Learning hub

Learn Data Analytics: SQL, Excel, Python and Portfolio Projects

What does it take to learn data analytics?

Learning data analytics means learning four skills in order: SQL to pull data, Excel for fast everyday analysis, Python with pandas for larger or repeatable work, and a BI tool for dashboards. You then prove them with two or three portfolio projects. Topfolio's Data Analyst track sequences this across twelve weekly milestones.

This page is the map of the skills themselves — what each one is for, why it sits where it does in the order, and exactly which course, practice questions and projects on Topfolio cover it.

Start with SQL BasicsSee the learning order

Which data analytics skill should you learn first?

Learn SQL first, Excel second, a BI tool third and Python fourth, then spend the rest of your time on portfolio projects and interview preparation. The table below gives the reason for each position, a self-study estimate, and where that skill falls inside the 12-week Data Analyst Career Track.

Data analytics skills in learning order, with the reason for each position, typical self-study time, and the corresponding weeks in the 12-week Data Analyst Career Track.
#SkillWhy in this positionTypical self-study timeIn the 12-week trackStart here
1SQL (basics to window functions)Nothing else works until you can pull the data. Also the most heavily interviewed skill.4 weeksWeeks 1–2, 4–5, 11SQL Basics course
2ExcelFastest to become useful in, and it is where stakeholders read your work.2–3 weeksWeek 3Excel for Data Analytics
3BI tool (Tableau or Power BI)Turns your first real analyses into something shareable while your Python is still new.3–4 weeksWeek 7Tableau Basics course
4Python with pandasMuch easier after SQL — the operations are the same, the syntax is new.6–8 weeksWeeks 6 and 8Python Essentials course
5Portfolio projectsThe only part of your application that proves the four skills above are real.Ongoing from month 4Weeks 9–10Browse projects
6Interview preparationLast, because you need finished projects to talk about before mock interviews are useful.Month 5 onwardsWeek 12Interview practice tests

Self-study estimates are the per-phase durations published on our Data Analyst Roadmap. Track weeks are the milestone weeks of the 12-week Data Analyst Career Track. Statistics is a useful addition alongside these but is not a gate for a first analyst role.

The four skills, and where to learn each one

Each pillar below links straight to the course that teaches it and, where a question bank exists, to the practice topics that drill it.

SQL

Getting the data out

SQL is the language you use to ask a database a question and get a table back. Every other skill on this page assumes you already have the rows you need.

It is the skill most consistently tested in data analyst interviews, and the one that unblocks everything else. Start here even if you have never written code before.

What this covers

  • SELECT, WHERE, ORDER BY and filtering
  • GROUP BY, HAVING and conditional aggregation
  • INNER, LEFT, self and anti-joins
  • CTEs, subqueries and window functions
SQL Basics· 3 weeksSQL Advanced (Analytics)· 4 weeksPractise SQL JoinsPractise SQL Aggregation and GROUP BYPractise SQL Window FunctionsPractise SQL Common Table ExpressionsPractise SQL Subqueries

Excel

The everyday analysis layer

Excel is where most business teams actually look at numbers. Lookups, pivot tables and a handful of text functions cover the majority of day-one analyst requests.

It is the fastest skill on this list to become useful in, and it is what a stakeholder will open when you send them your output. Two to three weeks is usually enough.

What this covers

  • VLOOKUP, XLOOKUP and INDEX-MATCH
  • Pivot tables, calculated fields and pivot charts
  • IF, SUMIF and COUNTIF logic
  • Text functions, data validation and de-duplication
Excel for Data Analytics (Short & Focused)· 2 weeks

Python & pandas

Scale and repeatability

Python with pandas takes over when a task is too big, too messy or too repetitive for a spreadsheet — cleaning a million rows, joining several files, or re-running the same report every week.

Learn it third. Coming to pandas after SQL is much easier, because groupby, merge and filtering are the same ideas you already know under different names.

What this covers

  • DataFrames, selection with loc and iloc, boolean masks
  • groupby, agg, merge and pivot_table
  • Missing values, type conversion and text cleanup
  • Charts with matplotlib and seaborn
Python Essentials for Data Analytics· 4 weeksPractise pandas DataFramePractise SQL Data Cleaning

BI & visualisation

Making the answer readable

A BI tool turns a query result into something a non-analyst can use: a dashboard that refreshes, filters and answers the obvious follow-up question without another request to you.

Pick one tool and go deep. Tableau and Power BI are the two that appear in job descriptions; the concepts transfer between them, so the choice matters less than the depth.

What this covers

  • Connecting to a database or file source
  • Chart choice and dashboard layout
  • Filters, parameters and interactivity
  • Telling a single clear story per dashboard
Tableau for Data Analytics (Short & Focused)· 2 weeks
See every data analytics courseWhat is free and what is paid

Where to practise each topic

Reading about a JOIN and writing one are different skills. Every topic hub below runs real SQL or Python in the browser against a seeded database, so you see the result set immediately.

SQL Window Functions

Practice sql window functions questions

SQL Joins

Practice sql joins questions

SQL Aggregation and GROUP BY

Practice sql aggregation and group by questions

SQL Subqueries

Practice sql subqueries questions

pandas DataFrame

Practice pandas dataframe questions

SQL Common Table Expressions

Practice sql common table expressions questions

SQL Data Cleaning

Practice sql data cleaning questions

SQL Date and Time Functions

Practice sql date and time functions questions

SQL String Functions

Practice sql string functions questions

All SQL and Python practice questionsTimed interview practice tests

Turning the skills into a portfolio

Courses and practice questions build the skill; a portfolio is the only thing that proves it to someone who has never met you. Aim for two or three finished, end-to-end projects rather than a long list of notebooks that stop at the first chart.

What a finished project contains

  • A business question stated in one sentence before any code
  • The cleaning steps, including what you threw away and why
  • Analysis that uses more than one of SQL, Python and a BI tool
  • A recommendation someone could actually act on, not just a chart
  • A README so a recruiter understands it without running anything

Guided portfolio projects

Ambiguous business briefs against real databases

Free datasets to practise on

Sources for building your own project from scratch

Want the whole sequence in one place?

The Data Analyst Career Track puts SQL, Excel, Python and Tableau into twelve weekly milestones, adds portfolio projects and a capstone, and finishes with interview prep — so you never have to guess what to learn next.

Explore the 12-Week Data Analyst Career Track

Where to go next

This page covers the skills. Two other guides on Topfolio cover the parts around them — the job hunt, and the decision about which data role to aim for in the first place.

Data Analyst Roadmap

The 6-month career plan around these skills: GitHub and LinkedIn setup, resume structure, when to start applying, and how to prepare for each interview round.

How to Become a Data Analyst

The narrative version: what the job actually involves day to day, how AI is changing it, and the job-search strategy that follows.

Data Analyst vs Data Engineer vs Data Scientist

If you are not yet sure which of the three roles you want, read this before committing three months to a skill list.

SQL Interview Questions

The questions the SQL pillar above is ultimately preparing you for, with worked answers.

Frequently asked questions about learning data analytics

What skills do you need to learn data analytics?

Four core skills plus proof of work. SQL to get data out of a database, Excel for quick everyday analysis and reporting, Python with pandas for larger or repeatable work, and one BI tool such as Tableau or Power BI for dashboards. The proof is two or three portfolio projects built on real datasets.

Should I learn SQL, Excel or Python first?

Learn SQL first. It is the skill most consistently tested in data analyst interviews, and almost every other tool assumes you can already pull the data you need. Excel comes next because it is quick to pick up and is still how most business teams look at numbers. Python is third, once you hit work that Excel cannot repeat reliably.

How long does it take to learn data analytics?

Topfolio sequences the whole thing across twelve weekly milestones in the Data Analyst career track, from SQL in week one to a capstone and interview prep in weeks ten to twelve. Learning the same material on your own, unstructured, takes longer — our Data Analyst Roadmap plans it out over roughly five to six months at ten to fifteen hours a week.

Can I learn data analytics without a degree or a coding background?

Yes. Data analytics is one of the more accessible entry points into tech because hiring leans on demonstrable skills and a portfolio rather than a specific degree. The SQL Basics course starts from SELECT statements and assumes no prior programming, and the career track assumes zero for its first three weeks.

Is Excel still worth learning if I already know Python?

Yes, and it stays on the list for a practical reason: the people who receive your analysis mostly work in spreadsheets. Excel is where a stakeholder will open your output, ask a follow-up question, and expect an answer in the same file. Python is for scale and repeatability, not for replacing that conversation.

Do I need to learn machine learning to become a data analyst?

No. Machine learning is a bonus for a data analyst role, not a requirement. Understanding the basics — supervised versus unsupervised, train and test splits, overfitting — helps you talk about it in an interview, but SQL depth, clean analysis and clear communication are what the job is actually assessed on.

How many portfolio projects do I need?

Two or three finished end-to-end projects beat a long list of half-built ones. Each should state the business question, show how you cleaned and analysed the data, and end with a recommendation someone could act on. The Data Analyst career track builds a portfolio project in week nine and a capstone in week ten.

What can I learn on Topfolio for free?

The free tier covers SQL and Python practice questions, two interview practice tests, all career roadmaps, the first module of every course, gamification features, and one AI resume feedback per month. Full courses, career tracks and programs are one-time purchases with lifetime access, not subscriptions.