A-CUBE

// Beginner — no prior coding needed

Data Analysis

Turn raw data into decisions people actually act on

Start from spreadsheets and finish building dashboards a business runs on. No prior coding needed — you write your first query in week one.

Excel SQL PostgreSQL Python pandas Power BI Tableau Git

What you will build, week by week

6 modules, every one of them hands-on.

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  1. 01 Module 1 — Thinking like an analyst

    2 weeks

    What a good question looks like, and why most reporting answers the wrong one.

    • Framing a business question you can actually answer with data
    • Types of data, and what each can and cannot tell you
    • Spreadsheets properly: lookups, pivot tables, conditional logic
    • Cleaning messy real-world data
    • Reading a dataset critically before trusting it

    Hands-on Take a messy real spreadsheet and produce a clean, defensible summary.

  2. 02 Module 2 — SQL, the analyst's core skill

    4 weeks

    The one skill every data job asks for. You will write queries daily from here on.

    • SELECT, WHERE, ORDER BY, LIMIT
    • Aggregation with GROUP BY and HAVING
    • Joins across multiple tables, and when each kind is right
    • Subqueries and common table expressions
    • Window functions: running totals, rankings, period comparisons
    • Reading a database schema you have never seen before

    Hands-on Answer ten business questions against a real multi-table database.

  3. 03 Module 3 — Python and pandas

    4 weeks

    Where spreadsheets run out, Python takes over.

    • Python fundamentals for people who are not programmers
    • pandas: loading, filtering, grouping, merging
    • Handling missing and inconsistent data
    • Dates, times and time series
    • Automating a report that used to be manual
    • Charting with matplotlib and seaborn

    Hands-on Automate a weekly report end to end, from raw file to finished chart.

  4. 04 Module 4 — Statistics that matter in practice

    2 weeks

    Enough statistics to avoid confidently saying something untrue.

    • Distributions, averages and why the mean often misleads
    • Correlation against causation, with real examples of the mistake
    • Sampling and confidence, in plain language
    • A/B tests: designing one and reading the result honestly

    Hands-on Critique a published analysis and write up what it got wrong.

  5. 05 Module 5 — Dashboards and telling the story

    3 weeks

    An analysis nobody understands has no value. This module is about being understood.

    • Power BI and Tableau: building and publishing dashboards
    • Choosing the right chart, and the ones to avoid
    • Designing for the person reading it, not for yourself
    • Presenting findings to non-technical stakeholders

    Hands-on Build and present a dashboard to the class as if to a client.

  6. 06 Module 6 — Portfolio and getting hired

    1 week

    What you leave with, and how you use it.

    • Building a portfolio project from a dataset you choose
    • Publishing your work on GitHub so employers can read it
    • Resume and LinkedIn for analyst roles
    • Mock interviews: SQL round, case round

    Hands-on A finished, documented portfolio analysis in your own GitHub repository.

Before you join

None. If you can use a computer and are willing to work with numbers, you can start. We teach the spreadsheet skills and the coding from scratch.