Data Analyst
Make sense of data and help teams decide what to do
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Jobs this leads to
- Data Analyst
- Business Analyst
- Marketing Analyst
- Reporting Analyst
Before you start
- ✓ Comfort with spreadsheets
- ✓ Basic math skills
13 missions to Data Analyst
5 worlds, 13 missions. Tap any circle to open it; the glowing one is next.
Most analysts start here, and so should you.
SQL shows up in almost every data analyst job posting.
Turn your data into dashboards people can actually read.
Add Python and statistics to your toolkit.
Assemble a few projects that show what you can do.
More about this career
About the job, pay where you live, questions people ask, and where this career can take you next.
About the job
What you’d do
- Produce standard or custom reports that summarise business, financial or economic data
- Build and maintain dashboards, databases and other business intelligence tools
- Collect data from industry reports, public information and other sources
- Spot industry or regional trends that matter for business strategy
- Pull trend data together to support recommendations for action
Where you’d work
- Professional, scientific and technical services firms
- Finance and insurance companies
Every step in full
The complete text of every step, to read or print. Work through them in the journey above.
Excel & Spreadsheets 3 steps
Advanced Formulas & Data Retrieval
Mastering the programmatic side of spreadsheets to lookup, aggregate, and calculate values efficiently without manual entry.
Real-world task: Given a massive, unformatted raw export of sales data, use XLOOKUP and SUMIFS to create a summary table answering 'Total revenue per region'. Use IFERROR to catch missing regions gracefully.
Why it matters: Companies run on spreadsheets. Being the person who can instantly retrieve cross-referenced data using purely formulas will make you invaluable in your first week.
Checklist
- Learn lookups: VLOOKUP, HLOOKUP, INDEX/MATCH, and XLOOKUP
- Master logical statements (IF, AND, OR, nested IFs)
- Learn statistical functions (COUNTIFS, SUMIFS, AVERAGEIFS)
- Understand absolute vs. relative cell referencing ($A$1 vs A1)
- Practice error handling (IFERROR) to keep sheets functional
Data Cleaning & Organization
Real-world data is dirty. You need to standardize formats, handle blanks, and split strings before any real analysis can happen.
Real-world task: Take a dataset intentionally corrupted with trailing spaces, varied date formats (MM/DD/YY vs DD-MM-YYYY), and inconsistent capitalization. Write formulas to clean it into a pristine, unified format without manually retyping values.
Why it matters: If your data is bad, your analysis is wrong. You'll spend more time standardizing messy data extracts than making charts, guaranteed.
Checklist
- Use Text-to-Columns and string manipulation (LEFT, RIGHT, MID, CONCATENATE)
- Remove duplicates and standardize text casing (PROPER, UPPER, LOWER)
- Learn how to handle empty cells and NA values logically
- Understand Data Validation to restrict inputs going forward
- Format data as Tables for dynamic range referencing
Pivot Tables & Basic Dashboarding
The fastest way to aggregate, slice, and present spreadsheet data to stakeholders without writing code.
Real-world task: Build a one-page interactive 'Executive Dashboard' in Excel tracking Monthly Key Performance Indicators (KPIs). Include slicers so a manager can filter the metrics by department or quarter.
Why it matters: Stakeholders don't want to see 10,000 rows of raw data. They want a dashboard they can click around in to get immediate answers.
Checklist
- Create Dynamic Pivot Tables from structured Data Tables
- Use Calculated Fields within a Pivot Table
- Add Slicers and Timelines for interactive filtering
- Build basic visual charts (Bar, Line, Scatter) from Pivot data
- Design a clean layout avoiding 'spreadsheet clutter'
SQL & Databases 3 steps
Data Retrieval & Filtering
Querying exactly the rows you need out of massive databases securely and efficiently.
Real-world task: Query a sample 'Customers' database table. Retrieve the top 50 customers living in a specific state who signed up in the last year, ordered by their lifetime value.
Why it matters: You will rarely be analyzing data stored locally. You have to query the company's central database to pull just to the sandbox data you need before analyzing it.
Checklist
- Write basic syntax: SELECT, FROM, WHERE, LIMIT
- Filter accurately using AND/OR, IN, BETWEEN, and LIKE for text patterns
- Order results using ORDER BY (ASC, DESC)
- Understand basic data types (VARCHAR, INT, DATE)
- Learn to alias tables and columns (AS) for readability
Aggregation & Multi-Table JOINs
Combining data scattered across different tables (e.g., matching a Users table with an Orders table).
Real-world task: Join three tables: 'Orders', 'Products', and 'Users'. Write a query to find the top 5 product categories generating the most revenue, but only include users who registered in Q3.
Why it matters: Relational databases are normalized (split apart) for storage efficiency. You have to know how to piece the puzzle back together to see the whole picture.
Checklist
- Master GROUP BY and aggregate functions (COUNT, SUM, AVG, MAX)
- Filter aggregated data using the HAVING clause
- Understand the differences between INNER, LEFT, RIGHT, and FULL OUTER JOINs
- Write multi-condition JOINs handling null values
- Use UNION to vertically stack query results
Advanced SQL: CTEs & Window Functions
Level up from writing scripts to engineering multi-step data transformations.
Real-world task: Write a query utilizing CTEs and a Window Function to calculate the month-over-month revenue growth percentage for a generic e-commerce store, partitioned by product category.
Why it matters: Window functions are the #1 way analysts separate themselves from beginners in technical interviews. They make complex time-series analysis trivial natively in SQL.
Checklist
- Use Common Table Expressions (WITH clause) to string together logical steps
- Learn Window Functions over partitions (ROW_NUMBER(), RANK(), DENSE_RANK())
- Calculate running totals and moving averages (SUM() OVER (...))
- Use LEAD() and LAG() to compare period-over-period data
- Familiarize yourself with Subqueries and when NOT to use them (performance)
Data Visualization 2 steps
Business Intelligence (BI) Fundamentals
Learning how to navigate and build within enterprise BI tools like Tableau or PowerBI.
Real-world task: Connect Tableau Public (or PowerBI Desktop) to a regional COVID-19 dataset. Replicate standard metrics: total cases (KPI card), cases over time (Line chart), and cases by district (Bar chart).
Why it matters: No executive wants to read a list of numbers. You are hired to translate numbers into a visual signal they can glance at to make a decision.
Checklist
- Connect BI tools to flat files (CSV) and live SQL databases
- Understand dimensions (categorical data) vs measures (numerical data)
- Build core standard charts: Bar charts, Line graphs, Scatter plots, and Heatmaps
- Create calculated fields within the BI environment itself
- Format axes, labels, and legends clearly
Dashboard Design & Storytelling
It's not just about making a chart; it's about telling a compelling and accurate story.
Real-world task: Redesign a cluttered, poorly-made dashboard (find an example online). Strip away 3D effects, fix the color palette for accessibility (colorblind safe), and add a clear headline that explicitly states the insight rather than just a generic title.
Why it matters: A brilliant analysis is useless if nobody understands the dashboard. Communicating the insight effectively is just as important as finding it.
Checklist
- Learn the principles of Data Ink Ratio (removing clutter, gridlines, 3D effects)
- Use color strategically to highlight anomalies, not to make it 'pretty'
- Implement drill-downs and cross-filtering for interactivity
- Design tooltips that provide extra context on hover
- Learn 'Z-pattern' layout to guide the viewer's eye across the dashboard
Python & Statistics 3 steps
Python for Data Analysis (Pandas & NumPy)
Replacing Excel for datasets that are too large (1 million+ rows) or require programmatic processing.
Real-world task: Load a 2-million row CSV into a Pandas DataFrame. Filter out invalid rows, group by a target variable, calculate the mean of another column, and export the cleaned aggregated result back to a new CSV.
Why it matters: Excel crashes around 1 million rows. Python handles millions easily and, more importantly, creates reproducible, automatable workflows.
Checklist
- Set up Jupyter Notebooks / Google Colab for interactive coding
- Understand basic Python syntax: loops, lists, dictionaries, functions
- Use NumPy for fast numerical arrays and calculations
- Use Pandas DataFrames to load, inspect, filter, and merge datasets
- Handle null values and duplicates strictly mapped in Pandas
Foundational Statistics
The math protecting you from making blatantly false claims about your data.
Real-world task: Take a dataset comparing two marketing campaigns. Use a t-test in Python (using scipy.stats) to determine if Campaign B actually outperformed Campaign A, or if the difference was just random statistical noise.
Why it matters: If you cannot determine statistical significance, you might tell your company to spend millions on a strategy change that was actually a fluke in the data.
Checklist
- Understand mean, median, mode, and when to use which (handling outliers)
- Learn variance, standard deviation, and normal distributions
- Understand correlation vs. causation logically
- Grasp the concept of statistical significance (p-values) and confidence intervals
- Learn basic probability theory
A/B Testing & Experimentation
Applying statistical rigor directly to business product decisions.
Real-world task: Write a 1-page brief analyzing an A/B test for a website button color change. Include the p-value, the confidence interval, and a final bottom-line recommendation to the product team on whether to launch the change.
Why it matters: A/B testing is the standard protocol for tech companies launching features. Being the analyst who can definitively say 'Yes, this worked' is highly valued.
Checklist
- Design an experiment: hypothesis, null hypothesis, control vs variant
- Determine sample sizes statically before running the test
- Avoid common traps: peeking at data early, Simpson's Paradox
- Segment experimental data to find hidden behavioral insights
- Communicate the test result definitively to non-technical teams
Portfolio & Job Hunt 2 steps
Building a Distinctive Portfolio
Proving you can analyze data end-to-end without your hand being held.
Real-world task: Scrape or download a unique dataset (e.g., from a local government API). Clean it, throw it into Tableau, find three actionable insights, and write a Medium/LinkedIn article explaining your process.
Why it matters: A hiring manager will look at your portfolio for 30 seconds. If it's the exact same project from a bootcamp tutorial, you get rejected.
Checklist
- Select 2-3 datasets you actually care about (Sports, Finance, Gaming, Public Health)
- Do NOT use Titanic, Iris, or Housing Prices datasets (too cliché)
- Clean data in Python/SQL, analyze it, and build a Tableau/PowerBI dashboard
- Write a Markdown README on GitHub explaining the business problem and your findings
- Host visualizations publicly (Tableau Public / GitHub Pages)
The Technical & Case Interview Prep
Passing the gauntlet of recruiter screens, SQL whiteboard tests, and business case studies.
Real-world task: Do a mock interview translating a technical SQL finding to a 'business manager' (played by a friend). They will ask 'So what?'. You must be able to abstract the code and answer with business impact.
Why it matters: The technical interview ensures you aren't lying on your resume. The business sense interview ensures you actually understand how the company makes money.
Checklist
- Practice live SQL coding on platforms like HackerRank or StrataScratch
- Prepare the STAR method for behavioral questions
- Translate resume bullets from 'Looked at data' to 'Increased metric X by Y% using Z'
- Practice business sense questions (e.g., 'Why might Uber's revenue drop on a Tuesday?')
- Send cold messages to analysts on LinkedIn for informal chats and referrals
Questions people ask about Data Analyst
Clear answers regarding timelines, prerequisites, salaries, and study strategies.
How long does it take to become a Data Analyst?
This roadmap estimates 5–9 months. It has 5 phases and 13 steps, from “Excel & Spreadsheets” to “Portfolio & Job Hunt”.
How do I become a Data Analyst?
Follow the 5 phases in order, starting with Phase 1, “Excel & Spreadsheets”. Your first task: Given a massive, unformatted raw export of sales data, use XLOOKUP and SUMIFS to create a summary table answering 'Total revenue per region'. Use IFERROR to catch missing regions gracefully.
How much does a Data Analyst earn?
Pay depends on the country. The salary box on this page shows DraftMyCareer’s estimate, in local currency, for the country chosen in the menu.
What do I need before starting?
This roadmap lists: Comfort with spreadsheets; Basic math skills.
What skills does a Data Analyst need?
This roadmap covers: SQL, Excel, Python, Data Visualization, Statistics, Communication.
Switch Careers: Paths Into and Out of This Role
See every switch →Next-Step Career Upgrades & Lateral Moves
- Data Scientist
Transition from backward-looking reporting to forward-looking predictive modeling and algorithmic decision-making.
⏱ 4–7 months🧭 ModerateWhat you would learnAdvanced Python (Scikit-Learn, SciPy) · Statistical Modeling & Probability · Machine Learning Algorithms
View Action Plan
- Month 1–2: Advanced Statistical Inference Master probability distributions, regression diagnostics, and experimental A/B testing.
- Month 3–4: Core Machine Learning Build predictive supervised and unsupervised models with feature selection in Python.
- Month 5–7: Real-world Modeling Projects Develop end-to-end churn prediction, recommendation systems, and model interpretability (SHAP).
- AI & ML Engineer
Step into the highest-growth tech domain building transformative AI agents and intelligent systems.
⏱ 6–9 months🧭 ChallengingWhat you would learnDeep Learning (PyTorch) · Natural Language Processing & LLMs · FastAPI & Microservices
View Action Plan
- Month 1–2: Software Engineering & Python Learn OOP, algorithms, asynchronous programming, and clean code principles.
- Month 3–4: Deep Learning Foundations Study neural network architectures, backpropagation, and PyTorch tensors.
- Month 5–7: LLM System Architecture Construct production RAG architectures, model fine-tuning, and semantic search APIs.
- Month 8+: MLOps & Deployment Automate model registry, tracking (MLflow), and inference pipelines on GPU clusters.
Moving Into This Role From Other Jobs
- From: Digital Marketer
Technical data analysts enjoy stronger job security, clear career advancement into data science, and analytical mastery.
⏱ 3–5 months🧭 Beginner-FriendlyWhat carries overGoogle Analytics / GA4 · Conversion metrics & ROI analysis · Spreadsheet modeling
View Action Plan
- Month 1–2: Advanced SQL Mastery Practice complex aggregation, window functions, and subqueries on business datasets.
- Month 2–3: BI Visualization Dashboards Design automated executive dashboards tracking sales, retention, and funnel drop-off.
- Month 4–5: Python Data Wrangling Automate marketing data extraction via APIs and perform statistical correlations.