How to Become a Data Analyst: A Complete Guide [2025 Update]

The demand for skilled professionals who can utilize data for more intelligent decision-making continues to increase with the surge in the development and adoption of artificial intelligence and machine learning technology. Today, data analytics is one of the most impactful and rewarding careers.

According to the U.S. Bureau of Labor Statistics, the average salary of a data professional in the US in 2023 is $108,020, and th projected job growth for data professionals by 2030 is 36%. If you’ve been considering starting a career as a data analyst, this is a good time to start. This post outlines the necessary steps on how to become a data analyst.

Six Steps to Becoming a Data Analyst

To begin your journey as a data analyst, here are the six significant steps you would need to take:

1. Understanding Data Analyst Job Roles
2. Identifying Required Essential Skills
3. Gaining Essential Knowledge
4. Creating a Data Analytics Portfolio
5. Practising Presenting Findings
6. Getting an Entry-Level Data Analyst Job

Step 1: Understand Data Analyst Responsibilities 

It is crucial to understand what a career as a data analyst entails. Data analysts collect, organize, and interpret data to provide implementable conclusions for organizations. Your job would be to help management make informed decisions based on the data you collate and analyze.

As a data analyst, your tasks would typically include:

• Gathering data from various relevant and reliable sources.
• Identifying trends or patterns in the data you have collated.
• Creating visual representations such as charts or graphs to make data understandable.
• Preparing reports to communicate your findings effectively.
• Cleaning and analyzing large data sets.
• Collaborating with teams to ensure alignment of business objectives.

While the specifics may vary between industries, the common goal is using data to guide smarter, more informed decisions. Understanding a data analyst’s responsibilities will help align your learning path with recruiters’ expectations and ease your transition into the field.

Step 2: Identify Required Essential Skills

To become a successful data analyst, one must build a strong foundation of both technical and non-technical skills, such as:

Technical Data Analyst Skill Requirement

• Mastering Fundamental Concepts in Statistics: Learn measures like mean, median, and mode to interpret data effectively and draw accurate conclusions.
• Data Cleaning and Preparation: Learn to clean and prepare raw data for analysis.
• Data Visualization: Become adept at transforming numbers into meaningful charts and graphs.
• Proficiency in Programming Languages: Be skilled in languages like Python, R, and SQL, which are staples in data-related fields.
• Familiarity with Data Analysis Tools: Get comfortable with tools like Tableau, Excel, and big data technologies like Spark and Hadoop to expand your analysis capabilities.

Technical Data Analyst Skill Requirement

• Problem-Solving: Improve your problem-solving ability, which is crucial for tackling complex analytical challenges.
• Communication: Enhance your communication skills to collaborate effectively with non-technical project stakeholders.
• Soft Skills Development: Cultivate skills like attention to detail, organization, and data storytelling to make your data presentations actionable and engaging.

If you are switching to data analysis from another tech or non-tech role, a good starting point is to do a skills audit and identify relevant skills you already have from your past roles. For example, if you’re switching from a role in finance, you already have problem-solving, critical thinking, or basic Excel skills.

Step 3: Gain Essential Data Analysis Knowledge

After identifying the required skills and doing a skills audit, the next step is to draw up a learning plan containing what you’ll learn and where you’ll learn them. Various options exist, such as:

• Entry-Level Training: These are training specially crafted for beginners, with a curriculum highly targeted at entry-level education. A stellar example is the EvolveWithTech program, which provides hands-on, beginner-focused data analyst training with interactive live lessons from industry experts, career tapestry, and micro-internship opportunities, ensuring you gain practical skills needed to kickstart your data analysis career.
• Online Courses: These are readily available and affordable but largely theoretical. Online courses are very flexible, allowing you to study at your own pace, but they require intense discipline as it’s common practice to start an online course and abandon it halfway. Online courses also lack personalized guidance, which can be challenging, especially for tech courses like data analysis, where learners usually need more support and personalized guidance.
• Bootcamps: Extremely intensive programs designed to teach core skills in a condensed timeframe. Some bootcamps also offer career services such as resume reviews and interview prep. However, the fast pace can be overwhelming for complete beginners.
• University Degrees: If you prefer a traditional academic approach, a degree in data science, statistics, or computer science can provide a deeper understanding of analytical concepts and theoretical principles. This route offers structured, extensive coverage of broad topics but takes more time (3-4 years) and can be very expensive.

It’s worth mentioning that certifications validate your skills to potential employers, and practical projects demonstrate your ability to apply what you’ve learned to real-world scenarios.

When deciding on a learning path, you should choose one tailored towards beginners, which awards a completion certificate and requires you to complete a project to add to your portfolio, like the Evolve With Tech Data Analysis Program.

Step 4: Create a “Standout” Data Analytics Portfolio

Building a strong portfolio is crucial to showcasing your skills and standing out to potential employers. Your portfolio should contain projects that demonstrate your technical abilities and problem-solving capacity. Here are a few tips to create a portfolio that captures attention:

• Real-Life Projects: Include projects from actual real-world experience, such as volunteer opportunities, open-source collaborations, and freelance gigs. These projects show you’re actively seeking opportunities to gain experience and acquire practical knowledge.
• Use Engaging Visualizations: Visualization is an essential part of data storytelling. Hence, use clear, concise, and relevant visualizations, but don’t go overboard. Also, highlight all the tools used to create the visualizations and briefly explain your choice of tool and chart. It’s easy to overlook visualizations because you assume recruiters are technical but don’t, as they’re one of the key factors recruiters look out for.
• Use Real-World Data: If you have a choice between actual real-world data and demo data, choose real-world data; these are readily available online on platforms like NASA’s open data, climate records from the National Centers for Environmental Information (NCEI), Kaggle datasets, etc.
• Showcase Your Technical Skills: It’s tempting to start and complete relatively easy projects and focus on the quantity of projects – but don’t. Instead, focus on the quality of projects – choose challenging projects that showcase your technical skills in data cleaning, exploratory data analysis (EDA), statistical analysis, creating insightful visualizations, etc. These technical projects help you stand out from the competition. For example, you could analyze trends in climate data, customer behaviour, or even public sentiment on trending topics.
• Make Your Portfolio Easily Accessible: Recruiters typically receive hundreds of applications; if there’s little friction to accessing your portfolio, they will likely not check it. It is advisable to have your portfolio online on sites like GitHub, Kaggle, Tableau Public, etc.

A robust, technical, and extensive portfolio demonstrates your technical expertise and signals your commitment to growth. With a strong portfolio, you can confidently showcase your skills and creativity.

Step 5: Practice Presenting Findings

Your ability to effectively present and communicate findings is essential because, as a data analyst, one of your main goals is to drive action, which requires mastering storytelling with data and presentation skills.

• Storytelling With Data: Storytelling transforms raw data into meaningful insights that other stakeholders can understand and utilize. Storytelling with data aims to make the data you have collated easy to understand for both technical and non-technical audiences. The key elements of storytelling with data are:
  • Context: Set the scene for your data.
  • Visuals: Use charts, graphs, and infographics.
  • Narrative: Craft a storyline that guides the viewer. It’s essential also to identify the key message or insight you want to pass across, choose the proper data visualization, and make your story relatable to your audience.

• Analysis Presentation: Learn to create impactful charts and graphs with great visuals for your presentations. Keep it simple to avoid overwhelming your audience, and practice effective ways to deliver your findings. PowerPoint or Google Slides help craft presentations that effectively balance your text and visuals. Always be clear and ensure you’re tailoring your message to your audience’s level of understanding.

Step 6: Get an Entry-Level Data Analyst Job

Numerous sub-niches exist within the data analysis field, such as Business Analyst, Financial Data Analyst, Marketing Analyst, Product Analyst, Operations Data Analyst, Healthcare Data Analyst, Web Analyst, Sales Analyst, HR Analyst, Customer Data Analyst, and Quality Assurance Analyst. Consider an entry role in any of these fields.

For entry-level positions, recruiters look for a mix of:

• Proficiency in Excel, SQL, and Python.
• Experience with tools like Tableau or Power BI.
• Ability to translate complex data into actionable insights.
• Soft skills like critical thinking, problem-solving, and communication are essential.

You don’t necessarily have to wait until you have all these requirements; some entry-level roles are willing to train you once you cover the basics. So, at every point of your learning journey, be on the lookout and apply for entry-level roles.

Build An Impressive Resume

Build a strong resume to showcase your relevant skills and experience, following these tips:

• Use a clean, easy-to-read format. Research shows that 83% of recruiters say they’re more likely to hire a candidate who has a well-formatted resume.
• Include sections highlighting your proficiency in relevant tools and your soft skills. According to McKinsey, 41% of recruiters first look for skills on a resume.
• Include details of data projects you have handled. For instance, you could say, “Analyzed customer retention rates using Python and created visualizations in Tableau.”
• Add certifications you have earned.
• Have a resume template you can always modify to highlight the skills and experiences required for each job you apply for. 83% of recruiters say they’re more likely to hire a candidate who has tailored their resume to the job they’re applying for.

Build a Strong Online Presence

A strong online presence can set you apart, so maintain an updated profile on professional platforms like LinkedIn. 40% of recruiters say they have hired a candidate based on their LinkedIn profile alone (HBR). Share insights on data-related topics regularly and connect with professionals in the analytics field.

Your profile should include your certifications, projects, and job experiences. Also, optimize your profile for search by:

• Including relevant keywords in your headline & description.
• Writing a keyword-rich summary that highlights your skills, experience, and aspirations.
• Including industry-specific terms and phrases recruiters and potential employers will likely use when searching.
• Using relevant hashtags.
• Regularly asking for recommendations from relevant connections.

Also, keep an active GitHub profile showcasing your projects and skills, and regularly comment on data analysis topics and answer relevant questions on forums like Reddit & Quora.

Apply for Internships or Graduate Trainee Programs

Internships are a good place to start your data analysis career; in most cases, internships lead to placement in the company. If it doesn’t, you’re still in a better position to get an entry-level job elsewhere – it’s a win-win situation.

Internships help you gain valuable experience by exposing you to industry-standard tools and giving you a front-row seat to data analysis processes. They effectively bridge the gap between learning and working. Platforms like LinkedIn, Indeed & Glassdoor, are excellent for finding internship opportunities.

Network With Industry Stakeholders & Peers

As someone just starting your data analysis career, networking with others in the industry is invaluable. Networking helps you learn from established professionals and access opportunities. A common question is: where do I find other data analysts to connect with? 

To do so:

• Join communities for data analysts – online and offline.
• Join data analyst LinkedIn groups.
• Join relevant active groups on forums like Quora, Discord, and Reddit.
• Participate in online hackathons or data competitions.

Remember that networking is not only about landing a job but also building professional relationships that will benefit you as your career grows.

Prepare Adequately for Interviews

Data Analyst interviews often include technical and behavioural components, so being adequately prepared is key. Practice common questions like:

• “How would you handle missing data?”
• “Describe a time you used data to solve a problem.”

Also, be prepared for technical assessments such as:

• Performing Excel tasks.
• Debugging Python scripts.

Before any potential interview, rehearse your answers to boost your confidence. During the interview, ask insightful questions.

Strategies For Data Analyst Job Search 

Searching for a Data Analyst job is similar to searching for other jobs in the tech industry. You’ll typically find job listings online on:

• Job Boards: 7 people are hired every minute on LinkedIn alone, excluding other job boards like Indeed, Glassdoor, etc., showing these platforms are effective sources of finding job openings. Regularly check for data analyst openings; you can also set job alerts to notify you of new openings as soon as they appear.
• Career Pages: You could also identify companies in industries that interest you, bookmark their career pages, and apply directly through career pages on their websites when relevant openings are posted.
• Work With a Recruiter: Recruiters are career matchmakers; companies hire them to search, screen, and connect companies to qualified talent. To find a suitable recruiter, do your homework. Recruiters typically specialize across job titles and industries. Look out for recruiters who post data analyst roles and have track records of success in helping others get data analyst roles in the past.

Be resilient and consistent with your job search, and you’ll hopefully land a data analyst role and start building your experience.

Becoming a Data Analyst with Evolve With Tech

EvolveWithTech offers flexible, beginner-friendly programs that provide career roadmaps, expert instruction, and real-world internships to help you thrive in tech.

Our data analysis program typically lasts six to eight weeks and focuses on transforming you into a data analysis powerhouse. We deliver practical tools and support so you can confidently begin your journey as a data analyst.

Our Career Tapestry Snapshot software analyzes your strengths and interests using SWOT insights to create a personalized career roadmap for your tech journey. Learn from industry experts with interactive one-on-one lessons, hands-on projects, and quizzes to solidify your skills. 

At EvolveWithTech, you will gain real-world experiences to enhance your portfolio and join a supportive community of tech learners to accelerate your growth.

Getting started with our data analyst program at EvolveWithTech is straightforward. The process begins by:

• Registering your interest by completing the form to confirm your commitment.
• Join our live introduction webinar, where we’ll explain the program, address your questions, and guide you through the process.
• After the webinar, we’ll pair you with a personal advisor to help craft your tailored career roadmap based on a personalized assessment called the Career Tapestry Snapshot, available for just $25.
• Secure your spot in the next cohort by registering for the Evolve Pod for $475, which confirms your enrollment.

After payment is finalized, your advisor will provide class schedules, log-in details, and everything you need to start your exciting tech journey. Our program is designed to accommodate working professionals, and we offer multiple schedule options.

How do you become a data analyst with no experience?

Breaking into data analytics without prior experience may seem challenging, but it’s not impossible. Get entry-level tech education from organizations like EvolveWithTech, which offers:

• Live, hands-on training.
• Guidance through completing a capstone project to help you build a portfolio.
• Offers micro-internships where you work on real-world datasets.

Additionally, contributing to open-source projects and joining online communities would help you gain practical exposure. According to Glassdoor, entry-level data analysts earn an average of $56,000 annually in the U.S., making this a rewarding career to start—even with no prior experience.

How do you become a data analyst without a degree?

With the rise of alternative education pathways, a data analysis degree is no longer mandatory for most data analyst roles. Most hiring managers now prioritize skills over formal qualifications.

Learning online is highly accessible and cost-effective for gaining valuable skills in the data analytics field. You have a flexible schedule and access to one-on-one class sessions, making balancing learning with other commitments easy. However, selecting the right learning platform and staying disciplined is essential.

How long does it take to become a data analyst?

The time it takes to become a data analyst depends on your current skillset and the career path you choose. If you transition from related fields like finance or IT, the timeline may be shorter as you already possess transferable skills.

Frequently Asked Questions (FAQs)

Conclusion and Next Steps

In this article, we have discussed the steps required to become a data analyst with no prior experience: Learning the responsibilities of a data analyst, Identifying and developing technical and soft skills, Gaining essential knowledge, Creating a portfolio and showcasing your skills, Presenting your findings, and Getting an Entry-Level data analyst job.

Ready to start your data analyst career journey? Evolve With Tech’s Data Analyst evolution track is the best place to start. Visit evolvewith.tech/data-analysis to get started today.