Python, SQL, Excel: Which Tools Should Aspiring Data Analysts Learn First?

Key Takeaways

• Excel is an accessible platform for cleaning, analysing, and visualising data without the need for coding.
• SQL builds on those basics, teaching you how to query and manage larger datasets directly from databases.
• Python expands your toolkit with advanced analysis, automation, and even machine learning capabilities.
• A progressive approach (Excel, then SQL, and finally Python) is recommended for those looking to learn data analytics.
• PSB Academy offers a range of analytics courses in Singapore to help learners progress from foundational concepts to advanced applications.

Which Data Analytics Tool is Your Real Career Starter?

Scroll through Netflix, check your Grab app, or review your monthly expenses in Excel—data is shaping your decisions every single day. It’s no wonder that more students and professionals are eager to get into the world of data analytics. But where should you begin?

If you’ve searched for the best data analytics tools to learn, you’ve probably seen endless debates online. Some may say Excel is enough, others swear by SQL, while Python fans argue it’s the only way forward. For beginners, this clash of opinions can feel overwhelming. But worry not—here’s a simple guide to help you get started.

Excel: The Essential Starting Point

For those starting their journey, especially those with no prior coding experience, Microsoft Excel offers a smooth and accessible entry into the world of data. While the spotlight today often falls on modern business intelligence tools like Power BI and Tableau, Excel is often used alongside these tools rather than in isolation.

While Excel doesn’t require coding knowledge, it can take time to fully master. Features such as advanced formulas, pivot tables, macros, and data visualisation options often stretch far beyond the basics, making it a tool that can grow alongside you during the transition to database or BI environments.

SQL: The Language of Databases

Once you've outgrown the limits of a single spreadsheet, it's time to learn Structured Query Language (SQL). It’s the language often used for communicating with, extracting from, and managing large relational databases.

Think of SQL as the search engine for data. Need to pull all sales from Q3? Want to analyse customer activity by region? With SQL, you can write queries that retrieve exactly the information you need. It’s particularly valuable in business intelligence and reporting roles, where working with raw, large-scale data is the norm.

SQL also works hand-in-hand with Excel or business intelligence (BI) platforms. It is primarily used to query and manipulate relational databases, such as PostgreSQL, MySQL, or SQL Server, which store structured datasets. You can use it to extract data from databases, then use Excel or tools like Tableau or Power BI to visualise it. For anyone serious about building essential skills for data analysts, SQL is a must-learn.

Python: The Power Tool for Analysis and Automation

If Excel is your starter kit and SQL your data access pass, Python can be seen as the all-in-one toolbox. Often regarded as one of the more versatile data analytics tools, Python opens the door to advanced analysis and automation, making it a natural step once you’re ready to move beyond the basics.

With Python, you can manage large datasets, perform statistical modelling, and even explore machine learning. Its libraries (Pandas, NumPy, and Matplotlib) also make it easier to clean data, run complex calculations, and build compelling visualisations.

Just as importantly, Python introduces programming concepts such as functions, loops, and libraries. These encourage a structured way of thinking, where problems are broken into smaller, repeatable steps. Over time, this shifts your role from simply analysing data to designing workflows and solutions that can scale with bigger challenges.

How to Choose (or Stack Them Together)

So, which tool do you start with? Don't think of it as an 'either/or' choice; think of it as a logical, progressive skill-stacking strategy.

1. Start with Excel: It's the typical entry point when it comes to data analytics for beginners, offering immediate, practical data manipulation skills.
2. Add SQL: Once you understand basic data concepts from Excel, SQL teaches you how to handle and retrieve data at scale.
3. Learn Python (or R): As your projects demand advanced statistics, automation, or machine learning, transition to a programming language.

This layered approach allows you to gain practical skills that are immediately useful while also building technical depth. In practice, however, this order can vary depending on your role.

Conclusion

Data analytics is one of the most exciting fields to step into today, whether you’re charting a new career path or simply adding digital fluency to your skill set. By learning tools like Excel, SQL, and Python, you can build a strong foundation that supports everything from basic data tasks to more advanced analysis and automation.

At PSB Academy, you’ll develop your data skills progressively. Our diploma programmes, like the Diploma in Business Analytics, introduce foundational tools like Excel, SPSS, and R programming. Following this, our analytics degree courses expand into SQL, Python, and machine learning, providing a structured pathway to help you grow confidently in the world of data.

Contact us to begin your journey into data analytics with PSB Academy today.