The Data Analyst
roadmap.

1. 01

   Master spreadsheets deeply

   Before SQL or Python, Excel and Google Sheets must be second nature. Learn pivot tables, XLOOKUP, INDEX/MATCH, array formulas, and conditional formatting. Practice cleaning and summarising messy datasets from Kaggle. A data analyst who can't answer a business question in a spreadsheet in 10 minutes won't last long. Speed and accuracy here are non-negotiable.

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2. 02

   Learn SQL

   SQL is the most important skill for a data analyst. Start with SELECT, WHERE, GROUP BY, and ORDER BY. Then learn JOINs (INNER, LEFT, RIGHT) — this is where most beginners struggle, so practice until joins feel instinctive. Move to subqueries, CTEs (WITH clauses), and window functions like ROW_NUMBER(), RANK(), and LAG(). Practice on SQLZoo, Mode Analytics' tutorial, or with your own PostgreSQL instance.

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3. 03

   Learn a visualisation tool

   Pick one: Tableau Public (free, portfolio-friendly), Power BI Desktop (free, widely used in enterprise), or Looker Studio (free, integrates with Google). Build at least 3 interactive dashboards on real datasets. The goal isn't beautiful charts — it's dashboards that answer a specific question clearly without requiring explanation. If someone needs 5 minutes to understand your dashboard, it needs to be rebuilt.

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4. 04

   Learn statistics fundamentals

   You don't need a maths degree. You need to understand mean, median, mode, standard deviation, correlation, and basic probability. Learn to spot when an 'average' is misleading. Understand the difference between correlation and causation, and what statistical significance actually means. Khan Academy's statistics course is free and covers everything you need at this stage.

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5. 05

   Build real projects on public data

   Analyse 3–5 real public datasets and write up your findings. Good sources: Kaggle, data.gov, Our World in Data, Google Trends. For each project, start with a business question (not 'let me explore this data'), clean the data, analyse it with SQL or Python, visualise key findings, and write a 1-page summary of what you found and what you'd recommend. This is the format real analysis takes.

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6. 06

   Apply for junior analyst roles

   Target roles titled 'Junior Data Analyst', 'Reporting Analyst', 'Business Intelligence Analyst', and 'Data Coordinator'. Tailor your resume to show SQL, Excel, and your visualisation tool. Link to your portfolio projects on GitHub or a personal site. In interviews, be ready to walk through a past project: what question you were answering, how you approached the data, what you found, and what you recommended.

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USD, salaried or contract

• LinkedIn

  Best source of full-time analyst roles — filter by 'entry level'

• Indeed

  High volume of analyst postings including smaller companies

• Glassdoor

  Good for researching salary ranges before interviews

• Upwork

  Freelance analysis projects — good for building portfolio while job hunting

• AngelList / Wellfound

  Startup analyst roles — often more autonomy and growth

• Company data teams directly

  Many companies post analyst roles on their own careers pages first

• SQLZoo — interactive SQL practice from basics to advancedPractice
• Mode Analytics SQL Tutorial — business-focused SQL problemsPractice
• Khan Academy Statistics — free, comprehensive, beginner-friendlyCourse
• Google Data Analytics Certificate (Coursera) — structured 6-month curriculumCourse
• Tableau Public Training Videos — official free trainingCourse
• Kaggle Datasets and Competitions — practice on real dataPractice
• Towards Data Science (Medium) — accessible articles on real analysis techniquesReading
• GitHub — host your analysis notebooks and dashboardsTool

Do I need to know Python to become a data analyst?

No — SQL and Excel are the core requirements for most junior analyst roles. Python (with Pandas) becomes valuable when datasets are too large for spreadsheets or when you need to automate repetitive analysis. Add it once you're comfortable with SQL, not before.

How long does it take to become job-ready as a data analyst?

With consistent daily practice, most people are ready to apply for junior roles in 3–6 months. The key milestones: fluency in SQL joins and aggregations, proficiency in at least one BI tool, and 3–5 portfolio projects with documented business questions and findings.

What's the best free dataset for practice?

Kaggle has the largest collection of clean and messy real-world datasets across industries. For business-focused practice: the Northwind database (sales), Superstore dataset (retail), and any public government dataset (data.gov) work well. Always start with a business question, not the dataset.

Power BI or Tableau?

Tableau Public is better for portfolio building — your work is publicly visible and shareable. Power BI is more common in enterprise environments and integrates with the Microsoft ecosystem. If you're targeting large companies or government, learn Power BI. If you're targeting startups or building a freelance portfolio, start with Tableau.

How important is communication vs. technical skill?

Equally important. Many technically strong analysts are passed over for promotion because they can't communicate findings to non-technical stakeholders. Practice translating every analysis into a plain-English summary with a clear recommendation. The analyst who can do this earns significantly more.

Can I become a data analyst without a maths or statistics degree?

Yes. The statistics required for most analyst roles — mean, median, distributions, basic correlation — can be learned in a few weeks. What you need is a strong grasp of logical thinking and a willingness to validate your own assumptions. Many successful analysts come from business, journalism, and social science backgrounds.

• Business Intelligence Analyst

  Natural next step

• Data Engineer

  Natural next step

• Product Analyst

  Natural next step

• Data Entry

  Data Entry Specialist

• Admin & Support

  Virtual Assistant

• Customer Support

  Customer Support Representative