Excel vs SQL vs Python: What Should a Data Analyst Learn First?

If you are starting a career in data analytics, one of the first questions you will probably encounter is:

Should I learn Excel, SQL, or Python first?

All three are valuable data analytics skills, but they serve different purposes.

Excel is widely used for spreadsheet-based analysis, reporting, calculations, and business operations. SQL is essential for working with data stored in relational databases. Python is a powerful programming language used for data cleaning, analysis, automation, visualization, and more advanced analytics.

The challenge for beginners is not deciding which tool is “best.” The real question is understanding what each tool does, when you need it, and in what order you should learn it.

This guide explains the differences between Excel, SQL, and Python, what data analysts use each tool for, which skill to learn first, and how to build a practical learning roadmap.

Excel vs SQL vs Python: The Short Answer

For most beginners, a practical sequence is:

Excel → SQL → Python

This is not a universal rule. Your ideal order depends on your career goals, current experience, workplace, and the type of data you expect to work with.

A simplified view looks like this:

Skill	Best For	Difficulty for Beginners	Common Data Analytics Uses
Excel	Spreadsheet analysis	Beginner-friendly	Calculations, reporting, dashboards, data cleaning
SQL	Database querying	Beginner to intermediate	Extracting, filtering, joining, and aggregating data
Python	Advanced analysis and automation	Intermediate	Data cleaning, automation, visualization, statistical analysis

Excel helps you understand how data behaves.

SQL teaches you how to retrieve and manipulate structured data from databases.

Python gives you a programmable environment for performing more advanced analysis and automation.

Learning all three can create a strong foundation for a data analytics career.

Why Do Data Analysts Need Excel, SQL, and Python?

Data analysts rarely work with data in just one format.

A business might store sales transactions in a database, maintain operational reports in spreadsheets, and use Python for automated analysis.

A typical workflow might look like:

Database → SQL → Data extraction → Excel/Python → Analysis → Visualization → Business decision

For example, imagine an online retailer wants to understand why sales declined.

A data analyst might:

Use SQL to retrieve transaction data.
Use Excel to perform quick calculations and business checks.
Use Python to clean and analyze a larger dataset.
Create visualizations to identify patterns.
Present the findings to business stakeholders.

The tools complement each other rather than competing with one another.

What Is Excel Used for in Data Analytics?

Microsoft Excel is one of the most accessible and widely recognized tools for working with data.

It provides a spreadsheet environment where users can organize, calculate, analyze, visualize, and present information.

For many beginners, Excel is a useful starting point because the feedback is immediate.

You can enter data, apply a formula, create a chart, and see the result without needing to understand programming or database architecture.

Common Excel Tasks for Data Analysts

Data analysts may use Excel for:

Data cleaning
Calculations
Sorting and filtering
Pivot tables
Charts
Dashboards
Reporting
Data validation
Lookup operations
Basic statistical analysis
Ad hoc analysis
Financial modeling
Business reporting

Excel is particularly useful when working with relatively manageable datasets or when business teams already depend heavily on spreadsheets.

Excel Skills Every Beginner Should Learn

You do not need to learn every Excel feature before moving forward.

Start with the fundamentals.

1. Basic Formulas

Learn formulas such as:

SUM
AVERAGE
COUNT
MIN
MAX
IF

These help you perform common calculations and build simple analytical logic.

2. Lookup Functions

Learn how to retrieve information from related tables.

Common functions include:

XLOOKUP
VLOOKUP
INDEX
MATCH

Understanding how lookup operations work is particularly useful when combining information from different datasets.

3. Conditional Logic

Functions such as IF, IFS, AND, and OR help analysts create rules and classifications.

4. Text Functions

Data often contains inconsistent names, codes, or descriptions.

Useful functions include:

LEFT
RIGHT
MID
TRIM
TEXT
CONCAT

5. Date Functions

Analysts frequently work with dates.

Important concepts include:

Date differences
Month and year extraction
Week calculations
Date formatting
Time-based analysis

6. Pivot Tables

Pivot tables are one of the most useful Excel features for analyzing structured datasets.

They allow you to summarize information by categories, time periods, products, regions, customers, or other dimensions.

7. Charts

Learn how to choose appropriate visualizations for the question you are answering.

Common options include:

Bar charts
Line charts
Column charts
Scatter plots
Area charts

8. Data Cleaning

Learn to identify:

Duplicate records
Missing values
Inconsistent formatting
Incorrect data types
Unexpected values

These skills transfer directly to other analytics tools.

What Is SQL Used for in Data Analytics?

SQL, or Structured Query Language, is used to work with data stored in relational databases.

While Excel generally presents data in spreadsheets, SQL allows you to interact directly with structured databases.

This is one of the reasons SQL is such an important skill for professional data analysts.

Instead of manually opening a spreadsheet containing millions of records, an analyst can write a query that retrieves exactly the information required.

For example, a business might have millions of customer transactions stored in a database.

An analyst could use SQL to answer questions such as:

Which products generated the most revenue?
How many customers purchased in the last 30 days?
Which regions have declining sales?
What is the average order value?
Which customers have not purchased recently?

Why SQL Is So Important for Data Analysts

Modern organizations often store operational data in databases.

These databases may contain information about:

Customers
Orders
Products
Payments
Employees
Website activity
Marketing campaigns
Support tickets
Inventory
Financial transactions

SQL allows analysts to retrieve and transform this information.

A data analyst does not necessarily need to become a database administrator.

However, understanding how to query and manipulate data is extremely useful.

SQL Skills Every Beginner Should Learn

Start with the core concepts rather than trying to memorize hundreds of commands.

SELECT

The SELECT statement is used to retrieve data.

You should understand how to select specific columns and retrieve records from a table.

WHERE

WHERE allows you to filter records according to conditions.

For example, you might filter transactions belonging to a particular region.

ORDER BY

This allows you to sort results.

For example:

Highest revenue first
Newest customers first
Lowest transaction value first

GROUP BY

GROUP BY is essential for aggregation.

It allows you to calculate metrics by categories such as:

Product
Region
Customer segment
Month
Department

Aggregate Functions

Learn:

COUNT
SUM
AVG
MIN
MAX

These are fundamental to analytical queries.

JOINs

JOINs allow you to combine information from multiple tables.

Important JOIN types include:

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN

Understanding JOINs is one of the most important steps in becoming comfortable with real-world SQL.

CASE Statements

CASE allows analysts to create conditional logic within queries.

For example, you might classify customers into different segments based on their purchase amount.

Subqueries and CTEs

Once you understand the basics, learn:

Subqueries
Common Table Expressions
Window functions

These become increasingly useful for more complex analysis.

What Is Python Used for in Data Analytics?

Python is a general-purpose programming language widely used for data analysis, automation, machine learning, and data science.

Python becomes particularly useful when spreadsheet formulas or SQL queries alone are not enough.

It can help analysts automate repetitive tasks, process larger datasets, perform statistical analysis, and create reusable workflows.

Common Python Libraries for Data Analytics

You do not need to learn every Python library.

A practical beginner path includes several key libraries.

Pandas

Pandas is widely used for working with structured data.

It allows analysts to:

Load datasets
Filter rows
Select columns
Clean data
Group information
Combine datasets
Transform values
Calculate statistics

NumPy

NumPy provides tools for numerical computing and is commonly used alongside pandas.

Matplotlib

Matplotlib can be used to create data visualizations.

Seaborn

Seaborn provides higher-level statistical visualization capabilities and works closely with pandas.

Jupyter

Jupyter Notebook provides an interactive environment where analysts can combine code, explanations, calculations, and visualizations.

Python Skills Every Beginner Should Learn

Before jumping into data analysis libraries, learn basic Python programming.

Focus on:

Variables
Data types
Lists
Dictionaries
Conditions
Loops
Functions
Error handling
Reading files
Basic modules

You do not need to become an advanced software engineer before using Python for analytics.

The goal is to learn enough programming to manipulate data confidently.

Excel vs SQL vs Python: Key Differences

The biggest difference is the environment in which each tool is most useful.

Feature	Excel	SQL	Python
Main purpose	Spreadsheet analysis	Database querying	Programming and analysis
Beginner difficulty	Low	Moderate	Moderate
Programming required	No	Limited	Yes
Works with databases	Limited/direct connections	Yes	Yes, through libraries/connectors
Data cleaning	Yes	Yes	Yes
Data visualization	Yes	Limited	Yes
Automation	Yes, with advanced features	Limited	Excellent
Large datasets	Can become limiting	Excellent for databases	Excellent with appropriate workflows
Statistical analysis	Basic to moderate	Basic to moderate	Advanced
Machine learning	No	No	Yes
Reporting	Excellent	Usually through other tools	Possible
Business users	Very common	Common in data teams	More technical

These categories are broad. Actual capabilities depend on the specific tools, databases, libraries, and workflows being used.

Should a Data Analyst Learn Excel First?

For many beginners, Excel is a practical first step.

That does not mean every data analyst must master Excel before touching SQL or Python.

Excel can help you understand fundamental analytical concepts such as:

Rows and columns
Data types
Filtering
Sorting
Aggregation
Calculations
Relationships between variables
Charts
Basic reporting

These concepts become useful when you move into SQL and Python.

If you already have strong Excel skills, you can move directly into SQL.

Should You Learn SQL Before Python?

For many aspiring data analysts, SQL is a logical next step after basic Excel.

One reason is that SQL teaches you how to think about structured data.

You learn how to:

Select information
Filter records
Aggregate data
Group categories
Join datasets
Create analytical queries

These are core analytical operations.

Python can then extend those skills into more programmable and automated workflows.

However, if your career goal specifically involves Python-heavy analytics, automation, data science, or machine learning, learning Python earlier can also make sense.

When Should You Learn Python?

Python becomes especially useful when you want to move beyond basic spreadsheet analysis and database querying.

Consider learning Python when you want to:

Automate repetitive analysis
Process larger datasets
Perform advanced data cleaning
Conduct statistical analysis
Build reusable workflows
Work with APIs
Explore machine learning
Transition toward data science
Build AI-powered data applications

Python is not necessarily required for every data analyst role.

Some analyst positions may rely heavily on Excel, SQL, and business intelligence platforms.

The job description should therefore influence your learning priorities.

Excel vs SQL vs Python Based on Career Goals

Your ideal learning sequence can change depending on the role you want.

If You Want to Become a Business Data Analyst

A practical sequence could be:

Excel → SQL → Power BI/Tableau → Python

Business analysts often need strong reporting, visualization, SQL, and communication skills.

If You Want to Become a Product Analyst

Consider:

Excel → SQL → Product Analytics → Python

Product analysts often work heavily with event data, user behavior, funnels, retention, experimentation, and product metrics.

If You Want to Move Toward Data Science

Consider:

Python → Statistics → SQL → Machine Learning

Excel can still be useful, but Python becomes more central to the learning path.

If You Want to Work in Data Engineering

A different path may be more appropriate:

SQL → Python → Databases → Data Pipelines → Cloud/Data Platforms

The focus shifts toward data infrastructure and engineering rather than primarily business analysis.

What Should a Complete Beginner Learn First?

If you are completely new to data analytics, avoid trying to learn Excel, SQL, Python, Power BI, statistics, machine learning, and every other tool simultaneously.

A structured sequence is easier to manage.

Stage 1: Learn Data Fundamentals

Start with:

What is data?
Structured vs unstructured data
Rows and columns
Data types
Variables
Records
Metrics
Dimensions
KPIs

Stage 2: Learn Excel

Practice:

Formulas
Lookups
Sorting
Filtering
Pivot tables
Charts
Data cleaning

Stage 3: Learn SQL

Focus on:

SELECT
WHERE
ORDER BY
GROUP BY
Aggregations
JOINs
CASE
CTEs
Window functions

Stage 4: Learn Data Visualization

Explore tools such as:

Power BI
Tableau
Excel dashboards
Python visualization libraries

Stage 5: Learn Python

Focus on:

Python fundamentals
pandas
NumPy
Data cleaning
Visualization
Automation

Stage 6: Build Projects

Apply the skills to real datasets.

This is where knowledge starts becoming practical capability.

How Long Does It Take to Learn Excel, SQL, and Python?

There is no universal timeline because learning speed depends on your background, available time, and target role.

A beginner might structure learning approximately like this:

Stage	Focus
Weeks 1–3	Data fundamentals + Excel
Weeks 4–7	SQL fundamentals
Weeks 8–10	Data visualization
Weeks 11–15	Python fundamentals
Weeks 16–20	pandas + analytics projects
Weeks 21+	Portfolio + specialization

The objective should not be to finish a course as quickly as possible.

The more useful milestone is being able to solve realistic analytical problems independently.

How to Practice Excel, SQL, and Python Together

One of the best ways to learn these tools is to solve the same analytical problem using different technologies.

Imagine you have an e-commerce sales dataset.

You could use Excel to:

Calculate total revenue
Create a pivot table
Identify top-selling products
Build a dashboard

Then use SQL to:

Query customer transactions
Join customers with orders
Calculate revenue by region
Analyze monthly sales

Then use Python to:

Clean the raw dataset
Detect unusual values
Automate the analysis
Create additional visualizations

This approach helps you understand that the tools are different ways of solving related problems.

A Practical Data Analytics Project

Suppose you are analyzing an online store.

Your dataset contains:

Order ID
Customer ID
Product
Category
Region
Order date
Quantity
Revenue
Profit

You could build a complete project around five questions:

Question 1: Which products generate the most revenue?

Use Excel or SQL to calculate revenue by product.

Question 2: Which regions are performing best?

Group the data by region.

Question 3: How are sales changing over time?

Analyze monthly revenue trends.

Question 4: Which products have high sales but low profitability?

Compare revenue and profit.

Question 5: What recommendations can the business consider?

Translate the findings into business insights.

The final project demonstrates more than tool knowledge.

It demonstrates analytical thinking.

Which Is More Important: Tools or Analytical Thinking?

Tools matter, but tools alone do not make someone a strong data analyst.

Imagine two analysts.

Analyst A knows dozens of SQL commands but does not understand the business question.

Analyst B knows fewer SQL features but can clearly identify the problem, select appropriate metrics, analyze the data, and explain the results.

Tool knowledge is important, but the ability to ask the right questions and interpret results correctly is equally important.

A strong data analyst develops:

Analytical thinking
Business understanding
Data literacy
Problem-solving
Communication
Data visualization
Technical skills

Excel, SQL, and Python are tools that support these capabilities.

Do Data Analysts Need All Three?

Not necessarily.

The required technology stack varies by company and role.

Some analysts may primarily use:

Excel + Power BI

Others may use:

SQL + Power BI

A more technical analytics team might use:

SQL + Python + cloud data platforms

Some organizations may use:

SQL + Tableau + spreadsheets

Therefore, do not assume that you must master every tool before applying for an entry-level data role.

Instead, review job descriptions and identify recurring requirements.

How to Decide What to Learn First

Use this simple framework.

Choose Excel First If:

You are completely new to data
You work heavily with spreadsheets
You want to understand basic analysis
You need reporting skills
You are transitioning from a non-technical background

Prioritize SQL If:

You want to work with databases
You are targeting data analyst roles
You want to work with business data at scale
Job descriptions frequently mention SQL
You already know Excel

Prioritize Python If:

You want to automate analysis
You are interested in data science
You want to work with machine learning
You enjoy programming
You want to build more advanced analytical workflows

Common Mistakes Beginners Make

Trying to Master Everything at Once

Learning multiple tools simultaneously can create unnecessary confusion.

Build depth in one skill before adding another.

Focusing Only on Syntax

Knowing formulas, SQL commands, or Python syntax is not enough.

Learn why and when to use them.

Avoiding Real Datasets

Tutorial exercises are useful, but real datasets introduce messy values, missing information, inconsistent formats, and ambiguous questions.

Building Dashboards Without Questions

A dashboard should answer meaningful business questions.

Do not create charts simply because the tool allows you to.

Ignoring Communication

Data analysts often need to explain findings to people who are not data specialists.

Practice explaining your results in simple language.

Learning Tools Without Understanding Data

Before becoming highly technical, understand:

Data types
Metrics
Dimensions
Aggregations
Relationships
Trends
Outliers
Missing values

These concepts apply across tools.

How to Build a Data Analytics Portfolio

Your portfolio should demonstrate how you solve problems, not simply list the software you know.

A strong project can include:

1. Business Problem

Explain what you are trying to understand.

2. Dataset

Describe the data and its limitations.

3. Data Cleaning

Explain what you changed and why.

4. Analysis

Show the calculations, queries, or code used.

5. Visualization

Present the most important findings clearly.

6. Insights

Explain what the analysis reveals.

7. Recommendations

Where appropriate, explain what a business could investigate or consider next.

A portfolio project that combines SQL + Python + visualization can demonstrate a broad range of skills.

However, a well-executed Excel project can also be valuable, especially for roles where spreadsheet analysis is central.

Should You Learn Power BI or Tableau Too?

Yes, if your target roles frequently request business intelligence or visualization skills.

Power BI and Tableau are different from Excel, SQL, and Python because their primary focus is often on data visualization, dashboards, reporting, and business intelligence.

A common learning path might therefore be:

Excel → SQL → Power BI → Python

This is particularly practical for beginners targeting business-focused data analyst roles.

How AI Is Changing Data Analytics Skills

AI tools are increasingly being used to assist with:

SQL generation
Formula creation
Python code
Data cleaning
Documentation
Data exploration
Chart suggestions
Summarization

This does not eliminate the need to understand Excel, SQL, or Python.

In fact, understanding the underlying tools makes it easier to review AI-generated work and identify mistakes.

For example, if an AI system generates an SQL query, an analyst should still be able to determine whether:

The correct tables were used
The JOIN logic is correct
Filters are appropriate
Aggregations are valid
Duplicate records could distort the results

AI can accelerate analytical work, but verification and judgment remain important.

A 90-Day Excel, SQL, and Python Learning Plan

If you want a structured starting point, consider this approach.

Days 1–30: Excel and Data Fundamentals

Learn:

Data types
Basic formulas
IF logic
Lookups
Sorting and filtering
Pivot tables
Charts
Data cleaning

Complete at least one small project.

Days 31–60: SQL

Learn:

SELECT
WHERE
ORDER BY
GROUP BY
Aggregations
JOINs
CASE
CTEs
Basic window functions

Practice answering business questions with SQL.

Days 61–90: Python

Learn:

Python basics
pandas
NumPy
Data cleaning
Basic visualization
Reading CSV/Excel files
Grouping and aggregation

Finish a project that combines Python with a real dataset.

A Simple Learning Roadmap

If you want the shortest practical roadmap, use this:

Step 1: Learn data fundamentals.

Step 2: Build strong Excel fundamentals.

Step 3: Learn SQL for database analysis.

Step 4: Learn a visualization platform such as Power BI or Tableau.

Step 5: Learn Python for automation and advanced analysis.

Step 6: Build three to five practical projects.

Step 7: Create a portfolio.

Step 8: Tailor your resume to data analyst job descriptions.

Step 9: Practice SQL, analytical, and business interview questions.

Step 10: Continue developing skills based on the type of analytics role you want.

Excel vs SQL vs Python: Which One Should You Learn First?

There is no single sequence that applies to every learner.

For someone starting from scratch, Excel is often the easiest entry point because it provides an accessible environment for learning fundamental data concepts.

After that, SQL is a strong next skill for learning how to retrieve and analyze structured data stored in databases.

Then Python can expand your capabilities into automation, advanced analysis, programming, and data science.

A practical progression for many beginners is:

Excel → SQL → Visualization → Python

But if you already have strong Excel skills, start with SQL. If your goal is data science or programming-heavy analytics, Python may deserve earlier attention.

The best learning order is ultimately the one that matches the roles you want to pursue.

Frequently Asked Questions

Is Excel or SQL better for a data analyst?

They serve different purposes. Excel is particularly useful for spreadsheet analysis, reporting, calculations, and quick business analysis, while SQL is designed for querying and manipulating data stored in databases. Many data analysts benefit from knowing both.

Should I learn SQL or Python first?

For many aspiring data analysts, SQL is a practical first technical skill after Excel. Python can follow for automation and more advanced analysis. However, people targeting data science or programming-heavy roles may choose to learn Python earlier.

Can I become a data analyst with Excel only?

Excel can be sufficient for some spreadsheet-heavy analytical roles, but many professional data analyst positions also require SQL and business intelligence tools. Review the requirements of the roles you want to target.

Is Python necessary for data analytics?

Python is valuable but not required for every data analyst role. Its importance depends on the organization, role, datasets, and level of automation or advanced analysis involved.

How much Excel should a data analyst know?

You should be comfortable with formulas, lookups, filtering, sorting, pivot tables, data cleaning, charts, and basic analytical functions. Advanced Excel skills can be developed as your role requires them.

How much SQL should a beginner data analyst learn?

Start with SELECT, WHERE, ORDER BY, GROUP BY, aggregate functions, JOINs, CASE statements, subqueries, and CTEs. Once comfortable, learn window functions and more advanced querying techniques.

Can a non-technical person learn Excel, SQL, and Python?

Yes. A non-technical background does not prevent you from learning these tools. Start with data fundamentals and Excel, then gradually move into SQL and Python through practical exercises and projects.

Can AI replace the need to learn Excel, SQL, and Python?

AI can assist with formulas, queries, and code, but understanding the underlying tools remains important. Analysts need to verify outputs, understand business context, identify errors, and make appropriate analytical decisions.

Which skill is most important for a data analyst?

Technical tools are important, but effective data analysts also need analytical thinking, business understanding, communication, data visualization, and problem-solving skills. The right combination depends on the role.

Final Thoughts

Excel, SQL, and Python are not competing technologies. They are complementary skills that support different parts of the data analytics workflow.

Excel provides an accessible foundation for spreadsheet analysis and business reporting.

SQL helps you work directly with structured data stored in databases.

Python gives you a flexible programming environment for automation, advanced analysis, and more technical data workflows.

If you are starting from scratch, a practical sequence is:

Learn Excel → Learn SQL → Learn data visualization → Learn Python → Build projects.

But do not measure your progress by how many tools you can list on a resume.

Measure it by whether you can take a real business question, work with imperfect data, analyze it correctly, communicate what you discovered, and explain why the result matters.

That is the foundation of effective data analytics.

jack@jacksonrobin.com

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