My path into data analytics began with a practical question: could I learn to use data to answer problems that mattered at work? The accounts in this article show several ways into the field, not one formula. They also point to a shared pattern: learn core tools, keep working with real questions, make your analysis visible, and get better at explaining what the results mean.
What drew me to data analytics
Isaac D. Tucker-Rasbury describes his early motivation this way: “My journey into data analytics began from a place of curiosity and a need to distinguish myself early in my career, particularly during my time at Goldman Sachs.” In his account, curiosity became a practical effort to develop skills he could use in his work.
Other routes begin elsewhere. Susan, whose story is published by The Curious Academy, started with doctoral biological research before moving into a bootcamp and practicing spreadsheets, SQL, data cleaning, and visualization. Laura McWhinney describes moving from journalism and communication study to a master’s in information technology focused on business data analytics, and then to a data specialist role in early childhood education.
These accounts are individual experiences, not evidence that one background or transition is typical. Their useful lesson is that analytics draws on more than a computer-science route: subject knowledge, communication, and a willingness to investigate a question can all be part of the starting point.
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What a data analyst actually does
Data analysis is not just running queries or making charts. An analyst needs to clarify the question, understand the context in which it matters, choose an appropriate way to investigate it, and explain what the findings imply. A Wiley-hosted career-guide excerpt puts the thinking behind the tools succinctly: “A good data analyst needs to know how to think like an analyst.”
The work differs by organization and industry. Some roles lean more heavily on technical analysis; others involve frequent stakeholder conversations, business research, or support for public services. Tucker-Rasbury’s first full-time analyst role, on an FP&A team, combined Excel, SQL, Power BI, some Python, and research into prospective clients and business opportunities. McWhinney’s work as a data specialist was in early childhood education. These examples illustrate why it is worth asking what decisions a role supports, not just which software it lists.
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How I built practical skills
Start with spreadsheets and SQL
Spreadsheets are a useful way to inspect and organize data, while SQL helps retrieve and work with data stored in databases. Tucker-Rasbury recommends getting a firm grasp of “the basic tools (ex. MS Excel & Power Query, SQL, DataViz (Power BI or Tableau), and Python).” Those are his recommendations from experience, not a mandatory checklist for every job.
Add visualization and project work
A visualization tool such as Power BI or Tableau can help turn an analysis into something others can inspect and discuss. Susan’s learner profile describes practicing data cleaning and visualization and producing a comparative analysis portfolio project. That is one learner’s account published by the training provider, rather than an independent evaluation of the bootcamp.
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Learn more tools as the work calls for them
Python may be helpful in some positions, but the accounts do not show that every beginner needs it before applying for analyst work. Tucker-Rasbury later describes using SQL for reporting and contributing to a data pipeline with SQL, dbt, Visual Studio Code, and Git/GitHub. The stack changed with the work; it was not a universal starting requirement.
How the learning turned into work
Tucker-Rasbury recounts trying to learn SQL after missing a workplace analytics bootcamp. He later landed his first full-time analyst role in October 2021 on an FP&A team. In that role, he used a mix of spreadsheets, SQL, Power BI, some Python, and business research. His later experience with reporting and data pipelines shows one way a role can expand from answering business questions toward helping build the processes that supply data.
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McWhinney’s transition followed a different sequence: journalism and communication study, graduate study focused on business data analytics, then a data specialist position in early childhood education. She presents the CAP framework as a way to define business problems and select suitable analytical approaches. Her qualification is important: “Certifications don’t replace experience, but they can sharpen it.” A certificate can structure learning, but these accounts do not establish it as a universal hiring requirement or a substitute for applied work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a portfolio can—and cannot—show
A portfolio makes examples of applied work visible to someone who has not worked with you. Tucker-Rasbury recommends creating public-facing projects and sharing them; Susan’s profile describes a comparative analysis project. A useful project should make clear what question it addresses, what data and methods it uses, what the result means, and where the analysis has limits.
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A portfolio is evidence of practice, not a job guarantee. The accounts support treating it as one part of a broader effort that can include a clear resume, applications, networking, and continued learning. They do not establish that a project or certificate alone secures a position.
How to choose a learning route or first role
When comparing courses, self-study, or other learning options, look for opportunities to practice with spreadsheets, SQL, visualization, and projects, as well as useful feedback and practice communicating findings. A course label matters less than whether you can apply what you learn to a question and explain the result.
When comparing jobs, read beyond the title and tool list. Consider:
- Technical depth: How much of the work involves querying, cleaning, modeling, or building data processes?
- People and communication: Will you gather requirements, present findings, or work closely with stakeholders?
- Domain: What business area, industry, or public service will you need to understand?
- Purpose and outputs: What decisions or services does the analysis support, and what are you expected to deliver?
The Wiley career-guide excerpt emphasizes that analyst work shifts with company and industry and often combines technical and business tasks. Comparing those dimensions gives a more realistic picture of a role than expecting every analyst to have the same working day.
Quick Recap
What I would tell someone starting now
- Begin with a question you care about. A clear problem gives tool practice a purpose and helps you learn the context behind the data.
- Build a workable foundation. Practice spreadsheets and SQL, then learn a visualization tool. Add Python or other tools when a project or target role calls for them.
- Finish and explain projects. Show the question, approach, result, and limitations—not only a polished chart.
- Seek feedback and collaboration. Susan’s account describes learning alongside work and benefiting from collaboration; discussion can reveal unclear assumptions and improve how findings are presented.
- Look for fit, not a supposedly universal route. The examples here include finance, early childhood education, biological research, and journalism. The right next step depends on the work and domain that hold your interest.
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