Business Application	Performance tracking, reporting	Data quality assessment	Recommendation systems, forecasting	IoT analytics, clickstream analysis	Chatbots, computer vision

 

Why Is Data Analytics Important?

Data analytics is crucial because it enables us to make informed decisions. We can base our choices on hard data instead of guessing or relying on gut feelings. For businesses, this means increased efficiency, better customer understanding, and, ultimately, higher profits. In healthcare, it can lead to more accurate diagnoses and better treatment plans.
 

What Is the Purpose of Data Analytics?

The purpose is to turn raw data into actionable information. By analysing data, we can identify trends, predict future events, and solve problems. For example, a retailer might use data analytics to predict which products will be popular during a particular season and adjust their inventory accordingly.
 

Types of Data Analytics

Descriptive Analytics

This type tells us what has happened in the past. It summarises historical data, like how many products a company sold last year or how many website visitors a particular page had last month.

Diagnostic Analytics

Diagnostic analytics digs deeper to understand why something happened. If sales dropped in a certain region, diagnostic analytics can help figure out if it was due to a new competitor, a change in marketing strategy, or something else.

Predictive Analytics

As the name suggests, predictive analytics tries to forecast what will happen in the future. It uses historical data and statistical algorithms to make predictions, such as predicting customer churn or stock prices.

Prescriptive Analytics

Prescriptive analytics not only predicts what will happen but also suggests what actions should be taken. For instance, if a predictive model shows that a machine is likely to fail soon, prescriptive analytics can recommend the best time to perform maintenance.
 

The Data Analytics Process and Examples

Data Collection

The first step is to gather relevant data. This can come from various sources like databases, surveys, sensors, or social media. For example, an e-commerce site might collect data on customer purchases, browsing history, and feedback.

A food delivery app, "DelishSG", collects data from its order database on food items ordered, delivery addresses, and timings. They also send post-delivery surveys via email, asking about service satisfaction. Additionally, they use GPS sensors in delivery scooters to track travel times.

Data Cleaning

Raw data is often messy. It may contain errors, missing values, or duplicates. Data cleaning involves fixing these issues to ensure accurate analysis. For instance, if a survey has some incomplete responses, data cleaning might involve either removing those responses or filling in the missing values appropriately.

A property firm, "PropertyLinkSG", has data on condo sales. Some records lack unit size details. They clean this by referring to the Urban Redevelopment Authority (URA) property database. Duplicate entries are removed.

Data Exploration

Once the data is clean, it's time to explore it. This is where we look for patterns, relationships, and outliers. We might use visualisations like histograms or scatter plots to get a better understanding of the data.

A fintech startup, "FinVerseSG", uses scatter plots to study the link between transaction amounts and customer age in their digital banking data. Histograms show transaction frequency distribution. This helps spot anomalies in different Singaporean districts.

Data Modelling

Data modelling involves creating mathematical models to represent the data. These models can help us make predictions or understand complex relationships. For example, a linear regression model can be used to predict sales based on advertising spend.

A tourism body, "Visit Singapore", predicts tourist arrivals at attractions like Gardens by the Bay. They use time-series models with data from the Singapore Tourism Board (STB) on past arrivals, major events, and economic data.

Data Visualisation

Data visualisation is all about presenting data in a graphical format. Graphs, charts, and dashboards make it easier for non-technical people to understand the insights from the data. A pie chart can quickly show the market share of different companies, for example.

The National Environment Agency (NEA) uses interactive dashboards to show air quality data. Line graphs display PM2.5 trends in different regions, and pie charts show pollutant proportions.

Decision-Making

Finally, the insights from data analysis are used to make decisions. A marketing team might decide to increase their budget for a particular campaign based on data-driven insights about its effectiveness.

A grocery chain, "MarketPlusSG", finds from data that customers in the west prefer local produce on weekdays. The chain then boosts local produce stock and promotion in western stores on weekdays.

 

Must-Know Data Analytics Tools

There are many tools for data analysis. For beginners, Excel and Tableau offer easy analysis without coding. Intermediate users progress to Power BI and SQL for database work. Advanced analysts use Python, R and Spark for complex statistics and big data – these require proper programming knowledge but deliver powerful results.

1. Microsoft Excel

Excel remains essential for basic data tasks. It handles sorting, filtering, and simple formulas (like VLOOKUP). Pivot tables help summarise data quickly. Many Singaporean SMEs use Excel for sales tracking and budgeting. Free alternatives include Google Sheets.

2. Tableau

This visualisation tool turns numbers into interactive dashboards. Users drag and drop to create charts without coding. Singapore’s GovTech uses Tableau for public transport data displays. It connects to Excel, SQL, and cloud databases.

3. Power BI

Microsoft’s business analytics service creates reports and live dashboards. It works well with other Microsoft products. Singapore banks use Power BI to monitor fraud patterns. The free version handles most small-business needs.

4. SQL (Structured Query Language)

SQL manages database information. It retrieves specific data (e.g., "Show all customers in Punggol"). Skills in PostgreSQL or MySQL are valued in Singapore’s tech jobs. Many courses teach SQL basics in weeks.

5. Python (Pandas/NumPy)

Python’s Pandas library cleans and analyses data. NumPy handles complex calculations. Grab uses Python for fare pricing models. Beginners can start with free Jupyter Notebook tutorials.

6. R

R specialises in statistical analysis and graphs. Researchers at NUS use it for scientific studies. While steeper to learn than Python, it offers powerful data testing tools.

7. Google Analytics

This tracks website visitor behaviour. Singaporean e-commerce sites use it to see which products get the most views. The free version shows traffic sources and user demographics.

8. Apache Spark

Spark processes huge datasets fast. Singapore’s ride-hailing apps use it for real-time location analysis. It requires coding knowledge, but outperforms traditional tools for big data.

9. KNIME

A free, no-code tool for automating data workflows. Local universities use KNIME to teach analysis concepts. It has pre-built modules for common tasks like sentiment analysis.

10. RapidMiner

This machine learning platform helps predict trends. Singaporean insurers use it for risk modelling. The drag-and-drop interface suits beginners exploring AI analytics.
 

Essential Skills for a Data Analyst

A data analyst needs a combination of technical and soft skills. Technical skills include proficiency in data analysis tools, knowledge of statistics, and programming. Soft skills like communication are also crucial. After all, what's the point of finding great insights if you can't communicate them effectively to stakeholders?
 

Data Analytics Jobs & Career Paths

There are various career paths in data analytics. You could start as a junior data analyst, working on basic data analysis tasks. With experience, you can move up to become a senior data analyst, handling more complex projects. You might also choose to specialise in areas like business intelligence or predictive analytics.
 

Data Analytics Salary Insights

Average salaries in Singapore

In Singapore, data analysts can expect a competitive salary. The average salary depends on factors such as experience, the industry they work in, and the location within the city-state. But we can use the average salary level of data analysts last year as a rough reference:

• Entry-Level (0-2 years): S$4,500–S$4,500–S$6,500/month
  (Source: Michael Page Singapore Salary Benchmark 2024)


• Mid-Career (3-5 years): S$6,800–S$6,800–S$9,200/month
  (Source: Robert Half Tech Salary Guide 2024)


• Senior (5+ years): S$9,500–S$9,500–S$15,000/month
  (Source: EY Singapore Labour Market Report)

 

Factors affecting pay (experience, industry)

Experience is a major factor. More experienced data analysts command higher salaries. The industry also plays a role; for example, those in finance or technology may earn more. The following are the key factors that influence remuneration:

• 1. Skills: Python/SQL (+15-25% premium)
• 2. Certifications: AWS/Azure certs add ~S$1,000/month
• 3. SME vs MNC: MNCs pay 10-30% more

 

Best Data Analytics Courses

SIM's data analytics courses combine hands-on practice with essential theory, equipping you with valuable real-world skills. By earning a certificate or degree, you'll gain:

• Practical abilities in data collection, preparation, analysis and visualisation
• Career-ready expertise using current tools and methodologies
• Flexible study options with user-friendly online learning
• Dedicated support throughout your journey

With businesses increasingly relying on data-driven decisions, skilled analysts who can interpret and present insights are in high demand. Our graduates find rewarding roles across sectors like finance, healthcare, marketing and technology, making this qualification a smart investment for your future.

Conclusion

Data analytics is an exciting and rewarding field. With the right knowledge, skills, and tools, you can unlock valuable insights from data and make a real impact. Whether you're aiming for a new career or just want to understand data better, start exploring today!

 

FAQs

• 1. Is data analytics full of coding?

  Not entirely. While tools like Python and SQL help, many analysts use no-code platforms (Excel, Tableau). Coding becomes useful for complex tasks, but entry-level roles often focus on interpreting data rather than writing algorithms. Start with the basics, then learn coding if needed.

• 2. What is the difference between Data analytics and data analysis?

  Data analysis examines raw data to find trends. Data analytics applies those insights to solve problems. Think of analysis as "what happened?" and analytics as "what should we do?" Both overlap, but analytics is more business-focused.

• 3. How to get into data analytics?

  Begin with Excel and SQL, then learn visualisation (Power BI/Tableau). Take online courses (SIM E-Learning) or Singaporean programmes like SkillsFuture. Build a portfolio with real datasets. Apply for junior roles—many firms hire analysts without degrees if you show practical skills.