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More from Academy Xi
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With so much talk about data transforming the way we work and live, especially with the emergence of AI, data analytics and data science have become highly sought-after careers.
Both fields open doors to exciting job prospects, high salaries, and the chance to make a real impact. But if you’re exploring career opportunities in these disciplines, you might be wondering: what’s the difference between data analytics and data science? And how do you decide which one is the right fit for your skills, goals, and interests?
In this guide, we’ll break down what each path involves, covering typical responsibilities, required skills, career options, salaries, and helpful tips to help you make the best choice.
What is data analytics?
Data analytics is all about understanding and interpreting data to answer specific questions, spot trends, and support business decisions. Most of the time, data analysts focus on historical data. They’re often looking at what happened in the past to help organisations extract meaningful insights and make smarter choices now.
Typical responsibilities of a data analyst
Data analysts are responsible for tasks such as:
- Data cleaning and preparation: Ensuring data accuracy by organising and cleaning data for analysis.
- Spotting trends and patterns: Analysing historical data and past performance to see what works and what doesn’t, which can inform future business strategies.
- Creating reports and dashboards: Putting together easy-to-read visuals such as charts, graphs and dashboards to share insights with different audiences.
- Translating data into actionable insights: Turning complex data findings into visualisations and learnings that can help guide company decisions.
You’ll find data analysts in nearly every industry, from finance to marketing to healthcare, making it a flexible role with many options. Data analyst roles are very versatile as companies will always need them to understand their performance and make informed decisions a lot of the time.
Key skills for data analysts to have
To thrive as a data analyst, these are some go-to skills that data analysts must hone:
- Data visualisation: Knowing how to present data visually to make it easier to understand, often using tools like Tableau or Power BI.
- Statistical analysis: Understanding basic statistics and data interpretation to back up findings.
- SQL proficiency: Because SQL is the language for working with databases, it’s a must-have skill for querying and managing them.
- Communication: Being able to explain data insights clearly to stakeholders, especially for those in non-technical positions, is important so that you can help assist in quick and effective decision-making.
Common tools used by data analysts
There’s a mix of familiar and specialised tools that data analysts rely on to carry out their tasks, including:
- Microsoft Excel: For data organisation, analysis, and basic visualisations.
- SQL: For querying and managing relational databases.
- Tableau and Power BI: Popular tools for data visualisation and reporting.
- Python (pandas library): Used for data manipulation and analysis when more complex tasks are required.
Popular data analytics careers
Data analysts often start in entry-level roles and can move into specialised fields like business intelligence (BI), data engineering, or even data science over time.
Common roles in data analytics include:
- Data Analyst
- Business Analyst
- Operations Analyst
- Marketing Analyst
- Financial Analyst
As they gain experience, data analysts may transition into higher managerial roles, such as Analytics Manager or BI Manager.
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What is data science?
On the other hand, data science is a broader, interdisciplinary field that includes data analytics as one of its components. Data scientists focus on exploring data through mathematical models, algorithms, and advanced statistical methods.
You could say that data science takes data analysis to a whole other level by combining statistical analysis, programming, and machine learning to make predictions and uncover insights from massive and complex datasets. It also uses AI to automate certain processes.
Typical responsibilities of a data scientist
A data scientist will often perform the following tasks in their day-to-day roles:
- Data mining: Extracting valuable insights from structured and unstructured data sources.
- Predictive modelling: Developing models that predict future behaviours, trends, or outcomes.
- Machine learning: Training algorithms to learn from data, recognise patterns and improve predictions over time.
- Big data processing: Handling and analysing large datasets using advanced tools.
Data scientists often work in industries that are data-heavy, like tech, finance, e-commerce and healthcare, where AI and predictive modelling can have a big impact. Their duties involve building machine learning models to automate processes, forecast trends, and improve customer experiences.
Key skills for data scientists to have
Data scientists need a mix of technical and analytical skills, including:
- Programming: Proficiency in Python or R, both essential for data manipulation and model building, is highly desirable.
- Machine learning knowledge: Understanding how machine learning algorithms work and how to apply them is particularly important.
- Big data skills: Familiarity with big data tools like Hadoop and Spark to work with large datasets.
- Mathematics and statistics: Having a strong foundation in mathematics, especially in probability, linear algebra and statistics.
Common tools used by data scientists
Compared to data analysts, data scientists have their own set of powerful tools:
- Python and R: These are the go-to programming languages commonly used for data analysis and model development.
- TensorFlow and PyTorch: Libraries for building and training machine learning models.
- Hadoop and Spark: Big data tools for managing and processing large datasets.
- Jupyter Notebooks: An interactive coding environment that is perfect for creating data science projects and presentations.
Popular data science careers
Data scientists, due to their specialised skill set, often pursue more technical roles within machine learning or AI. Common roles include:
- Data Scientist
- Machine Learning Engineer
- AI Specialist
- Big Data Engineer
- Quantitative Analyst
Meanwhile, experienced data scientists may move into roles like Lead Data Scientist, Head of AI, or even research-focused positions in AI development.
Key differences between data analysts and data scientists
Though they have overlapping functions, data analytics and data science differ in focus, data types, skills required, and overall goals.
One major difference is that data analytics often helps interpret the past to inform the present, while data science uses advanced tools to look ahead and drive future decisions. Here’s some other key traits that distinguish the two:
- Focus and goals: Whereas data analysts work with historical data to identify trends, answer questions, and support data-driven decisions, data scientists look ahead to predict future behaviours, trends, and outcomes. They use models and algorithms to discover patterns, automate processes, and drive innovation.
- Data types: Data analysts work mainly with structured data (like databases or spreadsheets), which is easier to manage with tools like SQL and Excel. In contrast, data scientists deal with structured, semi-structured, and unstructured data (text, images, video), often requiring big data tools like Hadoop and Spark to manage complex datasets.
- Different skill sets: In data analytics, you’ll often be involved in SQL, data visualisation, and statistical analysis, using tools such as Tableau, Power BI, and Excel. Meanwhile, data science requires programming skills, machine learning, data mining, and big data processing.
- End-user impact: A data analyst will deliver insights that guide daily business decisions, improving departmental efficiency. On the other hand, a data scientist will develop models that inform long-term strategies, product innovations, and business solutions through automation and predictive insights.
Salaries: Data analytics vs. data science
On average, salaries for data science roles generally outpace those for data analytics because of the higher level of technical skill required.
However, it’s also worth noting that In some high-stakes industries, like finance or healthcare, data analysts with specialised skills (e.g. risk analysis in finance or clinical data analysis in healthcare) can command high salaries that rival even those of data scientists.
Depending on experience and seniority, highly experienced data analysts with years of expertise, especially in management or specialised roles, can often earn more than junior or entry-level data scientists.
According to Glassdoor, here’s a rough breakdown of an average salary from these careers in Australia as of 2024:
- Data Analyst: $80,000 – $107,000
- Business Analyst:$90,000 – $120,000
- Machine Learning Engineer: $95,000 – $140,000
- Data Scientist: $100,000 – $141,000
Note: Salaries can also vary based on location, experience, and industry.
Choosing between data analytics and data science
If you’re trying to decide between data analytics and data science, consider the following factors:
1. Background and interests: What best aligns with your skillset?
If you enjoy statistics, analysing trends, or working with tools like Excel, data analytics could be a good fit. However, if you’re more interested in programming, algorithms, and machine learning, data science would probably be a more enjoyable and suitable path for you.
2. Learning journey: How long will it take me to learn?
Data analytics is often quicker to learn, focusing on tools like SQL and Excel. In comparison, data science takes longer to master because of its highly technical nature, requiring knowledge in machine learning, Python, and more advanced math.
3. Industry demand: Are employers looking to hire in this area?
In just about every industry, data analysts are needed to extract insights and identify trends. Although data scientists are also valuable across the board, they are particularly needed in the tech, finance, healthcare, and AI-driven industries because of the complex data being dealt with on an everyday basis.
4. Long-term growth: Is there potential for me to explore other career pathways?
Data analytics offers many opportunities for growth, especially into business intelligence and data engineering. Data science can open doors to specialised roles in machine learning and AI development.
How you can get started in a data analytics or data science career
Whether you choose data analytics or data science, it’s important that you’re taking steps in the right direction to kickstart your journey in either of these fields. Here’s a quick guide to help you get started:
1. Build foundational data skills
Start by learning the basics of Excel, SQL, and data visualisation for data analytics. For data science, dive into Python or R and brush up on statistics and linear algebra.
2. Take online courses or enrol in workshops
Look for beginner-friendly courses. Platforms like Coursera and Udemy offer programs tailored to both data analytics and data science.
At Academy Xi, we also offer self-paced and comprehensive Data Analytics courses that can help you build practical skills in data analytics and allow you to work on hands-on projects while boosting your career prospects. Furthermore, if you’re seeking training for your team or organisation, our Data Analytics Workshop empowers your workforce with data skills that drive growth and innovation.
3. Build a portfolio and work on real-world projects
You can start to build a portfolio of personal projects by applying your skills in small, real-world projects that can be shared with prospective employers. For instance, if you’re in data analytics, create data reports and build dashboards. For data science, you can try building a simple machine learning model. Showcase your work on GitHub or LinkedIn which can be the basis of a solid marketable portfolio.
4. Explore internship, volunteer or project opportunities
Pursuing internships or project-based opportunities to apply your skills in real-world settings can be another fantastic way to gain experience. They’ll help you learn how data is used in business and may even lead to full-time roles down the track.
5. Join data communities and network with like-minded professionals
Connect with others by joining data communities on LinkedIn, Reddit, or local meetups. These networks can offer support, feedback on projects, and even potential job leads. Another tip we recommend is attending webinars and participating in niche forums to connect with professionals in your field.
Starting a career in data is about consistent learning and hands-on practice. Whether you’re interested in analysing trends or building predictive models, dive in and explore where data can take you.
Which career path is right for you?
Both data analytics and data science offer rewarding career paths. If you enjoy working with data to drive business decisions, data analytics might be perfect. But if you’re drawn to programming, machine learning, and AI, data science could be your ideal path.
Ultimately, the decision comes down to what interests you the most. Take some time to explore, try out some beginner projects, and see which field feels like the best fit for your strengths and goals. Whatever you choose, you’ll be stepping into a field that’s dynamic, in-demand, and packed with potential.
How we can help upskill you in data analytics
At Academy Xi, we help you become future-ready with courses and workshops designed to level up your digital skills, including data analytics.
If you’re interested in transforming your data capabilities with tailored training, our Data Analytics Courses can help enhance your ability to harness data that can be applied in your career or wider business.
Organisations can also access our Data Analytics Workshop to equip your employees with essential data skills that lead to better efficiency and strategic decisions.
Reach out to our team at enterprise@academyxi.com or book a call with our team to see how we can help you navigate the world of data.
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