How to Become a Data Analyst

A data analyst’s core job is answering questions with data. Those questions might be about which products are selling, why customer retention is declining, which marketing channels are performing, or where operational costs are rising faster than expected. The analyst’s job is to find, clean, and analyze the data that answers the question, then communicate the answer clearly to people who need to act on it.

In practice, most data analysts spend their time querying databases with SQL, building reports and dashboards in Excel or Power BI, cleaning messy datasets, running statistical analysis, and presenting findings to non-technical colleagues. The ratio of those activities varies widely by role and industry, but all of them appear somewhere in almost every data analyst position.

Common job titles that fall under the data analyst umbrella include data analyst, business analyst, marketing analyst, operations analyst, reporting analyst, and data visualization specialist. All of these roles share the same foundational skill set.

AI is changing the mechanics of data analysis faster than almost any other professional field. Tools that once required hours of manual data cleaning can now be automated. Exploratory analysis that used to require a skilled Python developer can now be initiated with a natural language prompt. Machine learning models that once required a data scientist to build can now be assembled with AI-assisted platforms.

This does not reduce demand for data analysts. It changes what analysts are expected to know. Employers increasingly want analysts who understand how to work alongside AI tools, who can validate AI-generated outputs, and who can frame the right questions for AI-assisted analysis to answer. The analyst who understands the business problem, knows what data matters, and can interpret and communicate results remains essential. What AI replaces is the most repetitive and mechanical parts of the job, which frees analysts to do more strategic work.

DWC’s data analytics curriculum integrates AI-assisted tools and workflows throughout, including machine learning concepts, predictive modeling, and AI-powered analytics platforms. Students learn to work with these tools alongside the foundational SQL, Python, Excel, and statistical skills that remain the baseline for any analyst role.

Yes, and this is increasingly common. Many employers, particularly in technology, financial services, and marketing, evaluate analysts based on demonstrated skills and portfolio work rather than academic credentials. The job requires technical ability and clear thinking, both of which can be demonstrated through projects rather than diplomas.

That said, a degree is helpful context in a resume and some industries, particularly larger enterprises and regulated sectors like healthcare and finance, still list a degree as a preferred qualification. For career changers, the most effective approach is usually to combine a structured training program with a strong portfolio of completed projects and any relevant prior professional experience that demonstrates analytical thinking.

Career coaching is included in all DWC certificate programs and covers exactly this: how to position your prior experience alongside new technical skills, how to build a portfolio that resonates with hiring managers, and how to navigate the job search as a career changer. See how our career coaching works here.