There is no way to sugarcoat it. You are facing one of the toughest job markets this country has seen in a long time. At the moment, there are very few entry-level data analyst job opportunities. And even the ones that are called entry-level expect you to have a ton of skills from day one.
But giving up isn’t an option. You didn’t study for the last 16+ years to quit now. And many people think the future of data analytics is still quite bright.
So what should you do? The only real path forward is to stop looking for a magic bullet and instead focus on small, actionable steps that gradually improve your chances as a candidate. Build up your entry level data analyst skills, and you will land a job, even in this tough market.
One non-negotiable step is to make sure you have the right skills on your resume. And here’s a spoiler: technical skills alone won’t get you there.
Who Is Looking For These Skills?
When optimizing your resume, you need to think of three people who will review it.
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The Applicant Tracking System (ATS):
This is a computer program that scans your resume first. It checks for keywords and may filter you out if it doesn’t find enough of your entry level data analyst skills. -
The Recruiter:
This person works in HR and usually does not have an analytics background. They sift through resumes and pick out the ones that seem like a good fit. They look for a few key things, even if they don’t fully understand all the details. -
The Hiring Manager:
This is the person who makes the final decision. They know the work well and care deeply about the details. Their opinion is the most important—if they see your resume, you’re one step closer to landing the job.
This guide will focus mainly on the recruiter and hiring manager. You might be thinking, “I’ve sent hundreds of resumes and hardly ever hear back. It must be the ATS.” While that might seem true, chances are many people have looked at your resume and decided not to call you.
Data Analyst Requirements
There is a certain set of entry level data analyst skills that recruiters and hiring managers expect these days. This guide will show you exactly what these skills are and why you need them on your resume.
I have a unique advantage. First, as a career coach for early-career data analysts, I help clients craft resumes that work. I see firsthand what gets callbacks—and what gets ignored.
Second, as a full-time data analyst at a Fortune 500 company, I take part in hiring. I see who makes it past the ATS and recruiters, how hiring managers react to resumes, and what ultimately leads to interviews.
Full disclosure: some of what I’ll share might sound like things you’ve heard before. And some may even feel like corporate buzzwords. I get it—I hate corporate speak too. But ignoring these trends can hurt your chances of getting noticed.
My goal here is simple: to help you spot the blind spots in your resume and give you the best shot at getting it in front of a hiring manager.
Note: While this is meant for data analysts, the same skills can be applied to data science and other data professional roles.
Technical Skills
Technical skills are the hard, specific abilities you need to work with data. , including effective presentation skills. and critical thinking. These are the bare minimum to even be considered for data analyst roles. They include programming languages like SQL, Python, and R, and tools like Tableau, Microsoft Excel, Google Sheets, and Power BI. These skills let you clean data, run statistical analysis, and create clear data visualizations.
In a data analyst role, these skills are part of your skill set used to manage large datasets, run complex analyses, and deliver insights. Employers expect to see these abilities across your Professional Summary, Skills, Experience, Certification, Portfolio, and even Relevant Coursework sections. Make sure your certifications clearly list the tool names so ATS and recruiters don’t miss them.
Here are some sample resume bullet points that show off your technical skills:
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School Project: Developed a Python script that reduced data processing time by 30% on a semester-long project.
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School Club: Led a SQL database project for the club, increasing member data accuracy by 25%.
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Volunteer Organization: Created a Tableau dashboard to track donor activity, boosting campaign efficiency by 20%.
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Food Services Job: Used Google Sheets to log daily sales, identifying trends that cut waste by 15%.
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Retail Job: Analyzed customer purchase data with Excel, revealing patterns that increased sales by 10%.
Remember: Being the most technical candidate won’t automatically land you the job, but not having the right technical skills on your resume can cost you the chance.
Project Delivery
Project delivery is more than just doing what you’re told. It means taking an idea and turning it into a finished analytic deliverable. In data analysis, this means planning and executing projects step by step—even when some parts are out of your control.
Hiring managers aren’t looking for full-blown project managers, but they do want to see that you can create a plan with timelines and dependencies and then follow through. Knowing the difference between agile and waterfall can help, too. The key is to show that you have taken projects from start to finish, and ideally, you’ve done so while collaborating with others.
Here is a closer look at some sample resume bullet points that show your project delivery skills:
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School Project: Led a 5-member team to develop a predictive model for campus energy use, delivering a 30-page report two weeks early and boosting project scores by 15%.
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School Club: Managed a data visualization project for the tech club that analyzed survey feedback and increased event participation by 20%.
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Volunteer Organization: Coordinated a donor database overhaul for a local charity that cut data errors by 35% and streamlined fundraising efforts.
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Food Services Job: Designed a project to analyze daily sales at a busy restaurant, pinpointing inventory waste and reducing costs by 12%.
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Retail Job: Developed a plan to track weekly customer trends in a retail store, which boosted upsell opportunities by 10%.
Collaboration
Collaboration is all about working effectively with others to reach a shared goal. In a professional setting, data analysts work with other analysts, IT teams, business stakeholders, and managers. Sometimes you need help from others—like getting access to data or feedback on your work—and sometimes they need something from you, such as visual representations of data to clarify insights.
A key tool for effective collaboration is the RACI framework. RACI stands for Responsible, Accountable, Consulted, and Informed. This model helps clarify each team member’s role. Most of the time, you’ll be in the Responsible role, doing the work to get the job done. But it’s important to be clear about what you need to succeed and to address roadblocks head-on. Hiring managers want to see that you can coordinate with others to drive real results—not just “play nice.”
Here are some sample resume bullet points that show your collaboration skills:
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School Project: Led a 6-person team on a semester-long business analytics project, coordinating tasks and holding regular check-ins to overcome a technical roadblock, which resulted in earning an A grade and praise for teamwork.
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School Club: Worked with club members to develop a new data tracking system for event planning, gathering input from peers and streamlining communication, which boosted participation by 25%.
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Volunteer Organization: Spearheaded a project to create a volunteer scheduling system at a non-profit, defining roles and responsibilities with staff and volunteers to save 10+ hours per week and improve volunteer satisfaction.
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Food Services Job: Collaborated with kitchen and front-of-house teams to set up a system for tracking daily inventory and orders, reducing service delays by 18% through improved communication.
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Retail Job: Coordinated with a 4-person team to analyze customer sales data for a local store, managing client communication and weekly meetings to ensure timely data delivery that drove a 15% increase in upsell opportunities.
Analytic Translation
Analytic Translation means you don’t just crunch numbers—you make them meaningful. It’s about explaining your technical findings in simple terms so non-technical stakeholders can quickly understand and take action. You interpret data, add business context, and highlight the “so what” behind the numbers.
Instead of overwhelming your audience with details, focus on clear, actionable insights. Strong communication skills show that you can bridge the gap between complex analysis and everyday business decisions. Use it in your professional summary, portfolio project descriptions, and anywhere you describe your experience.
Here are some sample resume bullet points that show your analytic translation skills:
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School Project: Created a one-page infographic to summarize complex data analysis, enabling classmates to grasp key trends and boosting project evaluation scores by 10%.
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School Club: Delivered a concise presentation on club membership data, turning raw numbers into a clear narrative that increased recruitment by 18%.
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Volunteer Organization: Crafted an easy-to-read report on donor trends that linked statistical findings to actionable fundraising ideas, resulting in a 22% increase in donations.
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Food Services Job: Simplified daily sales and inventory data into brief, visual reports for shift managers, leading to a 10% reduction in order errors.
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Retail Job: Translated customer purchasing data into a short, impactful memo that highlighted key buying patterns, contributing to a 12% rise in targeted promotions.
If you want more context, check out this data storytelling overview from Harvard Business School.
Customer Service
Good customer service means treating your stakeholders like valued customers. Imagine walking into a store where someone listens to your needs, understands your questions, and offers a solution that makes your day easier. In data analytics, the same idea applies. Your work must not only be accurate—it also needs to be clear and helpful to the people who depend on it.
By taking the time to understand what your stakeholders need, explaining your findings in simple terms, and being responsive to their questions, you build trust and strong relationships. Even when mistakes happen or deadlines slip, a customer-first mindset ensures that people know you are committed to making things right.
When I interview candidates, I ask: “Can I trust this person one-on-one with an important stakeholder?” If the answer isn’t a confident yes, I move on—even if they’re strong in other areas. Building trust with clear, friendly communication is key to a successful career in data analytics.
Here are some sample resume bullet points that show how you can create a positive customer experience:
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School Project: Collaborated with a simulated client to identify key concerns and tailored a presentation that built trust through clear, concise communication.
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School Club: Hosted a workshop that broke down complex topics into simple terms, ensuring participants felt informed and valued.
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Volunteer Organization: Addressed donor inquiries with clear, supportive explanations, resulting in increased engagement and trust.
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Food Services Job: Managed customer concerns by explaining menu updates and service changes, contributing to a consistently positive experience during peak hours.
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Retail Job: Delivered attentive assistance and clear product information, helping customers feel confident in their choices and boosting overall satisfaction.
People Leadership
At the entry level, people leadership is all about helping your team get better—even if you’re not the manager yet. It means stepping up to share your knowledge, guide your peers, and even mentor others when you can. Showing that you care about your teammates’ growth and success demonstrates that you have the potential to lead in the future.
Here are some sample resume bullet points that show your people leadership skills:
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School Project: Organized study sessions and shared insights with classmates, helping the group improve overall project scores.
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School Club: Mentored new club members by explaining key concepts and best practices, boosting their confidence and participation.
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Volunteer Organization: Provided informal guidance to fellow volunteers, offering tips on data collection and analysis that enhanced team performance.
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Food Services Job: Helped onboard new staff by explaining procedures and sharing effective techniques, which improved team efficiency.
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Retail Job: Assisted colleagues in understanding new systems and processes, leading to smoother operations and better customer service.
Operational Efficiency
Operational efficiency is all about making work processes faster and smoother. through effective data management. It means finding ways to cut down wasted time and effort while producing better results. For entry-level data analysts, this is one of the easiest skills to show off right away.
Many entry-level data analysts inherit routine tasks. A mediocre analyst does the job just fine. A good analyst, however, understands the role of a data analyst and finds ways to improve these tasks. By streamlining processes, errors are reduced and work gets done faster. This means more time for the new projects that help drive the business forward.
When writing your resume, focus on showing how you saved time or cut down mistakes. Here are some sample bullet points:
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School Project: Redesigned the data collection process for a group research project by creating a clear, standardized template, cutting manual work by 30% and saving an estimated 20 hours on the project.
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School Club: Developed an event planning checklist for a school club that streamlined task organization, reducing planning time by 20% and saving nearly 80 hours per year.
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Volunteer Organization: Organized volunteer schedules using an online tool, boosting efficiency by 40% and saving around 90 hours annually by reducing overlaps and miscommunications.
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Food Services Job: Implemented a digital log system to track daily tasks in a food service role, cutting order processing time by 15% and saving approximately 50 hours monthly.
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Retail Job: Optimized stock management by transitioning to a digital inventory system, reducing stock-check time by 25% and saving roughly 100 hours annually.
Pro Tip: When writing your resume, show the hours saved over a year. It makes your impact look bigger, and managers often plan work on an annual basis.
Pro Tip: If you aren’t sure of the exact hours saved because you didn’t track them, use the word “estimated” and provide a rough number that makes sense.
Strategic Thinking
Strategic thinking and critical thinking are the ability to think critically and creatively to solve complex problems and make informed decisions. It means looking at the big picture, anticipating future challenges and opportunities, and developing plans to achieve long-term goals. For entry-level data analysts, this skill is crucial because data-driven insights guide business strategy.
Many new analysts focus only on executing tasks and miss how their work fits into the larger goals of the organization. When that happens, their insights lack impact and don’t help steer the company toward success. A strategic thinker goes beyond their own tasks—they spot potential roadblocks and opportunities, and use data to plan ahead and drive proactive decisions.
Even if you haven’t worked as a data analyst yet, you’ve likely used strategic thinking in other areas. Showcasing these moments proves you can solve problems, plan for the future, and help a business succeed.
Here are some sample resume bullet points that demonstrate strategic thinking:
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School Project: Analyzed social media engagement for a student-run event planning organization, identified top-performing content themes, and developed a content strategy that increased social media followers by 50% and event attendance by 20%.
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School Club: Evaluated historical weather patterns and energy usage data for a university campus project, then developed a predictive model to optimize energy consumption, resulting in a 15% reduction in energy costs.
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Volunteer Organization: Streamlined the adoption process at an animal shelter by creating a new screening and matching system, reducing adoption time by 20% and increasing successful placements by 15%.
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Food Services Job: Noticed a drama club struggled to attract new members, proposed analyzing past event attendance to tailor promotions, which led to a 30% growth in membership after the changes were implemented.
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Retail Job: Reviewed seasonal sales data in a retail setting to identify trends and potential issues, then suggested strategic inventory adjustments that improved overall performance.
Organization
Being organized means keeping things neat and on track. Even if you haven’t worked in a big company yet, you can show that you have what it takes to manage details and responsibilities. Whether it’s handling inventory, planning events, or keeping track of orders, employers want to see that you can create order from chaos. Here are some sample resume bullet points to demonstrate your organizational skills:
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School Project: Coordinated a large group research project by setting clear timelines and managing document submissions, ensuring every milestone was met on schedule.
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School Club: Organized club events by planning schedules, assigning roles, and tracking supplies, resulting in smooth and successful activities.
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Volunteer Organization: Managed a charity fundraiser by coordinating volunteer tasks and keeping all event details in order, which helped the event run flawlessly.
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Food Services Job: Kept track of multiple customer orders in a busy restaurant, ensuring that all tables were served accurately and on time.
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Retail Job: Maintained and organized inventory by implementing a new filing system, reducing restocking time and ensuring product availability.
Time Management
Time management is the skill of planning and using your time wisely. It means knowing what to do first, setting priorities, and avoiding last-minute rushes. With good time management, you can handle many tasks without feeling overwhelmed.
Most data analysts work on several projects at once, each with its own deadline and priority. While your manager sets overall priorities, you’ll need to estimate how long each task will take, adjust when new challenges arise, and ensure all work is completed while keeping everyone satisfied. You may also need to help leadership understand tradeoffs and evaluate the costs and benefits of various deliverables.
Delays and unexpected issues are common. For example, you might have to wait on data from another team or fix unexpected errors. The best data analysts are flexible and always have work lined up so they can start on something else when delays occur.
When it comes to your resume, consider these sample bullet points that show your time management skills:
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School Project: Managed 4 simultaneous school projects with overlapping deadlines, achieving 100% on-time completion and boosting overall grades by 10%.
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School Club: Coordinated a team project by setting clear deadlines and priorities for 5 team members, leading to a 100% on-time submission rate.
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Volunteer Organization: Utilized a digital calendar to juggle part-time work, coursework, and extracurricular activities, achieving a 95% on-time task completion rate.
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Food Services Job: Managed seating arrangements, call-ahead orders, and dining room flow during peak times as a restaurant host, streamlining service and cutting average wait times by 30%.
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Retail Job: Handled both cashier and stocking duties during peak hours by prioritizing tasks, ensuring timely completion, and boosting efficiency by 15%.
What are the essential entry-level data analyst skills?
Essential skills include technical tools like SQL, Python, Tableau, Excel, R, and Power BI. Soft skills like organization, strategic thinking, customer service, analytic translation, and problem solving are also key.
Do I Need All Of These On Your Resume?
Short answer: No.
While essential entry-level data analyst skills include technical tools like SQL, Python, Tableau, Excel, R, and Power BI, plus soft skills such as organization, strategic thinking, customer service, analytic translation, and problem solving, you don’t need to list every single one. Focus on your data accomplishments first and weave these skills into your achievements. This way, you show real impact rather than just a laundry list of skills.
What are the best ways for entry-level data analysts to gain practical experience?
To gain practical experience, entry-level data analysts can work on real-world projects, internships, or freelance jobs. They can also participate in data analysis competitions, contribute to open-source projects, and attend a boot camp, seek mentorship from experienced data analysts in the field, or consider completing a bachelor’s degree program, master’s degree, or professional certificate from a bootcamp to enhance their skills.
Do I Need To Know Machine Learning To Be A Data Analyst?
While machine learning, data mining, and other big data skills can be useful in data analytics, most entry-level data analyst roles focus on foundational tasks like data cleaning, data mining, visualization, and basic statistical analysis using tools such as Excel, SQL, Tableau, or Python. Rather than needing to build complex models like a data scientist, you’ll be expected to derive insights from data and support business decisions. If you’re interested in machine learning and pursuing a future career in this field, you can learn it over time, but it isn’t a must-have for starting your career as a data analyst.
Do I Need A Github Page For My Portfolio?
Short answer: No, but you need portfolio projects and a clear way for people to see them.
Hiring managers typically aren’t interested in reading lines of code. They want to see your analysis and how you present your results. Rather than a raw code repository, focus on creating polished portfolio projects that showcase your ability to solve real problems. Emphasize clear visualizations, actionable insights, and the overall impact of your work. A personal website or a dedicated portfolio page where you present these projects is more effective than a Github profile filled with code.