Every click, purchase, patient visit, and support ticket creates data. Most organizations have more of it than they know what to do with, and they need people who can turn raw numbers into clear answers: Which products are selling? Where are customers dropping off? What is driving costs up? Those people are data analysts.
Data analytics is also one of the most accessible ways into a tech career. You do not need a computer science degree or years of programming experience. What you need is a practical toolkit (spreadsheets, SQL, a visualization tool, and a working grasp of statistics) plus proof that you can use it.
This guide walks through exactly how to become a data analyst, including the skills employers screen for, the certifications worth earning, what the job pays, and a step-by-step plan for landing your first role.
A data analyst collects, cleans, and analyzes data to help an organization make better decisions. The work usually follows a predictable cycle:
For a deeper look at the role itself, including the different types of analysts and how the job compares to data science, read our guide on what a data analyst does.
Data analysts are hired in almost every industry, including:
That range is one of the biggest advantages of the career. Your core skills stay the same no matter which industry you choose.
Spreadsheets are still the most common analytics tool in business. Employers expect comfort with formulas, lookups (XLOOKUP or VLOOKUP), pivot tables, conditional formatting, and basic charts.
SQL (Structured Query Language) is how analysts pull data from databases. It shows up in more data analyst job postings than almost any other skill. Learn how to select, filter, sort, join tables, and aggregate data with functions like SUM, COUNT, and AVG.
Tableau and Microsoft Power BI are the two dominant platforms for building dashboards. Knowing at least one of them well makes you immediately more competitive.
You do not need to be a statistician, but you should understand averages, medians, distributions, correlation, and basic probability. These concepts help you decide whether a pattern is real or just noise.
Python is increasingly common in analytics roles for cleaning large datasets and automating repetitive tasks. It is not always required at the entry level, but it can set you apart and becomes more important as you advance.
If you come from customer service, retail, sales, operations, healthcare administration, or the military, you probably already have more of these skills than you realize.
Start with the basics of how data works: data types, data structures, databases, and the analytics lifecycle. From there, build skills in this rough order:
Self-study works for some people, but a structured program gives you a logical sequence, instructor feedback, and hands-on projects that double as portfolio pieces.
Certifications help you prove your skills to employers before you have job experience. Three are especially useful for entry-level data analysts:
Many analysts also add CompTIA Project+, since analytics work is often organized as projects with stakeholders, timelines, and deliverables.
Want to understand why Data+ carries so much weight with employers? Read our breakdown of why CompTIA Data+ is the data analytics certification you need. For a wider view of entry-level credentials, see the best IT certifications for beginners.
A portfolio is the single most effective way to overcome a lack of experience. Hiring managers want to see how you think, not just which tools you list on your resume.
Aim for three to five projects that show the full analytics cycle. Strong project ideas include:
For each project, write a short summary: the question you asked, the data you used, the tools you applied, what you found, and what you would recommend. That summary is what turns a project into an interview conversation.
You can build experience before your first analyst title:
Your first job title might not say “data analyst.” Common entry points include:
When you apply, tailor your resume to each posting’s tools and keywords, link your portfolio at the top, and describe results with numbers. “Built a Power BI dashboard that tracked weekly sales across 12 locations” is far stronger than “familiar with Power BI.”
Data analytics pays well from the start, and salaries climb steadily with experience. Here is a look at national averages for common analytics roles:
| Role | Average Salary (U.S.) |
| Data Analyst I (0 to 2 years) | $69,826 |
| Data Visualization Analyst | $69,403 |
| Junior Business Analyst | $72,501 |
| Data Analyst II (2 to 4 years) | $84,332 |
| Data Analyst III (4 to 7 years) | $99,100 |
| Data Analyst IV (7+ years) | $123,031 |
| Business Intelligence Analyst (all levels) | $111,991 |
Salary data referenced in this post is sourced from Salary.com.
Pay varies by industry, location, and company size, and larger enterprises generally pay more than startups. Skills like SQL, Python, and advanced visualization, plus certifications, can all push offers higher.
Data analytics offers a clear growth path. A typical progression looks like this:
Many analysts eventually specialize in an industry, such as healthcare, finance, or marketing, where domain knowledge makes them especially valuable. Others build on their technical skills and move toward data engineering or software development.
| Path | Typical Timeline | Best For |
| Self-study plus portfolio | 6 to 12 months | Highly self-directed learners |
| CompTIA Data+ Bootcamp | 5 days of training | Professionals with spreadsheet experience who want a fast credential |
| Associate degree with certifications | 1 to 2 years | Career changers who want a degree, multiple certifications, and a full skill set |
| Bachelor’s degree | 3 to 4 years | Students aiming for advanced or specialized analytics roles |
For most career changers, landing an entry-level analyst role takes several months to two years, depending on the path and how much time you can commit each week.
Not always, but it helps. Many entry-level postings list a degree as preferred, and a degree paired with certifications makes a candidate stand out, especially without prior analyst experience.
CIAT’s Associate of Applied Science in Business Data Analytics is designed for career changers with no prior experience, and it requires no certifications. The 64-credit program can be completed in one to two years and includes coursework in:
Students prepare for four industry certifications along the way: CompTIA Data+, CompTIA Project+, Tableau Desktop Specialist, and Microsoft Power BI Data Analyst Associate. Graduates are prepared for roles including data analyst, business analyst, data visualization analyst, junior marketing analyst, and healthcare or research data analyst.
For students who want to go further, the Applied Bachelor’s Degree in Software Development offers a longer path that combines programming and data skills. Compare every option on CIAT’s data analytics programs page or read more about data analytics degrees.
For people who enjoy solving puzzles, spotting patterns, and explaining what the numbers mean, data analytics is an excellent fit. It offers strong starting salaries, clear advancement, remote-friendly work, and skills that transfer across nearly every industry.
It is less suited to people who dislike detail work or would rather avoid presenting findings to others. Data analysis involves plenty of cleaning and checking before the interesting insights appear, and communicating results is just as important as finding them.
If you are weighing data analytics against other paths that do not require heavy coding, our guide to tech careers beyond coding is a helpful comparison.
Yes. Many data analysts start without prior analyst experience by building skills in Excel, SQL, and a visualization tool, earning a certification like CompTIA Data+, and creating a portfolio of real projects. Entry-level titles like junior data analyst or reporting analyst are common starting points.
CompTIA Data+ is one of the strongest options for beginners because it is vendor-neutral and covers the full analytics process. Tableau Desktop Specialist and Microsoft Power BI Data Analyst are also valuable because they prove skill in the visualization tools employers use most.
SQL is essential for most data analyst roles. Python is a strong bonus that becomes more important as you advance, but many entry-level analysts start with SQL, Excel, and Tableau or Power BI and learn Python over time.
According to Salary.com, a Data Analyst I with zero to two years of experience earns an average of about $69,826. Junior business analysts average about $72,501, and pay grows significantly with experience.
Most career changers can land an entry-level role within several months to two years. A bootcamp can deliver a certification in days, while an associate degree typically takes one to two years and builds a broader skill set with multiple certifications.
You need solid foundational math and basic statistics, such as averages, percentages, distributions, and correlation. Advanced mathematics is generally more important for data science than for data analysis.
Data analysts focus on collecting, cleaning, and analyzing data to find trends and insights. Business analysts focus on improving business processes and systems, using data as one input alongside stakeholder needs. The roles overlap heavily, and many people move between them.
Both are widely used. Power BI is common in organizations that already rely on Microsoft tools, while Tableau is popular across many industries for its visualization capabilities. Learning either one well is valuable, and the skills transfer easily between them.
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