As the world becomes more data-driven, students are exploring tools that can give them an edge in analytics, research, and business. But one common question arises—should you learn Excel, Python, or SQL first?

The global big data analytics market is expected to rise from $70.7 billion in 2024 to $200 billion by 2035. This growth is fuelling demand for professionals who can turn raw data into actionable insights—and these three tools are foundational.

Let’s break down their roles, strengths, and help you decide where to begin.

1. Excel: The Perfect Starting Point

Excel is the most accessible and widely used tool for beginners. It’s great for:

  • Sorting and filtering datasets
  • Creating charts and pivot tables
  • Performing basic statistical functions
  • Exploring small to medium-sized data

Excel is often the first step in any data analytics journey. It’s user-friendly and helps you understand concepts like rows, columns, and data cleaning before moving on to programming.

Best for: Non-tech students, business analysis, or beginners exploring analytics.

2. SQL: Mastering Databases

SQL (Structured Query Language) helps you extract and manipulate data from databases. It’s essential because almost every organisation stores data in cloud databases.

With cloud computing adoption rising, understanding how to query cloud-based data is a must-have skill.

SQL helps you:

  • Access and manage large datasets
  • Write queries to filter, sort, and summarise data
  • Work directly with data stored in the cloud

Best for: Students aiming for analyst roles, backend data access, or cloud-based analytics.

3. Python: Automating and Advancing

Python is a powerful programming language used in advanced analytics, automation, and machine learning. Once you’re comfortable with data basics, Python lets you:

  • Perform deep data analysis
  • Automate repetitive tasks
  • Build models for forecasting and classification
  • Work with libraries like Pandas, NumPy, Matplotlib

Python is essential if you're considering data science, AI, or large-scale analytics.

Best for: Students with some coding interest aiming for data science or technical analytics roles.

So, Which One First?

Start with Excel if you're a beginner. Move to SQL for database management. Then pick up Python for advanced skills. Each tool builds on the last, and together they form a strong foundation for a data analyst career.

Why Cloud Computing Matters

As cloud computing grows, the ability to analyse cloud-stored data becomes crucial. Platforms like AWS, Azure, and Google Cloud support SQL, Python, and Excel integrations. That’s why data professionals today need cloud-ready skills alongside analytics expertise.

Learn Data Analytics with FutureSkills Prime

FutureSkills Prime offers a comprehensive data analytics course designed by industry experts. It includes:

  • Step-by-step learning for Excel, SQL, and Python
  • Real-world projects and cloud integration
  • Government-recognised certifications
  • Flexibility to learn at your own pace

Whether you're new or transitioning into tech, FutureSkills Prime helps you build future-ready data skills.

FAQs

Q1. Is Excel still relevant in data analytics?

Absolutely. It’s great for exploring data quickly and is used in nearly every industry for basic reporting.

Q2. Why should I learn SQL before Python?

SQL is simpler and helps you work with large datasets stored in databases. It’s often required in data analyst roles.

Q3. How is cloud computing related to data skills?

Most data today is stored in the cloud. Knowing how to analyse it using SQL or Python is essential.

Q4. Can I learn all three together?

Yes, but it’s best to learn them in this order: Excel → SQL → Python for a smooth learning curve.

Q5. What does the FutureSkills Prime course include?

It covers Excel, SQL, and Python for data analytics with practical examples, expert instruction, and certification.