How to become a data analyst: education and skills

By: The Capella University Editorial Team with Bradly E. Roh, PhD, DBA and Interim Dean and Vice President for the School of Business, Technology and Health Care Administration

Reading Time: 10 minutes

Companies are drowning in data, and they need people who can make sense of it. Data analysts do exactly that. They turn a spreadsheet full of numbers into insights that a company can act on.

When you’re exploring this field, it’s easy to assume that you probably need a math degree or years of coding experience. But what you really need is a clear starting point: which skills to build, in what order and how to demonstrate them.

Discover a roadmap to becoming a data analyst that covers your education options, the technical and communication skills that matter most, how to build a portfolio and where AI comes into play.

Neither Strategic Education, Inc., Capella University, nor any of their affiliates promotes, endorses or has any business relationship with the products or programs mentioned herein.

What does a data analyst do?

A data analyst collects, organizes and analyzes data to help guide business decisions. They identify patterns and trends within raw data and use them to produce insights. Businesses can use these insights to improve operations, leverage new market opportunities and support long-term growth.

Data analyst responsibilities

While an analyst’s responsibilities vary by role and industry, the core objective is to use data to answer questions and solve problems. Their main duties and tasks include:

• Cleaning and organizing data from multiple sources
• Identifying trends and business opportunities using analytical methods
• Visualizing insights through dashboards and tools like Tableau
• Monitoring key performance indicators (KPIs)
• Communicating findings and recommendations to stakeholders

Imagine an online store running a summer sale, but they don’t know why some products sold better than others. A retail data analyst assesses the sales and customer data to find out the reasons behind the top-selling products. Using these insights, the company can make smarter decisions about what to stock and promote for the next sale.

Data analysts vs. data scientists vs. data engineers

Data analysts, data scientists and data engineers are all related professions, but they’re not the same.

Analysts focus on interpreting and communicating data insights. Data scientists go deeper, they build predictive models and use advanced statistical analysis or machine learning techniques to support innovation.

Data engineers, on the other hand, design and maintain the systems and pipelines that collect, store and process data. If you’re still exploring which direction feels right, our guide to a career in data analytics covers the types of analytics and the different paths within and adjacent to the field.

How to become a data analyst

Becoming a data analyst starts with choosing the right learning pathway for your situation, then building a core set of technical and communication skills and proving those skills with real work.

Choose your educational pathway

There’s no single route into data analytics, but there is a right route for your schedule, budget and career goals.

• Formal education: A bachelor’s degree in computer science or IT with a data analytics specialization gives you a comprehensive foundation, covering technical skills alongside the statistical and business context that shapes how analysts think.
  
  Some analysts choose to further their studies and pursue a master’s degree in analytics to help develop skills relevant to more senior roles.
  
  If you’re already working, then an online IT degree can be a flexible option to get structured, formal education from experienced faculty without pausing your career.

• Professional certifications: Recognized data analytics certificates like the Google Data Analytics Certificate and IBM Data Analyst Professional Certificate can help you learn and validate your core analytics skills.
  
  They often take a shorter time to complete, but they might not be as comprehensive as a degree. Instead, they can add credibility on top of your existing qualifications and experience.

• Bootcamps: Online course providers like Udemy, Full Stack Academy or CareerFoundry offer intensive, project-focused bootcamps that could help you go from beginner to job-ready in a few months.
  
  Keep in mind that these programs often require a full-time commitment for the duration of the program, which doesn’t work for everyone.

• Self-study: Online data science or analytics resources, like the SQL Tutorial for Data Analysis by Mode, are good options for learning on your own. This is the lowest-cost option, with many free resources to choose from, but it’s also the hardest pathway to sustain.

Once you’ve chosen a learning route, the next step is to build your technical skills.

Build your technical skills

Many aspiring analysts get stuck here because they try to learn everything at once. But, technical skills build on each other and starting in the wrong place can make the learning process harder than it needs to be. Here’s a sequence that can help make your technical learning more effective and easier to navigate.

• Excel and basic statistics: Microsoft Excel is the most widely used data tool in the workplace. Getting fluent with basic Excel functions like XLOOKUP and features like pivot tables builds the intuition you need before writing a line of code.
  
  Pair it with foundational statistics, like mean, median, variance and distributions, framed around one question: what does this number actually mean for the business?

• Structured Query Language (SQL): Once you’re comfortable in Excel, SQL is the next essential skill. It’s one of the most universal programming languages for data analysis.
  
  Learn to write queries that filter, group, aggregate and join datasets using SQL, and you’ll be able to answer business questions directly from a database without relying on anyone else to pull the data for you.

• Data cleaning: A majority of your time as an analyst could be spent cleaning data, which involves handling missing values, duplicate records, inconsistent formatting and outliers.
  
  Learning to do this well in Excel and SQL before moving to more advanced tools builds habits that will serve you throughout your career.

• Data visualization: Once your data is clean, you need to communicate what it shows. Learn the principles of chart selection, like when a bar chart or line chart is right or when a table is clearer than either. Then learn a data visualization tool thoroughly. You can start with free tools like Tableau Public and Power BI Desktop.

• Python: Many new analysts wonder, "Do I need to know Python to be a data analyst?” and the answer is that although SQL and Excel are enough to get started, learning Python expands what you’re capable of and could open doors to more senior and technical roles over time.

Develop your business communication skills

Strong communication skills are crucial for data analysts, but it’s often one of the most overlooked parts in many data analyst career guides.

Analysts create value by influencing business decisions. So, a dashboard no one understands or a finding no one acts on isn’t useful.

In practice, this means analysts must translate technical findings into plain language and structure insights as a cohesive story: context first, then findings and lastly recommendations.

Use this simple exercise to build your data storytelling skills: after every dataset you practice on, write one paragraph explaining what you found and what a business should do about it. Do this consistently and it becomes second nature.

To help you become a better communicator, Capella’s online bachelor’s and master’s programs in analytics include capstone projects, so you learn how to translate classroom theory into real-world application.

Build portfolio projects tied to real problems

Education alone isn’t enough to get hired. Often, you need to show evidence that you can take a messy dataset, make sense of it and tell a business something helpful. That’s what portfolio projects demonstrate.

How to choose data analytics projects

Instead of projects that simply demonstrate technical skills, pick projects that mirror business questions, like “which product is performing well and why?” or “what’s driving customer churn?”

Use publicly available data from Kaggle, data.gov or your city’s open data portal for your project. Host them on GitHub or Tableau Public for easy sharing.

Data analysis portfolio projects essentials

Every portfolio project should include:

• A clearly stated business question
• A brief data cleaning log showing your process
• Two or three visualizations with written interpretation
• A short recommendations section

Ideas for data analysis projects

Here are example ideas for a data analytics project in operations and marketing.

• Operations: Use retail sales and inventory data to identify seasonal demand patterns and inventory risks. Create a dashboard that helps managers determine what products to stock and when.
• Marketing: Analyze campaign performance data to determine which channels or messages generate the highest engagement and conversions. Present recommendations for improving future marketing efforts.

Focus on showing end-to-end analytical thinking through your project rather than perfect technical execution. As you complete projects, you’ll learn to bridge the gap between learning analytics and applying them in actual work settings.

How AI is changing the data analyst role

AI tools and features are reshaping how data analysts work. Tools like Copilot in Excel and business intelligence platforms like Power BI can automate routine data cleaning and speed up report generation. ChatGPT and similar AI tools can also assist with basic SQL query writing.

AI can’t replace human judgment during analysis

AI platforms can surface insights, but they lack the decision-making skills that businesses actually depend on, including:

• Knowing which business problem is worth investigating in the first place
• Understanding the why behind the numbers
• Translating findings into a recommendation that a leadership team can act on
• Identifying bias in datasets and ensuring that conclusions are accurate before they influence decisions

Here’s a concrete example. An AI tool can generate a sales trend report in seconds, but it can’t narrow down the exact reason for a spike in Q3 numbers.

Let’s say the increase was driven by a one-time promotional event instead of genuine revenue growth. Since the AI can’t judge the true reason, it might recommend an increased overall budget, which would be a mistake. That judgment comes from a human analyst who understands the business.

So, when you’re preparing to become a data analyst, you need to prioritize the technical and soft skills that make you a good collaborator with AI platforms.

Analysts who use AI tools can work faster and produce more, but only if you use them effectively. That’s why the majority of Capella’s online IT degrees cover AI and its applications, preparing you to work with these tools rather than be caught off guard by them.

How to become a data analyst with no experience in the field

Education and a good portfolio are essentials for data analytics careers, but you also need experience. If you’re a new graduate or professional from another field, you likely won’t have a previous data-related job title, and that’s okay. Here’s how you can gain relevant experience.

• Explore internal opportunities: Your current workplace can be a great place to find opportunities to work with data. For example, you can volunteer to build a tracker to monitor team performance or analyze results from a recent project. You can get a head start here through Capella’s BS and MS in IT programs, which include work-based learning opportunities.
• Participate in analytics communities: You can join online analytics communities and discussion groups, like DataTalks.Club, Break Into Data, Locally Optimistic and the data analysis subreddit, to share knowledge and ask questions. You can also enter community challenges, like the ones by Kaggle, to build both your skills and visibility in the field.
• Develop a professional network: Along with networking through online communities, you can use LinkedIn for professional networking. Optimize your LinkedIn profile to reflect your skills, connect with other data professionals, share your learning journey, highlight your results and engage with content from the industry.
  

Capella students and graduates can also use the Career Development Center to make it easier to connect with professionals and alumni working in data.

Tips to pursue your first data analyst role

Even strong candidates can struggle to land interviews if they don’t present their skills effectively. Here are five tips to help you stand out:

• Lead with transferable skills on your resume. These are technical or soft skills that you’ve learned in previous roles or internships that are relevant to the data analytics role you’re applying for.
• Add direct links to your portfolio projects. You can include them in your resume, so hiring managers can see your work immediately.
• Prepare for technical interviews. You could practice SQL queries, walk through your data cleaning process or learn how to clearly explain your analytical decisions.
• Quantify your portfolio results. You can use metrics and frame outcomes in a business context. Instead of just describing the process, use statements like “Identified a 14% drop in repeat purchases linked to shipping delays.”
• Consider analyst-adjacent positions to build experience and create pathways into more specialized data analytics positions.

Prepare for your data analytics career path

Becoming a data analyst starts with curiosity and continuous learning. You don’t need advanced technical knowledge or a background in math, just a willingness to consistently practice and grow.

You can use bootcamps or self-study resources to learn the basics, but a structured, online IT degree at Capella, taught by experienced professionals, can give you a strong foundation in analytics. Through career-focused, AI-integrated curricula and real-world projects, Capella’s online IT degrees can help you prepare to pursue diverse opportunities in data analytics.