Once you become interested in data analytics, the first question is usually: "Which software do I need to learn?" Job listings mention Excel, SQL, Power BI, Tableau, Python, R and dozens of other tools, and that list can overwhelm a beginner. The good news is that most of the work is done with a handful of core tools. In this article we explain which tools a data analyst needs, what each one is used for and the most efficient order to learn them in.
For a general overview of the profession, see our article What Is Data Analytics and Who Is It For?.
1. Excel — the first step in analytics
Excel is still the most widely used analytics tool in the world. In many companies, daily reports, quick analyses and budget calculations are done in Excel. Key Excel skills for a data analyst:
- • Functions: SUMIFS, COUNTIFS, IF, XLOOKUP / VLOOKUP, INDEX-MATCH
- • Summarizing data with PivotTables and PivotCharts
- • Data cleaning: removing duplicates, text functions, filters
- • Loading and transforming data automatically with Power Query
- • Conditional formatting and basic charts
Strong Excel skills also make the next tools easier to learn, because thinking in tables is the foundation of analytics.
2. SQL — the language of databases
Company data is usually stored not in Excel files but in databases. To access it, you need SQL (Structured Query Language). SQL is one of the most requested skills in analyst job listings. What to learn:
- • Selecting and filtering data with SELECT, WHERE and ORDER BY
- • GROUP BY and aggregate functions (SUM, COUNT, AVG)
- • Combining tables with JOINs
- • Subqueries and CTEs
- • Window functions (ROW_NUMBER, RANK, LAG)
The great thing about SQL is its simple syntax: you can get your first results quickly. Starting with one system, such as MySQL, PostgreSQL or Microsoft SQL Server, is enough, since the core syntax is similar across all of them.
3. Power BI or Tableau — visualization
However accurate an analysis is, it has little value if management can't understand it. Visualization tools let you present results as interactive charts and dashboards.
- • Power BI — a Microsoft product that integrates well with Excel and is very widespread in corporate environments. Learn data modeling, table relationships, DAX formulas and dashboard design.
- • Tableau — known for powerful visualization capabilities and especially popular in international companies.
Choosing one is enough to start. Once you know one well, switching to the other is easy.
4. Python — the next level
Python isn't required at the start, but it takes an analyst to the next level. It is used for working with data volumes too large for Excel, automating repetitive tasks and running complex analyses. Core Python libraries for analytics:
- • Pandas — processing and analyzing tabular data
- • NumPy — numerical computing
- • Matplotlib and Seaborn — charts and plots
- • Jupyter Notebook — an environment for step-by-step analysis and documenting results
5. Statistics — not a tool, but essential
Statistics isn't software, but without it analytics stays superficial. Key concepts for a data analyst:
- • Mean, median, mode and standard deviation
- • Distributions and outliers
- • Correlation and how it differs from causation
- • Sampling and the reliability of conclusions
- • A/B testing basics
In what order should you learn them?
For beginners, the most efficient path looks roughly like this:
- 1. Excel (including PivotTables and Power Query)
- 2. Statistics fundamentals
- 3. SQL
- 4. Power BI or Tableau
- 5. Python (with Pandas)
The first four steps build a solid foundation for an entry-level data analyst role. Python gives you a competitive edge and room for career growth.
Tools aren't enough: other skills
Alongside technical tools, employers also look for:
- • Business understanding — knowing which numbers matter and why
- • Communication — explaining results clearly to non-technical people
- • Data storytelling — turning numbers into a logical story
- • Critical thinking — spotting errors in data and misleading conclusions
How to build a portfolio
For a candidate without experience, a portfolio is the strongest argument. Using open datasets (for example, from Kaggle), prepare a few projects:
- • A sales data analysis and report in Excel
- • A customer behavior analysis with SQL
- • An interactive dashboard in Power BI
- • Data cleaning and analysis in Python
For each project, briefly describe the question, the method you used and the result.
Frequently asked questions
Can I work as a data analyst knowing only Excel?
It's possible in some entry-level roles, but most job listings today also require SQL and a visualization tool. The Excel + SQL + Power BI combination opens many more doors.
Python or R?
Both work for analytics, but Python is more versatile, more widely used and has more learning resources. Python is recommended for beginners.
Power BI or Tableau?
Look at job listings from the companies you're targeting. Overall, Power BI is more widespread in corporate environments and feels more familiar to people coming from Excel.
Conclusion
You don't need to learn dozens of tools to become a data analyst. Excel, SQL, one visualization tool and statistics fundamentals give you a solid foundation, and Python takes you to the next level. What matters most is applying these tools to real data and learning to present your results clearly.
Want to learn these tools through hands-on projects? In the IDTech Academy Data Analytics course, you'll work with real data and build projects for your portfolio. Learn more about the course →