When people talk about learning to code, it can sound like the first step is to memorise syntax. I’m finding it more useful to begin with the kind of question I want to answer and then learn which tools can help me work through it.

Keep the business question in view

My foundation is in accounting and finance, with experience in reporting, budgeting, and variance analysis. That background gives me a way to think about what a figure represents, how measures are defined, and what someone may need to understand from a result.

Those questions still matter when working with code. Before querying or transforming data, I need to understand the fields, the level of detail, and what a useful answer would look like. Writing code can make analysis more repeatable, but it cannot decide whether the question or assumptions are sound.

Learn SQL as a way to ask questions of data

SQL is a practical way to retrieve and summarize information stored in tables. As I develop this skill for analytics, I’m focusing on understanding how to select relevant fields, filter records, combine tables, and aggregate data without losing sight of what each row represents.

A query can execute successfully and still produce an answer that is easy to misinterpret. Checking joins, identifiers, missing values, and row grain is part of asking a good question of the data, not an optional step after the query.

Learn Python for flexible analysis

Python offers another way to work with data, especially when a task involves a series of repeatable steps or statistical analysis. I’m developing my Python skills for data analysis and automation, while learning to make each step understandable enough to inspect and verify.

A script should not be treated as correct simply because it finishes running. The input, transformations, assumptions, and outputs still need to be checked. That is similar to reviewing a report: knowing how the result was produced helps determine whether it is fit for its intended use.

Build understanding through small applications

SQL and Python are broad tools, so trying to learn everything at once can make progress feel unfocused. Working through small, clearly scoped exercises gives me a way to practise one idea at a time: prepare data, check assumptions, calculate a result, and explain what it does and does not show.

Explaining a technical idea in plain language is also part of learning it. If I can describe what a query or script does, why its result is useful, and where its limitations are, I have a better chance of communicating the analysis clearly to someone who does not work with code every day.

Connect the tools to the work I want to do

I see finance, data analysis, and programming as connected capabilities. Finance helps me frame questions around measures and business context. SQL and Python can help me retrieve, examine, and work with data. Business intelligence can then help communicate useful information through reporting.

I’m still developing these technical skills. My aim is to build them steadily, apply them to appropriate practice work, and remain clear about the difference between a learning exercise and a verified business finding.

Key takeaways

  • Start with the question, not the programming language.
  • Understand the data and assumptions behind a result.
  • Use small exercises to build practical, verifiable skills.
  • Explain both what an analysis shows and what it cannot establish.