If you want to start a career in Data Analytics, one of the first questions you may have is: What should I learn first: Excel, SQL or Python?
All three are valuable skills for data professionals, but they serve different purposes. Excel is useful for organizing and analyzing data, SQL is essential for working with databases, and Python helps with advanced analysis, automation, and data science.
The right learning order can make your journey easier, especially if you are a beginner.
In most cases, a practical learning path is:
Excel → SQL → Python
However, the best order can depend on your career goal and current skill level.
Why Should You Learn Excel First?
For most beginners, Excel is a good starting point because it provides a simple way to understand how data works.
You can use Excel to clean, organize, calculate, filter, and visualize data without needing programming knowledge.
Important Excel skills for beginners.
- Data formatting and organization.
- Sorting and filtering.
- Basic and advanced formulas.
- VLOOKUP, XLOOKUP, INDEX and MATCH.
- IF, SUMIF, COUNTIF and related functions.
- Pivot Tables.
- Charts and dashboards.
- Data cleaning.
- Conditional formatting.
- Basic data analysis.
Excel also helps you understand important analytical concepts such as averages, percentages, trends, comparisons, and KPIs.
For students from commerce, management, finance, or non-technical backgrounds, Excel can be a comfortable first step into data analytics.
Why Learn SQL After Excel?
Once you understand how to work with data in Excel, SQL becomes easier to approach.
SQL, or Structured Query Language, is used to communicate with relational databases. Data analysts frequently use SQL to retrieve and analyze business data stored in databases.
For example, a company may have millions of customer transactions stored in a database. Instead of opening thousands of rows manually in Excel, an analyst can use SQL queries to retrieve exactly the information required.
Important SQL topics to learn.
- SELECT statements.
- WHERE conditions.
- ORDER BY.
- GROUP BY.
- Aggregate functions.
- CASE statements.
- JOINS.
- Subqueries.
- Common Table Expressions.
- Window functions.
- Date and string functions.
SQL is particularly important because real-world company data is often stored in databases rather than spreadsheets.
Why Learn Python After SQL?
Python becomes more useful once you already understand the basics of data analysis.
Python can help you work with larger datasets, automate repetitive tasks, perform advanced analysis, and build data science solutions.
For data analytics, some commonly used Python libraries include:
- Pandas for data manipulation.
- NumPy for numerical operations.
- Matplotlib for visualization.
- Seaborn for statistical visualization.
- Scikit-learn for machine learning.
You do not need to become an advanced programmer before starting data analytics. Beginners can start with basic Python concepts and gradually learn data-focused libraries.
Excel vs SQL vs Python
Each tool solves a different type of problem.
| Skill | Main Purpose | Difficulty for Beginners | Common Use |
|---|---|---|---|
| Excel | Spreadsheet analysis | Easy | Cleaning, calculations, dashboards |
| SQL | Database analysis | Moderate | Extracting and querying data |
| Python | Programming and advanced analysis | Moderate to High | Automation, analysis, data science |
Learning all three gives you a broader set of tools for working with data.
What Should You Learn First?
For someone completely new to data analytics, the following order is practical:
1. Start With Excel
Spend time understanding how data is structured and analyzed.
Learn formulas, Pivot Tables, charts, data cleaning, and basic dashboards. Try working with real-world datasets instead of only watching tutorials.
2. Move to SQL
After becoming comfortable with basic data analysis, start learning SQL.
Practice writing queries and solving business problems. Focus on understanding how tables are connected and how data can be filtered, grouped, and combined.
3. Learn Python
Once you have a foundation in Excel and SQL, begin Python.
Start with Python basics and then move into Pandas, NumPy, data visualization, and practical data analysis projects.
What If I Already Know Excel?
If you already have strong Excel skills, you do not need to spend months learning Excel again.
You can move directly to SQL and start building database skills.
After gaining confidence with SQL, begin Python.
Your path could look like:
Excel → SQL → Python → Power BI → Projects
What If I Want to Become a Data Analyst?
If your goal is to become a Data Analyst, learning Excel, SQL, Python, and a visualization tool can give you a strong technical foundation.
A practical roadmap can look like this:
Excel → SQL → Power BI/Tableau → Python → Projects → Interview Preparation
You should also develop business understanding and communication skills.
A good Data Analyst is not only someone who can write formulas or queries. They should also be able to understand a business problem, analyze data, identify useful insights, and explain those insights clearly.
What If I Want to Become a Data Scientist?
If your goal is Data Science, Python becomes much more important.
A possible learning path is:
Excel Basics → SQL → Python → Statistics → Machine Learning → Projects
Excel does not need to be your main focus if Data Science is your long-term goal, but basic spreadsheet skills can still be useful in professional environments.
Can I Skip Excel and Start With SQL?
Yes.
There is no technical rule that says you must learn Excel before SQL.
If you are comfortable with computers, numbers, and logical concepts, you can start with SQL directly.
However, Excel can make the early stages of data analysis easier because you can visually see the data and understand basic analysis concepts.
Can I Learn Python Before SQL?
Yes, but your learning experience may depend on your goal.
If you want to become a programmer or focus on Data Science, starting Python early can make sense.
For someone specifically targeting an entry-level Data Analyst role, learning SQL early is often practical because database querying is an important part of many analyst workflows.
The important thing is not only the order but how much practical work you do with each skill.
How Long Does It Take to Learn Excel, SQL and Python?
The learning time varies based on your previous experience and the amount of practice you do.
A beginner could structure their learning like this:
| Skill | Initial Learning Focus |
|---|---|
| Excel | Formulas, Pivot Tables, charts, dashboards |
| SQL | Queries, joins, aggregation, subqueries |
| Python | Basics, Pandas, NumPy, visualization |
| Power BI | Data modeling, dashboards, DAX |
Instead of focusing only on completing courses, try to build projects after learning each skill.
For example, you could create:
- An Excel sales dashboard.
- A SQL customer analysis project.
- A Python data-cleaning project.
- A Power BI business dashboard.
These projects help you understand how individual skills are applied to real problems.
Common Mistakes Beginners Should Avoid
Many beginners try to learn multiple technologies at the same time.
This can create unnecessary confusion.
Avoid these common mistakes:
- Learning five tools simultaneously.
- Watching tutorials without practicing.
- Memorizing SQL queries instead of understanding them.
- Learning Python syntax without working with data.
- Building projects only by copying tutorials.
- Ignoring statistics and business concepts.
- Waiting until you finish every course before creating projects.
A better approach is to learn one skill, practice it, and then use it in a small project.
A Simple Learning Roadmap
If you are starting from zero, this roadmap can help:
Step 1: Learn Excel fundamentals.
Step 2: Practice Excel with real datasets.
Step 3: Learn SQL fundamentals.
Step 4: Practice joins, aggregations, subqueries, and window functions.
Step 5: Learn Power BI or another visualization tool.
Step 6: Start Python for data analysis.
Step 7: Build 3–5 practical projects.
Step 8: Create a portfolio and resume.
Step 9: Practice Data Analyst interview questions.
This approach helps you gradually move from basic data handling to professional-level analysis.
Frequently Asked Questions
1. Should I learn Excel or SQL first?
For most complete beginners, Excel is a comfortable starting point because it introduces basic data analysis concepts. After that, SQL can help you work with data stored in databases.
2. Is Python necessary for a Data Analyst?
Python is not the only skill required for Data Analytics, and its importance varies by role. However, learning Python can help with automation, data cleaning, advanced analysis, and working with larger datasets.
3. Can I become a Data Analyst with Excel and SQL?
Yes. Excel and SQL provide an important foundation for Data Analytics. Adding a visualization tool such as Power BI or Tableau and developing practical project experience can further strengthen your skill set.
4. How long does it take to learn Excel, SQL and Python?
There is no fixed timeline. It depends on your previous knowledge, learning schedule, and practice. Consistent hands-on practice is generally more useful than simply completing tutorials quickly.
5. What should I learn after Excel, SQL and Python?
After learning these skills, you can focus on data visualization, statistics, data modeling, portfolio projects, and interview preparation. The next step should also depend on whether you want to pursue Data Analytics, Data Science, Business Analytics, or another career path.
