What is a Data Analyst?
A data analyst collects, cleans and studies data to help businesses make better decisions. They answer questions such as which products sell best, why customers leave or where the company should spend more money.
Data analysts work with spreadsheets, SQL, visualisation tools and sometimes Python. Just as important, they explain their findings clearly to people who are not technical.
Data analysts work in almost every industry, including retail, banking, healthcare, marketing and technology. That variety means you can often combine your analysis skills with an area you already know or enjoy, which makes you even more valuable to employers.
How to use this data analyst roadmap
Follow this data analyst roadmap step by step. Start with spreadsheets and SQL, because they are used in almost every data analyst job.
Use real, public datasets for your practice projects. Analysing topics you care about keeps you motivated and gives you interesting stories to share in interviews.
1Step 1: Spreadsheet skills
Excel and Google Sheets are still the most common tools for quick analysis.
What is it?
A spreadsheet organises data in rows and columns. Formulas calculate values automatically, and pivot tables summarise large tables in seconds, for example total sales by month.
What to learn
Many analysts use spreadsheets every day. Being fast and accurate here makes a great first impression.
Example
excel
=SUMIFS(D:D, B:B, "North", C:C, ">=2026-01-01")
=XLOOKUP(A2, Products!A:A, Products!C:C)The first formula totals North region sales since January, and the second looks up a product price.
Helpful resource: Microsoft Excel help and learning
2Step 2: Statistics basics
Understand the numbers behind your analysis so your conclusions are correct.
What is it?
Statistics helps you describe data and decide if a result is real or just chance. The mean is the average, the median is the middle value and correlation shows if two things move together.
What to learn
Statistics helps you avoid common mistakes, such as confusing correlation with cause.
Example
excel
=AVERAGE(B2:B100)
=MEDIAN(B2:B100)
=CORREL(B2:B100, C2:C100)Three quick formulas for the average, the middle value and the link between two columns.
Helpful resource: Khan Academy: Statistics and probability
3Step 3: SQL
SQL lets you pull exactly the data you need from company databases.
What is it?
SQL (Structured Query Language) asks questions of a database. You choose columns, filter rows, group results and join tables together.
What to learn
SQL is the most requested skill in data analyst job listings. Practise it until writing queries feels easy.
Example
sql
SELECT product, SUM(quantity) AS units
FROM sales
WHERE order_date >= '2026-01-01'
GROUP BY product
ORDER BY units DESC
LIMIT 5;Returns the five best-selling products this year.
Helpful resource: PostgreSQL tutorial
4Step 4: Data visualisation
Turn numbers into charts and dashboards that people understand quickly.
What is it?
Data visualisation turns numbers into charts and dashboards. Tools like Power BI and Tableau let you build interactive reports that managers can filter themselves.
What to learn
A simple, clear chart beats a complex one. Always ask what decision your chart should support.
Example
dax
Total Sales = SUM(Sales[Amount])
Sales YoY % = DIVIDE([Total Sales] - [Sales LY], [Sales LY])Two Power BI measures: total sales and growth compared with last year.
Helpful resource: Microsoft Learn: Power BI
5Step 5: Python for data analysis
Python helps you handle larger datasets and automate repeated work.
What is it?
Python helps analysts work with large files and repeat tasks automatically. Pandas is the main library for loading, cleaning and summarising data.
What to learn
You do not need to become a software engineer. Learn enough Python to clean, analyse and chart data. Our Python roadmap can help.
Example
python
import pandas as pd
df = pd.read_csv("sales.csv", parse_dates=["date"])
monthly = df.groupby(df["date"].dt.to_period("M"))["amount"].sum()
print(monthly)Load sales data and total it by month in a few lines.
Helpful resource: Pandas getting started
6Step 6: Data cleaning and quality
Real data is messy. Learn to fix it before you analyse it.
What is it?
Data cleaning fixes problems such as missing values, duplicates, wrong types and typos before analysis. Clean data makes your results trustworthy.
What to learn
Analysts often spend most of their time cleaning data. Doing it well makes every result more trustworthy.
Example
python
df = df.drop_duplicates()
df["city"] = df["city"].str.strip().str.title()
df["amount"] = df["amount"].fillna(0)Remove duplicate rows, tidy city names and fill missing amounts with zero.
Helpful resource: Pandas: Working with missing data
7Step 7: Business and communication skills
Understand what the business needs and explain your findings clearly.
What is it?
Business skills mean understanding what decision your analysis supports. KPIs (key performance indicators) are the few numbers a business uses to measure success, such as revenue, conversion rate or churn.
What to learn
Great analysts do not just share numbers. They explain what the numbers mean and what to do next.
Example
text
Question: Why did sales drop in March?
Data: sales by region, product, channel
Finding: online sales fell 18% after a price change
Action: test a smaller price increaseA simple structure for turning analysis into a clear recommendation. Use your own real figures.
Helpful resource: Storytelling with Data
8Step 8: Portfolio and job preparation
Show employers what you can do with real projects.
What is it?
A data portfolio shows how you turn questions into answers. Each case study should include the question, the data, your method, a chart and a recommendation.
What to learn
Each project should start with a business question and end with a clear answer.
Example
markdown
# Retail Sales Analysis
**Question:** Which products drive profit?
**Tools:** SQL, Power BI
**Insight:** 20% of products bring 70% of profit
**Link:** dashboard URLA short case study summary you can add to your portfolio.
Helpful resource: Find more career tips in our latest articles.
Data analyst roadmap project ideas
Projects turn what you learn into real skills. Build at least one project from each level below, and put every finished project on GitHub with a clear README.
Sales summary in Excel
Pivot tables and charts for a year of sales data.
SQL business questions
Answer 10 questions about an online store database.
Power BI dashboard
An interactive KPI dashboard with filters and drill-downs.
Customer churn analysis
Find why customers leave using Python and simple statistics.
A/B test report
Analyse an experiment and present a clear recommendation.
End-to-end data project
From raw data to cleaned dataset, SQL model and dashboard.
Tips to finish this data analyst roadmap
- Learn by building. Spend at least two thirds of your study time writing code and making projects, not watching videos.
- Be consistent. One focused hour every day beats a long session once a week. Small daily progress adds up quickly.
- Share your work. Post your projects on GitHub and LinkedIn. It builds your confidence and helps recruiters find you.
- Ask for feedback. Join a community, ask questions and review other people’s code. Explaining things to others is one of the fastest ways to learn.
- Do not chase every new tool. The fundamentals in this data analyst roadmap change slowly. Master them first, then explore new libraries.
Career paths and next steps
After this data analyst roadmap, you can apply for junior data analyst, business analyst or reporting analyst roles. Many analysts later move into data science, analytics engineering or management.
If you want to work with machine learning, continue with the AI engineer roadmap.
Related roadmaps
Data Analyst Roadmap FAQs
Do I need to know coding to be a data analyst?
You need SQL in almost every role. Python is very helpful and increasingly expected, but many analysts start with spreadsheets and SQL.
How long does it take to become a data analyst?
Most people can become job-ready in 4 to 6 months with regular practice.
Is Power BI or Tableau better to learn?
Both are popular. Check job listings in your area and learn the one that appears most often.
Is data analysis a good career in 2026?
Yes. Companies rely more on data than ever, and people who can turn data into clear decisions are in steady demand.