Pathstream logo

How to get an entry-level data analyst job with no experience or a degree (+ resume examples)

an entry-level data analyst job without experience or a degree

by Lily Malota | Jun 9, 2022

So you’ve been itching to grow within your organization or switch jobs. You did your research, and you want to become a data analyst. It makes sense, demand for data analysts is booming, and it’s a top career choice . But there’s just one problem. You don’t have the technical skills, experience, or college degree to get an entry-level data analyst job. 

We have good news for you – becoming an entry-level data analyst without experience, or even a college degree is possible. This article will explain how to explore a career in data analytics and provide resume examples that worked for Pathstream students!

Table of Contents

Can I become a Data Analyst with no experience or degree?

8 Steps to becoming a Data Analyst without a college degree or experience:

Identify your transferable skills.

Learn Data skills.

Take a certificate program.

Build a Data Analyst portfolio.

Set up alerts for entry-level data analyst roles.

Focus your effort on the top industries hiring.

Update your resume.

Prepare for your first data analyst interview.

Why it’s possible to get an entry-level data analyst job with no previous experience or college degree?

Many employers are open to hiring people without prior experience. We’ve seen it happen . There are a couple of reasons that make the data analytics industry easier to enter:  

8 Steps to becoming a Data Analyst without a college degree or experience

Step 1: identify your transferable skills..

It’s easy to forget that you most likely have a solid skillset you developed during your career. Sit down and  audit the skills  you currently possess. Think about the skills you identify and how they might transfer to a data analytics career.

Soft skills needed in data

Step 2: Learn the technical skills.

We recommend determining your skills gaps by reading “ Top skills data analysts need in 2022″. Then look for courses to teach you these skills. You can opt for a self-paced approach and sign-up for free classes online. If you’re looking for a program with more guidance but still a flexible pace, consider certificate programs. 

Step 3: Consider a certificate program.

You don’t need a data analytics degree to become a data analyst, but you need to build your technical skills and foundational knowledge. The best way to do this quickly is by enrolling in a  certificate program . The best programs are project-based and provide career service resources to students.

Protip:  If you’re concerned about the program’s cost, look into your employer’s education benefits. You may be eligible for  tuition reimbursement  through your employer, or your employer might cover the expense.  Find out if you’re eligible here .

first job data analyst

Step 4: Develop a Data Analyst portfolio.

If you have no data analytics experience, you need a portfolio. Employers want proof that you can apply what you know to real projects. You can build a portfolio using the projects from your certificate program, freelancing, or volunteering your data skills to a small business or nonprofit.

The types of projects that you should include in your portfolio are those that demonstrate your ability to:

Want to get started with your portfolio? Click here to read our guide for building a Data Analyst portfolio to get you hired.

Step 5: Set up alerts for entry-level data analyst roles.

If you’re looking for opportunities outside of your organization, then set up alerts on job boards. This step is often overlooked but makes your life easier! You can do this on Indeed, LinkedIn, Glassdoor, and Wayup to notify you when a data analyst job that meets your criteria is posted. You can also create a shortlist of companies you would like to work for and schedule time on your calendar to monitor their job boards. Connect with people who work at your target companies and set up an  informational interview . Remember, most jobs are filled through networking, so don’t be afraid to put yourself out there.

Step 6: Focus your effort on the top industries hiring.

If you aren’t particularly passionate about entering a specific industry and want to learn more about which industries are looking to hire more data analysts, check out this shortlist of 5 industries hiring data analysts and even include the top 3 companies hiring in each sector.  

Check out: Top industries and companies hiring data analysts

Step 7: Update your resume.

Once you’re confident a job posting is what you’re looking for, edit your resume to highlight relevant experience aligned with the job requirements. Make sure you proofread your resume and ask someone to provide feedback.

Here are some more resources to help you:

Step 8: Prepare for your first data analyst interview.

Great job; you lined up a few interviews! This last tip is to help you set yourself up for success and prepare to ace the interview. Most companies will set up a 30-40 minutes phone screen with their recruiters or hiring managers. This interview helps them understand your background and potential fit for the role. We put together a few blog posts to help you stand out during your interview for an entry-level data analyst role.

Check out and click here to download our resume examples below !

Data Analyst Resume no college experience

Are you ready to start your new career?

Whether you’re changing jobs or looking to get promoted within your organization, Pathstream can help you become an entry-level data analyst. We designed the Data Analytics Certificate Program to help you expand your knowledge of data analytics and learn how to use the right tools for a data job. You can visit our Tableau Data Analytics page here if you’d like to learn more about the certificate – including a detailed syllabus . 

first job data analyst

Was this helpful?

Thanks! What made it helpful?

How could we improve this post?

first job data analyst

Become a Data Analyst in 6 months.

first job data analyst

Learn how students upskilled their career.

Suggested Articles

career pathing

Career Mobility for Your Frontline Workforce

Education benefits, by industry, for insurance, for banking, "pathstream could be described as a one-stop shop that provides industry-standard training and employment resources and support. i was very pleased with the training i received.”.

DawnMarie Boursiquot H&R Block

first job data analyst

Upskill Your Team

Retain your frontline workforce, build a diverse talent pipeline, lead digital transformation, "through pathstream, i learned analytics, how to organize data, create databases, and pull actionable insights. with these skills, i found a way for us to speed up processing freight, and our department broke records.".

Portland Reed Amazon

first job data analyst

Resource Library

"my company launched salesforce, and i wanted to learn to use it properly. a program like pathstream helps employees like me fine-tune specific skills. these tech skills will further our careers at our company.".

first job data analyst

"It’s hard to effectively summarize my skills and experience on a resume, but I got some excellent feedback from my career coach at Pathstream. I really loved how they helped me highlight the project work I did in the Pathstream courses. I recently got hired as a Pricing Analyst at my company."

Robert Guise Waste Management

first job data analyst

How to Become a Data Analyst with No Experience

So you want to become a data analyst, but there’s one thing bothering you. You don’t have any industry experience, and you’ve been working (or studying) in a completely unrelated field.

Now, as you consider a big career change, you’re wondering: Is it really possible to make the switch? And what are your chances of getting hired?

The short answer is yes, it’s entirely possible—and yes, employers will be open to hiring you (even without any prior experience). In this post, we’ll explain exactly why and how.

We’ll answer the following questions:

Ready? Let’s go.

1. Is it possible to become a data analyst with no previous experience?

We’ll cut to the chase: It is absolutely possible to become a data analyst, even if you’re starting from scratch and don’t have any industry experience.

How can we be so sure?

There are several factors that make the data job market relatively accessible for newcomers:

Let’s take a look at these in more detail.

The data industry is booming

In 2019, the global data analytics market was valued at $49 billion USD. That’s more than double what it was worth in 2015. And, from 2020 to 2023, the market is projected to grow at a rate of 30% each year.

This rapid industry growth is reflected directly in the data analytics job market. In their Jobs of Tomorrow Report (2020) , the World Economic Forum highlights seven high-growth emerging professions, with data and artificial intelligence (AI) showing the highest growth rate at 41% per year.

Ultimately, a burgeoning data market means a wealth of job opportunities for data professionals—and, at present, the demand far outweighs the supply.

The data talent shortage

The data market is growing at a rapid pace, and businesses are desperately trying to keep up. Data-driven organizations consistently outperform their competitors , so it makes sense that hiring data experts will be an increasing priority across all industries. At the moment, though, we’re seeing something of a data skills gap. In a study conducted by NTUC LearningHub , 93% of working professionals said that their workforce is not achieving optimal productivity due to a lack of data skills.

This handy infographic put together by QuantHub captures the difficulties companies are facing when it comes to hiring skilled data professionals. The following stats are especially interesting:

In short: Data analysts are in high demand, putting newcomers in a great position. The jobs are there; as long as you’ve mastered (and can demonstrate) the right skills, there’s nothing to stop you getting a foot in the door.

CareerFoundry graduate Chad is a great example of this. Having studied History in university, he worked as a tech recruiter until he decided to take our Data Analytics Program and change careers. Despite having no prior experience in the industry, he got a job as a data analyst for British newspaper  The Telegraph ! It’s a fascinating, but not uncommon, story these days.

Data analysts rely on a vast array of transferable skills

Aside from the fact that data analysts are in high demand, the role itself requires a vast array of skills—many of which you’ll bring with you from other work and life experiences.

Some key transferable skills that will set you in good stead include:

You’ve no doubt picked up at least some of these skills already—be it at school, at work, or simply by interacting with different people. In the absence of data-specific experience, these transferable skills can help you demonstrate your suitability for data analytics jobs. And, with employers placing increasing importance on soft skills , it’s certainly worth highlighting these in your applications. We’ll show you how to do this in the next section.

Related watching: I s working in data analytics a good career fit for you?

2. How to become a data analyst from scratch (actionable steps)

We now know it’s possible to get hired as a data analyst, even without any previous experience on your resumé. First, though, you need to learn the necessary skills and start to market yourself as a data analyst. Here are some practical steps you can follow to get your career change underway:

1. Complete a data analytics certification

You don’t need a full-blown degree to become a data analyst, but you do need a structured and formal approach to learning the necessary skills. The best (and most flexible) way to do so is through a project-based course. Some key things to look for when choosing a course are:

For help finding the right course, take a look at this comparison of the best data analytics certification programs .

2. Polish up your data analytics portfolio

Data analytics is a hands-on field, and employers want to see proof that you can apply what you know to real projects. If you don’t have any real-world experience, you might be wondering what you could possibly include in your data portfolio. Here are some ideas:

You can learn more about how to build a professional data analytics portfolio in this guide .

3. Identify (and emphasize) your transferable skills

As you immerse yourself in learning new skills, it’s easy to forget that you’ve already got a pretty solid skillset under your belt—and that it will add to the value you bring as a data analyst. If you’re brand new to the field of data, it’s especially important to draw parallels between your previous experience and your new career. Spend some time identifying your core hard and soft skills, and think about how they might be transferred to data analytics.

Perhaps you’ve got a marketing background and are already familiar with some basic analytics tools. Maybe you’re a teacher, which makes you great at explaining things—an excellent skill when it comes to presenting your data insights and explaining what they mean to non-technical stakeholders. Can you see how seemingly unrelated experience will actually set you apart as an excellent data analyst? The trick is to recognize your value and convey it to employers through your portfolio, your resumé, and how you talk about yourself in interviews.

3. How to increase your chances of getting hired

You’re learning the necessary skills and building your portfolio. What else can you do to increase your chances of getting hired?

In the absence of industry experience, the best thing you can do to sharpen your competitive edge is to recognize the unique value you bring as a newcomer to the field . This advice comes from Mike McCulloch, Head of Career Services at CareerFoundry, who specializes in coaching graduates through their career change.

Mike points out that, rather than being a setback, having no prior experience in the industry is actually seen as a major asset. As Mike explains: “Newcomers don’t come with any of the preconceptions that mid-level professionals do. They see the business and its challenges through fresh eyes, and are therefore able to approach it from completely new angles. They don’t yet know what’s possible, so they ask different and unexpected questions. Not only does this keep seniors on their toes; it also helps the business to find new solutions to old problems.”

There are other advantages to hiring complete beginners. For one, it gives senior team members the opportunity to mentor someone. Another benefit that many companies will appreciate is the chance to train someone from scratch and nurture them for future career growth. Good hiring managers know that employees are an investment, and the best companies will see your newcomer status as an opportunity rather than a setback.

Many industry experts have written about why companies should hire entry-level professionals and the benefits they can expect if they do. Bear this in mind both in your job search and when speaking with hiring managers; if you’re well-versed in the value you bring, it’ll be easier for you to talk about it and get it across to others.

Ultimately, it’s all about how you market yourself. In addition to mastering the necessary skills, you need to reframe how you think about your lack of experience. Perhaps it’s not a deficit after all, and will actually help you stand out from the crowd!

4. What kinds of companies can you expect to work for?

Data analysts are among the most in-demand professionals , and you’ll find that, once you’ve mastered the core skills, you can work in almost any industry. When you’re just starting out, you can expect to land the job title of “data analyst” or “junior data analyst.” More specialized roles, such as healthcare data analyst , will require some industry experience.

As a newly qualified analyst, you’re likely to find job opportunities in the following sectors:

…to name just a few! For a more specific idea of the opportunities available to you as a newly qualified data analyst, it’s worth searching for “data analyst” or “junior analyst” positions in your local area. Browse sites like LinkedIn , Indeed , and Glassdoor for a well-rounded view of the current job market.

How much do entry-level data analysts earn?

How much you can expect to earn in your first data analyst job depends on where you (or the job) are based, and the sector you’re going into. According to data from Indeed , the average base salary for a junior data analyst in the United States is $77,573 USD. That’s considerably higher than the national average income of $54,132, so it’s not a bad starting point!

You can learn more about entry-level data analyst salaries in this article .

5. Key takeaways and next steps

The data market is growing exponentially, and the demand for skilled data analysts is growing with it. As a newcomer to the industry, you have plenty of value to offer—and not just despite  your lack of experience, but in many ways, because of it . Ready to get the ball rolling? Here’s a free introductory data analytics short course to ease you in. And, if you’d like to learn more about forging a career in data, check out the following:

first job data analyst

Towards Data Science

Rashi Desai

Jan 23, 2022


How I got my first job in Data Analytics with no prior full-time experience

It’s never too late to learn & unlearn for that bookmarked job.

Hi, I am Rashi, a Data Analyst at Blue Cross Blue Shield based out of Chicago, and here’s a story of securing my first job and other offers with no prior full-time work experience.

In the ever-expanding technological world of today, there are new job roles posted each day on company portals, and in the race to the finish line, candidates are forced to apply for any and every job role in the hope to secure one. This becomes especially difficult for new grads or people switching careers.

The book of business expects new hires to start adding value to the organization from day 1 while nobody gives you a job without experience, and you can’t gain experience without a job. Now, if you are at a point considering a big career change or searching for a job post-graduation, and if you’re wondering: do I have a chance of getting hired?

The short answer is yes. Employers (if not all) will be open to hiring you even without any prior experience and this blog is my story to explain exactly how that is possible — things that helped me secure the job!

1. Work on (a lot of) variety of projects

When I started with Data Science in 2018, I pivoted to fostering one skill at a time, and now working full-time as a data professional I can say, the most efficient way to master anything data is by doing a lot of projects across skills. If you are learning Python at this point, learn SQL after that, then R, then Tableau, and while you move on to learn more skills, work on the projects for the skills added to your profile.

Learn the concepts in-depth, their use cases, their implementation, and troubleshooting the issues. And what better than building an amazing portfolio while learning data science. From sentiment analysis to complex Tableau dashboards, image recognition to predictive modeling, do whatever interests you! Nothing goes to waste.

Consider certification and you have a capstone project right there! You can learn skills and hone important data analytics skills. Reach out to your network, businesses or take up freelancing projects to do some near real-world analytics work.

The whole point is to equip yourself with the skills demanded by a business rather than picking up courses while job hunting.

2. Internship experiences

Internships are hands-down the best gateway to gain real-world data analytics experience. No project or certification can prepare you as well-rounded as internships do. The large positive about securing good internships is the full-time offer you land into at the end of your internship (for most companies) And even if they can’t commit to offering you a full-time job, the experience makes it all worth it.

I interned with PepsiCo over the summer and then two semesters (Fall 2020 and Spring 2021) during grad school. The business acumen, integrating data with business, organizational behavior, team building, communication skills, the internship prepared me for the job.

Internships are truly one of the best ways to fill your data skills gap and you realize the value of transferable skills within the data analytics field once you start with a full-time job.

I’ve known many peers and friends who failed to secure an internship the reason being not starting the prep before time. Eventually, everyone lands up with a job, a day or two later. If you ask me, the countdown begins the day you decide to take on data as your career. Certifications, coding practice, working on novel projects, publishing a paper, or indulging in a data-related activity (such as writing a blog) — these set up the pitch for you and help you even at the time of your full-time job interview.

3. Networking and having a mentor

The perception around networking is balderdash (a better word for crap). For most people networking is about getting referrals or opening doors to more jobs, connecting to hiring managers BUT networking is all about gaining knowledge of industry expectations, what are hiring managers looking for in an ideal candidate, take negotiation lessons. The people you network with always leave you with an advice or two.

I’ve always focused on networking with professionals who resemble what I want to be in the next 5 years and asking questions that help me pivot in the right direction.

Having a mentor is the next step in gaining clarity towards your goal. You may or may not believe in all their values but a mentor can really help you navigate and prepare for the cut-throat job world. This can be anyone — your professor, internship manager, a peer from school, or someone you connected with over LinkedIn after attending the same meetup.

I’ve never felt motivated by an external force, however, things people say stay with me, and in one of my random connects with someone during my internship, my mentor said something on the lines of —

“Businesses will have new problems everyday but as a data professional, it’s important for you to understand what you like to work on and more importantly, what you don’t”

4. Identifying your passion

From the advice my mentor gave me, I started exploring towards the end of my internship what I enjoyed working on and made sure to communicate that through my resume. Not everyone is carved out to work as a Data Scientist and just because everyone is doing it doesn’t necessarily mean you have to do it too — 90% of job applications I applied to were for the Data Analyst role.

I had my inclination for a role where I get to use data for the business — for better-informed decision-making and while these words sound jargon, trust me when I say you can really present a story with data in your job.

From working on projects, getting certifications, internships, networking, and reading about the new in technology (data in the spotlight), make you better understand the needs and demands of a job role and will 10000% save you from the frustration of rejections.

You do not need to apply to 500 roles!

** Lessons learned **

If you’re looking to start a new job, it is quintessential to be hands-on and apply practical exposure to stand out. That is what equates to the confidence you can get from a real job experience.

That’s it from my end for this blog. Thank you for reading! Let me know in the comments what has been your journey in data and what are looking for in 2022!

If you enjoy reading stories like these, consider signing up to become a Medium member from this link !

Happy Data Tenting!

Rashi is a data wiz from Chicago who loves to visualize data and create insightful stories to communicate business insights. She’s a full-time healthcare data analyst and blogs about data on weekends with a good cup of hot chocolate…

More from Towards Data Science

Your home for data science. A Medium publication sharing concepts, ideas and codes.

About Help Terms Privacy

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store

Rashi Desai

Data Analyst from Chicago & Top 100 writer. Everyone loves a good story & I tell mine with data!

Text to speech

Data Science

Job guarantee

8 Entry-Level Data Analyst Jobs to Start Your Career

Sakshi Gupta

In this article

What Does an Entry-Level Data Analyst Do?

Is it hard to land an entry-level data analyst job, 8 entry-level data analyst jobs, what skills do you need to land an entry-level role as a data analyst, the best places to find entry-level data analyst jobs, tips to land your first data analyst role, faqs about landing an entry-level data analyst job.

Most people who want to join the field of data analytics and science, but haven’t started their careers yet, aren’t aware that the job title of “data analyst” actually encompasses many different roles. So if you’re interested in becoming a data scientist, then you’ll need to have an understanding of the data analyst landscape, particularly entry-level positions. 

That’s why we’ve created this guide. Below, we’ll look at eight of the most common data analyst roles across industries. Keep reading to find out what kind of data analyst roles are out there and understand which one is right for you. 

Entry-level data analysts work on small parts of larger data analysis projects . Their broad responsibilities are to collect and analyze complex datasets, and their eventual goal is to produce insights that can help their company make better strategic decisions. 

Related Read: Entry Level Data Analyst Salary Guide: Who Makes What?

Not if you have the right qualifications. Companies are open to hiring candidates who’ve completed data analytics bootcamps , so you don’t necessarily need to invest in a college degree. Apart from that, you should work on creating a data analyst portfolio by working on your own real-world projects.   

Let’s now take a look at what different kinds of entry-level data analyst jobs entail. We’re going to break them down based on the technical skills, analytical skills, and communication skills that are expected of employees in each role. 

Data Analyst Intern

What you’ll do.

Not every data analyst enters the industry via the intern route, but becoming an intern is perhaps the best way to cut your teeth as an analyst without having to shoulder the expectations of a full-time role. Data analyst interns are given basic tasks to gauge their analytical skills . This will usually be a small piece of a larger project. For example, you may be required to scrape a website for a particular kind of data or produce actionable insights from a small raw dataset. 

Related Read: How to Land a Data Analyst Internship

Average Salary

first job data analyst

The average salary for data analyst interns in the United States is $56,633 .

first job data analyst

Basic Requirements for This Role

The requirements for the data analyst intern role are quite minimal. You’ll need to have a basic understanding of data analysis methods and techniques and know how to work with business data. 

Junior Data Analyst

As a junior data analyst, you’ll get the opportunity to learn on the job and slowly begin contributing to bigger projects. This is one of the entry-level data analytics jobs where you can be given a whole host of different responsibilities based on the requirements of the company. 

Junior data analysts usually work under more senior analysts who break down projects into manageable chunks. Junior analysts are then assigned small parts of the project based on their technical skills and analytical abilities. 

The average salary for junior data analysts in the US is $61,398 .

entry level data analyst jobs- Junior data analyst

You need to have strong problem-solving skills to work as a junior data analyst. This is because you will be called on to solve some problems that you run into on your own. It also helps to have some presentation skills because analysts often make presentations to defend some of their decisions and findings. 

Entry-Level Operations Analyst

As the name suggests, entry-level operations analysts work on the operations aspect of businesses. It will be your job to quantify the efficiency of different operational procedures and come up with propositions on how they can be enhanced. 

The specific job description for this role will depend on the nature of the business that you’re working in. If you’re hired by an e-commerce company, then you might be tasked with studying warehouse and delivery operations. An entry-level operations analyst in a bank, on the other hand, will focus on aspects like customer acquisition or underwriting processes. 

Entry-level operations analysts make $54,506 on average in the USA.

entry level data analyst jobs- entry level operation analyst

Operations analysts need to have a strong ability to study business processes. They must also be able to work with complex data sets, as their ultimate goal is to help make strategic business decisions using data-driven insights and critical thinking. 

Get To Know Other Data Analytics Students

Rahil Jetly

Rahil Jetly

Sales Operations Manager at Springboard

Jo Liu

App Quality Analyst at Snap Inc.

Yogita Nesargi

Yogita Nesargi

Data Engineer at Deloitte

Junior Quantitative Analyst

Quantitative analysts are data analysts who have especially strong math and statistics skills. It will be your job as a junior quantitative analyst to crunch the numbers and come up with insights that can drive up business profits. 

Quantitative analysts are usually hired in big numbers in the finance industry. Their main job there is to architect algorithms that can help venture capital firms, hedge funds, and investment banks, assess investment opportunities, quantify risk, and detect fraud. 

entry level data analyst jobs- Junior quantitative analyst

The average salary for junior quantitative analysts in the US is $96,875 .

You’ve probably figured out by now that you need to be good with math and statistics to work as a quantitative analyst. It helps if you know how to use a tool like SAS or R , which analysts use for quant-heavy work. 

Entry-Level Healthcare Data Analyst

Entry-level healthcare data analysts work in a few different capacities in the healthcare industry. You might find yourself in a core healthcare data analyst role where you assist with healthcare research. These kinds of roles are usually reserved for data analysts who come with some kind of healthcare background. Another way in which data analysts can work in healthcare is by using data to analyze the operations of hospitals and other institutions that provide healthcare services. Or you might also work with public health bodies to build data regulatory frameworks for these institutions and perform data analysis for these institutions. 

The average salary of healthcare data analysts in the US is $70,345 . Salaries for the role typically range from $59,000 to $80,000, so entry-level hires can expect to make closer to the lower end of that spectrum.

entry level data analyst jobs- Healthcare salary

Healthcare data analysts usually work with very large volumes of data, so you should be comfortable working with voluminous datasets. When it comes to research, the data often tends to be visual in nature. It helps if you are a data analyst who knows image processing and image classification techniques .

Junior Financial Analyst

As a junior financial analyst, you will most likely find yourself working in a bank, hedge fund, or insurance company. With the rise of startups, venture capital firms have also started hiring financial analysts in large numbers. 

Your job as a junior financial analyst will involve a lot of number crunching. You will need to have strong math skills so that you can work with quantitative data. You will also need to quickly bring yourself up to speed with foundational concepts in the financial sector for your job as a junior analyst. 

Junior financial analysts make $68,109 per year on average in the US.

entry level data analyst jobs- Junior financial analyst

The main requirement for junior financial analysts is strong quantitative skills. Python is growing in popularity as a tool used to crunch financial data, and you may be required to pick up the language for your work. 

Junior Business Intelligence Analyst

As a junior business intelligence analyst, you’ll contribute to projects analyzing market trends, products, and competitors. As a data professional, you will need to be good at mining business data from disparate sources and collating it for analysis. There will also be an emphasis on understanding your own company and the industry at large quickly. 

entry level data analyst jobs- Junior Business intelligence analyst

The average salary for junior business intelligence analysts is $46,460 .

Basic Requirements For This Role

Junior business intelligence analyst roles usually require candidates to have basic skills in data analysis. You will need to know how to source data, build storage structures like data warehouses, and do basic analysis. Some companies might require you to learn a specific business intelligence tool like Domo or Rapid Insight. 

Entry-Level Manufacturing Analyst

Entry-level manufacturing analysts gather data from manufacturing operations and analyze them to generate insights to guide business decisions. Manufacturing analysis is a unique role because it primarily involves tools that are more mechanical than digital. However, there are now sensors and other devices used to generate data insights on the performance of manufacturing tools. Your work will often involve studying this data as well as identifying other key data sources on the factory floor. 

Manufacturing analyst salaries range from $67,500 to $120,000 in the USA. Entry-level hires can expect to make around the $70,000 mark.

Entry-Level Manufacturing Analyst

Manufacturing analysts should be willing to spend time learning about how factories function. You may be required to spend some time on the factory floor so that you gain some close-in experience. From there on in, your job will involve using basic data analytics tools and programming languages like Python and R to analyze the data available to you. 

Essential Technical and soft skills

To land a job as an entry-level data analyst, you’ll need to possess the following combination of hard and soft skills:

Hard Skills

Soft Skills 

Here are a few ways that you can go about looking for entry-level data analyst job openings: 

Job Boards 

Find a more exhaustive list of data analytics job boards here . 


As a young data analyst, you should slowly build up a network that consists of other data analysts , recruiters in the industry, and tech industry veterans. LinkedIn is a great place to get started. All you need to do is look up data analysts, and you’re just a connection request away from getting to know someone better. Data science and analytics communities and events are also a great way to network with folks in the industry. 

Related Read: Data Science Communities You Should Join

Marketplaces (Freelance or Project-Based Work)

Not every data analyst needs to work in a full-time office environment. You can choose to take the more flexible option and work as a freelancer. If you do decide to go down that route, there are plenty of marketplaces that hire talented data analysts. 

Related Read: How To Become a Freelance Data Analyst  

Here are five things you can do to help land your first data analyst role:

Pursue a Bachelor’s Degree (in a Related Field) or a Data Analytics Bootcamp

Having the appropriate educational background is a surefire way to get a recruiter’s attention when you’re applying for a data analytics job . If you want to go down the more conventional route, then a bachelor’s degree in computer science, software engineering, or math can help you land a data analyst job. 

But that’s not the only option available to you. Data analytics bootcamps are more affordable and usually have students job-ready in a matter of months. Many bootcamps also offer a job guarantee or have career services offerings that can help you land a job quickly. 

Build Relevant Experience by Pursuing Volunteer Work or an Internship

Having an existing body of work is perhaps the best way to stand out from other candidates applying to entry-level data analyst roles. There are a few ways you can go about building one. 

Volunteering your time can be the easiest way to get your foot in the door at an organization. You can check in with non-profits and other institutions in your area to see if they require your skills as a data analyst and are willing to accommodate you in their team, even if it’s on a part-time basis. 

Data analyst internships are also a great way to ease yourself into a career in data analytics. 

Polish Your Resume

Your data analyst resume is how you create a first impression on recruiters. If your resume doesn’t make an impact, then you probably won’t have a chance to take the conversation further with companies that you’re interested in. 

Build Your Network

Who you know matters just as much as what you know when it comes to landing a job. So remember to interact with as many data analysts, recruiters, and tech industry managers as you can both online and offline. 

Find a Mentor

Not everyone has somebody more experienced looking out for them. If you manage to find a mentor, then you can find opportunities more easily and grow faster than you would have otherwise. 

We’ve got the answers to your most frequently asked questions:

Can I Get a Data Analyst Job Without a Degree?

Yes, you definitely can ! Companies nowadays hire candidates who’ve graduated from data analytics courses and bootcamps. You can also apply to entry-level data analyst roles using a portfolio of projects that you’ve worked on. 

What Are the Qualifications To Become a Data Analyst?

You don’t need a qualification, like a specific degree or certification, to become a data analyst . Rather, you should focus on picking up the skills required for the job that you are applying for.

In general, data analysts should know how to process and analyze data; they should have a basic understanding of math and statistics. It also helps to know how to program in a language like Python or R. 

Is Data Analytics a Stressful Job?

Data analytics is not a stressful job on its own. In fact, it can be quite a rewarding career for those who enjoy the job. But if you do feel like your workload is getting a bit much to handle, you can always have that conversation with your manager. 

Does a Data Analyst Need To Code?

Not all data analysts code. Some work on mathematical analysis and others might use Microsoft Excel as their primary analytical tool. That said, it does help to know how to program because it is a skill that you might be called upon to use in your job as a data analyst.

Python, R, and Java are the programming languages you can choose from if you’re just starting out as a coder.

Since you’re here… Considering a career in data analytics? We can get you there. Don’t take our word for it – check out our student reviews . After just 6 months of study in our fully flexible Data Analytics Bootcamp , we’ll boost you into a job in the field, guaranteed. Get started now with our free data analytics curriculum . 

Download our 2022 data analytics salary guide

Take a closer look at the factors that influence compensation in data analytics. Stay ahead of the competition with job interview tips and tricks, plus advice on how to land the perfect role.

Related Articles

Data analyst vs. data scientist: salary, skills, & background.

difference between data analyst and data scientist

12 Best Data Analytics Courses for Your Career in 2023

data analytics course

How To Become a Fraud Analyst

How To Become a Fraud Analyst

first job data analyst

Your browser is not supported. Please download another browser to be able to use all of the Maven features.

first job data analyst

How To: Getting Your First Data Analyst Job

first job data analyst

Recently, we have seen a surge in students asking questions about how they can break into a career in Analytics and Business Intelligence.

If you’re someone I have had this discussion with, then you’ve likely already heard me say something like this:

Your first Data Analyst job is the hardest one to get. With some experience, your second job will be a lot easier to land. By the time you are looking for the third job, it will feel like a relative cake walk, and you’ll be able to choose from only the most interesting opportunities.

A career in Analytics and Business Intelligence can be a wonderful path. It can be rewarding in many ways, and once you have some experience, you’ll be on the right side of supply and demand.

All of this is true. However, it does not help answer the question on many students’ minds at the moment…

How can I get a Data Analyst job if I don’t have any experience?

That is what we are going to talk about today. The advice in this article is written specifically for the aspiring Data Analyst, trying to land their first role in the field. That said, if your goal is to get your first “Data Science” job, or to land your first role in any field, a lot of this information should still be useful.

First we should break the broader problem down into some distinct areas we can discuss...

Which skills can you acquire that will make you more valuable and attractive?

How can you make sure you look great when you are seen, how can you make sure more employers are seeing you.

In this article, we will focus on 1, 2, and 3 - the things that you should be doing to increase your likelihood of being invited for a job interview .

If you are interested in job interview advice, check out our Analyst Interview Prep Guide .

Let’s dive in.

Here, we will focus specifically on which technical skills, or “tools”, are the most attractive to employers. Keep in mind though, that technical skills are only part of the bigger picture. Below is a list of some of the areas employers will be looking to assess in any Data Analyst candidate. We discuss these in more detail in the Analyst Interview Prep Guide , so in this article, we will really hone in on technical skills:

Let’s talk about tools. If you want to be an Analyst, where should you start learning?

There isn’t necessarily a wrong answer, but my personal suggestion is that you should master Microsoft Excel before you spend time anywhere else .

Why Excel? There are so many reasons…

Excel is everywhere. I would challenge you to find a competent organization that isn’t doing at least something in Excel. With Excel, you can guarantee your skills will work at nearly any company.

In my opinion, Excel is the perfect entry point to a data career. For someone who doesn’t have coding skills or database experience, learning Excel is still relatively easy. It also gives you so much runway to add value and to pivot into other areas down the road...

Excel works great as a standalone, and pairs extremely well with a number of other tools. With all of the features listed above, there are Analysts who get so good at Excel they can build their entire career around it. Others will pair Excel skills with other tools, but I guarantee they will always find use for Excel even if they move on to become a “SQL guy” or a “Python guru”.

If you think Excel is a good fit, check out Maven’s Excel Specialist Path for a good framework of the various Excel skills you should aim to learn. Even if you aren’t interested in Maven’s courses, it can still serve as a great outline to guide your study.

Where should you focus after Excel?

If you feel like you really have mastery of Excel, and you are looking for the next area to expand into, any of these will be good choices…

Personally, I think these are listed in the correct order, but there is also a very valid argument to be made that it depends on exactly what you are excited about. If you have a strong passion for one of these buckets, bump it higher on the list. There is no better predictor of success in any of these than genuine enthusiasm, which will keep you going when things get challenging.

One word of caution... if you are really excited about modeling and data science tools like R and Python, I strongly encourage you to pick up SQL first. Trust me, I get it. Modeling and data science work is a lot of fun with the prepared datasets you will use in a data science course. However, in the real world, on the job, no one hands you that dataset. You are going to need to learn how to dig for yourself and create the dataset for your modeling work if you want to add value. Being good at R or Python alone, without knowing how to access data using SQL makes you somewhat impractical, and not very likely to create value on your own. There may be some exceptions where a person like this could be valuable if paired with someone else who can aggregate data sets for them, but consider this the exception and not what you should be aiming for. Aim to be “full-stack”. Be able to aggregate the data, package it in the right form, do the modeling work, and then translate your findings to actionable business recommendations. That’s someone I would hire. That’s SQL + R or SQL + Python.

If the above doesn’t sell you, here is a little more on why SQL is so valuable to learn…

SQL is used by almost every company that works with large and complex data sets. Data is typically stored in a relational database, and accessed using SQL. Like Excel, learning SQL gives you a skill that is extremely portable between companies .

SQL is relatively easy to learn. As far as coding languages go, SQL is fairly simple… you want to select the data in the address column from the customers table? Type SELECT address FROM customers. Done. The syntax is pretty simple to pick up. I’m not a “code guy” myself. I didn’t study computer science. I picked up a SQL book and learned. You can learn too. It’s not too bad at all.

Supply and demand for SQL talent is extremely attractive. Like Excel, almost all companies need to use SQL. So you would think the number of people who have mastered SQL would be up there with the number of Excel jockeys, right? No way! Far fewer people are truly great at SQL coding and analysis. When you know SQL, you get bucketed into “technical talent”. Demand is high, and supply for talent is hard to find. Salaries go up. This is where you want to be!

If you are looking to beef up your SQL skills, check out our MySQL Specialist Path . Again, even if you are not interested in Maven’s courses, seeing the curriculum can serve as a guide for what you should aim to learn in your studies.

After SQL, I recommend you spend some time with data visualization software like Power BI and Tableau . There are other platforms out there as well, but these two are the most widely used, so I really wouldn’t advise spending time learning another platform, unless your current employer uses something else and you can learn on the job.

Students will often point out that Tableau and Power BI serve the same function, and ask whether it makes sense to learn both. It is a great question. Where Excel and SQL are used by almost every employer in some capacity, the dashboarding tools market is more split. Together, Power BI and Tableau dominate the market, but it is a bit more rare to see one company adopt both platforms. They typically choose one or the other. I do not see either of them going away anytime soon, so learning the basics of both is probably the best move to maximize your attractiveness to the largest employer base. Plus, learning Power BI will likely make you a better Tableau user, and vice versa, as you’ll see how things are done in different platforms and learn the nuances more deeply.

Check out our Tableau Desktop course and our Power BI Specialist Path if you are interested in picking up these dashboarding skills. We also have an Advanced Tableau course in the works, which we hope to launch by January.

The next category of tools I mentioned learning is web analytics platforms. There are lots of them out there… Mixpanel, KISSmetrics, Clicky, the list goes on. These platforms generally all serve to help businesses understand what is happening on their websites and where their traffic is coming from.

With all of the web analytics platforms out there, the market is much more fragmented. So for the most part, learning one of these platforms does not give you much appeal to employers, because it is unlikely that they use the same platform. There is one exception to this… Google Analytics. “GA”, as it is often referred to, is the most widely used. The free version of GA is a great platform, and it is extremely easy to implement. For these reasons, many employers have GA running in some capacity. If you learn Google Analytics, the concepts you learn will also apply to other web analytics measurement platforms.

My advice on web analytics tools? Spend the time to learn Google Analytics, and ignore the other platforms until you work for an employer who uses one of them, then pick that one up.

At the time of writing, Maven does not currently offer a Google Analytics training course. We are actively looking for a great instructor. If you know someone who loves GA and would get excited about becoming an instructor, we would love to hear from you. Feel free to message John on LinkedIn, or share your recommendation in the chat on the Maven website.

The last skillset group on our list is data science and modeling tools like R and Python . If you are interested in this, I strongly recommend that you pick one of these and that you pair it with SQL so you can retrieve and manipulate your own datasets. They are each great, and while there is some advantage to learning a second one, there are diminishing returns. I also recommend avoiding SAS, SPSS, and Stata. These are great tools too, but they do mainly the same things, and not all employers have licenses for these programs, so your skills with these are not quite as portable from job to job. Stick with R and Python if you want to be a Data Scientist.

Like SQL, the supply and demand dynamic for people with these skills is fantastic. Lots of employers are looking for skills here, and there aren’t enough people who are true masters. One thing to pay attention to - if you see a company looking for “Python” skills, you should try to clarify whether they want you to be a Python Programmer, or a Python Data Scientist. Python is an interesting language, in that it can be used for both programming and data science. However, the skills are quite different. Just make sure you are thinking about this if you are looking at job descriptions.

At the time of writing, Maven does not currently offer any Python or R courses. They are quite valuable tools, but for the time being we have chosen to prioritize going deep on the core Business Intelligence tools and wait on adding any Data Science courses to the library. This could change in the future.

If you’ve stuck through this long-winded discussion on technical skills, good for you! We’re ready to move on to our next section…

Alright, so now you know the skills you need to master to look attractive. Next, you need to start thinking about how employers will start to see you.

In general, you’ll want to be thinking about two things - your online presence, and your resume. I would recommend you focus on them in that order.

Before you read any further, go to and type in your name. What comes up?

Try to put yourself in an employer’s shoes when looking at the search results. Do you look like a bright young professional who is all about Data and Analytics? Or is your Facebook account public with plenty of inappropriate pictures that make you look like a party animal?

If anything seems questionable, see if you can remove it. Make the Facebook and Instagram pictures private. Remove anything you’ve posted somewhere that you wouldn’t want your employer to see.

If there just isn’t much information on you, that is actually okay. It’s better to have nothing show up than to have results that are “strikes against you”. This isn’t the time to try and beef up your results. We are just talking about damage control right now. Remove the bad. Don’t worry about adding right now.

Next, let’s go to LinkedIn and do the same exercise. LinkedIn is where every recruiter or hiring manager will look to check you out , in addition to looking at your resume. Search for your name. You should be able to find yourself. If you find yourself, skip the next paragraph. If you don’t, read it!

Don’t have a LinkedIn profile? Make one. LinkedIn is the new resume and business card. If you want to be in business, you have to have it. If you’re a brilliant Software Engineer who loves being described as “quirky”, then you may be an exception and can ignore this advice. But if you want to be a Data Analyst or anything close, you are firmly in the realm of “business person” and you have to have a LinkedIn profile.

Next, check out your LinkedIn profile page. Make sure to look at the view that other people will see, vs your signed-in account view. If you don’t know how to do that, Google it.

How does your profile look? Again, try to put yourself in the shoes of an employer looking to hire a Data Analyst…

Some things you should do to improve your LinkedIn image…

If you do all of the things we discussed above, then when a hiring manager looks you up online, you’ll look like a great candidate.

The next place we want you to look great is your resume. A lot of the same concepts we talked about for LinkedIn apply for your resume too. In general, the goal is to make your resume scream “Data Analyst”. For a detailed walkthrough of how to improve your resume, check out our Data Analyst Resume Tips article.

After you take care of your LinkedIn profile and your resume, you’ll be looking good when someone finds you. Next, you’ll want to focus on being found more often.

At this point I will make the assumption that you’ve built some valuable skills, you are putting them on display in the right ways, and you’re ready to focus into getting more attention.

A fairly common thing we see with young professionals is doing the first two steps decently well, and then completely undervaluing this last part. Don’t let that be you.

What good is a top notch resume and LinkedIn profile if no one ever sees it?

Here are the steps you should take (we’ll talk through each one)...

First, make a list of all of your allies. Basically, just jot down every adult you know who likes you. Family members, friends, professors, former employers, coworkers, etc. Spreadsheets are great for this. I recommend Excel or Google Sheets.

Next, we will refine this list a bit to cut it down to your “high value connectors” . Add 2 column headers to the right of your list of names - “Willingness”,”Ability”.

Now you are ready to make an initial list of target employers. For this, don’t worry specifically about job openings. Just think about companies that you think would be a good fit. Pick companies in areas you want to work(could be close to home or somewhere else), which seem like they have a lot of data and need Analysts. List the companies, and then rank them on how excited you would be to work there. Later we’ll use this ranking. Again, Excel or Google Sheets is a great place to make this list.

Make a list of data influencers and major hashtags you should follow on LinkedIn. Who is talking about data often? Make your list, and follow them. You can learn a lot from following these people, and you can gain exposure by adding value to their conversations.

Don’t be restricted to the people and hashtags above. As you continue to immerse yourself in the data world, you’ll see people sharing content. When you see people genuinely adding value and sharing content that you are learning from, follow them to keep learning, and interact when you get a chance.

Next up, it’s time to increase your LinkedIn connections. This will be valuable to you later, as you will be easily able to figure out which of your allies knows someone at the companies you want to work for.

Time to start following the companies on your initial employer list. Most of them will be active on LinkedIn. For now, just be passive. Start following them, and learn the types of things they talk about. You might even get lucky and be one of the first to hear about an open position at some point. Later on, we’ll talk about how you can add value to the conversation to get on their radar.

Next, you want to start getting active on LinkedIn. You’ve started following companies you are interested in, data influencers, relevant hashtags. Now your LinkedIn feed will skew heavily toward the data world. Use your feed to learn what the conversation tends to be around. You’ll get smarter just by reading. Do this every day. You’ll also want to start engaging actively. Here’s your LinkedIn playbook at this stage…

At this point you have expanded your network, are learning, and are looking like a Data Analyst to anyone who finds you online. Time to start getting great at finding job opportunities. Here are some ideas to get you started…

Now you know how to find job opportunities. Let’s talk about how to make the most of them by applying the right way. This is where your beefed up LinkedIn network is going to come in very handy. You see, the unfortunate thing is that a lot of resumes submitted online don’t get much attention, or never even get read at all. It stinks, for sure. You could cry about it, or you could strategize as to how you can submit your resume and application more effectively.

Worst: online application submission (likely never read or quickly discarded if read) Better: referral from someone you know to someone working at the company Best: direct referral from someone you know to the hiring manager/ other department leader

You want to be gunning for referrals. Here’s how you should play this…

If you’ve read this far (good for you!) you’re now armed with an understanding of the skills that will make you valuable as a Data Analyst, how to talk about those skills once you have them, and how to make sure you get seen by the right people.

I know this sounds like a lot, but you get out what you put in. Those of you with the most ambition, I suspect you’ll follow all of this advice. For the rest of you, I hope you will at least do something.

You know what you need to do. Now get after it!

Ready to build practical, job-ready data skills of your own?

Create your custom learning plan today, and save up to 50% on all-access memberships when you upgrade to a paid account.

This is a limited time deal, make sure you take advantage of the savings!

50% OFF EARLY BLACK FRIDAY SALE HERE - learn more here.

Maven Paths

To get analytics tips & tricks delivered directly to your inbox.

John Pauler

John brings over a decade of business intelligence experience to the Maven team, having worked with companies ranging from Fortune 500 to early stage startups. As a MySQL expert, he has played leadership roles across analytics, marketing, SaaS and product teams.

You May Also Like

first job data analyst

Ready to become a

Data rockstar.

Start learning for free, no credit card required!

© Maven Analytics, LLC | All Rights Reserved

How to Become a Data Analyst (with or Without a Degree)

If you enjoy working with numbers and solving puzzles, a career as a data analyst could be a good fit.

A female data analyst sits at her desk in front of a laptop computer, looking at the camera

Data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started:

Get a foundational education.

Build your technical skills.

Work on projects with real data.

Develop a portfolio of your work.

Practice presenting your findings.

Get an entry-level data analyst job.

Consider certification or an advanced degree.

Let's take a closer look at each of those seven steps.

How do I become a data analyst? A step-by-step guide

You can find data analytics jobs in all sorts of industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just getting started in the professional world or pivoting to a new career, here are some steps toward becoming a data analyst.

Learn more: What Does a Data Analyst Do? A Career Guide

1. Get a foundational education.

If you’re new to the world of data analysis , you’ll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.

It used to be that most entry-level data analyst positions required a bachelor’s degree. While many positions still do require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs, bootcamps, or self-study courses.

2. Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

R or Python programming

SQL (Structured Query Language)

Data visualization

Data cleaning and preparation

Take a look at some job listings for roles you’d like to apply for, and focus your learning on the specific programming languages or visualization tools listed as requirements.

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.

3. Work on projects with real data.

The best way to learn how to find value in data is to work with it in real world settings. Look for degree programs or courses that include hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your own projects. 

Dig into climate data from the National Centers for Environmental Information , delve deeper into the news with data from BuzzFeed , or come up with solutions to looming challenges on Earth and beyond with NASA open data. These are just a few examples of the data out there. Pick a topic you’re interested in and find some data to practice on.

Tip: For more inspiration, check out Coursera’s library of data analysis Guided Projects —a series of guided, hands-on experiences you can complete in under two hours.

4. Develop a portfolio of your work.

As you play around with data sets on the internet or complete hands-on assignments in your classes, be sure to save your best work for your portfolio. A portfolio demonstrates your skills to hiring managers. A strong portfolio can go a long way toward getting the job.  

As you start to curate work for your portfolio, choose projects that demonstrate your ability to:

Scrape data from different sources

Clean and normalize raw data

Visualize your findings through graphs, charts, maps, and other visualizations

Draw actionable insights from data

If you’ve worked on any group projects through the course of your learning, consider including one of those as well. This shows that you’re able to work as part of a team.

If you’re not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people’s portfolios to see what they’ve chosen to include.

Tip: Sign up for a GitHub account and start posting your projects and code to the site. It’s an excellent spot to network with a community of data analysts, show off your work, and possibly catch the eye of recruiters.

5. Practice presenting your findings.

It can be easy to focus only on the technical aspects of data analysis, but don’t neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision makers and other stakeholders in the company. When you’re able to tell a story with the data, you can help your organization make data-driven decisions. 

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 1 ].

As you complete projects for your portfolio, practice presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practice speaking slowly and making eye contact. Practice in front of the mirror or your classmates. Try recording yourself as you present so you can watch it back and look for areas to improve.

6. Get an entry-level data analyst job.

After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analysts jobs. Don’t be afraid to apply for positions you don’t feel 100-percent qualified for. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.

If you’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real world experience for your resume and apply what you’re learning on the job.

7. Consider certification or an advanced degree.

As you move through your career as a data analyst, consider how you’d like to advance and what other qualifications can help you get there. Certifications, like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst, might help qualify you for more advanced positions at higher pay grades.  

Tip: Consider pursuing your data science degree online from an accredited university so you can continue working (and earning a paycheck) as you learn.

The University of Michigan School of Information’s online Master of Applied Data Science (MADS) degree is designed for aspiring data scientists to learn and apply skills through hands-on projects. You’ll learn how to use data to improve outcomes and achieve ambitious goals.

If you’re considering advancing into a role as a data scientist, you may need to earn a master’s degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference?

In this video, practicing data professionals offer their best advice for aspiring data analysts.


4.8 (10,467 ratings)

280K Students Enrolled

Course 1 of 9 in the IBM Data Analytics with Excel and R Professional Certificate

How to become a data analyst without a degree

A degree isn’t always necessary to get hired as a data analyst. Data analysts are in demand, and employers want to know that you have the skills to do the job. If you don’t have a degree, focus on making your portfolio shine with your best work. 

Get started with Coursera

If you’re looking to build job-ready data analyst skills without spending the time or money on a degree, consider the Google Data Analytics Professional Certificate through Coursera.

Learn how to clean and organize data with SQL and R, visualize with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs directly with Google and more than 130 other US employers.

How to become a data analyst without experience

Often employers will want you to have experience working with data before taking a role as a data analyst. Luckily, you don’t have to wait to get hired to start gaining experience. Data is all around us. 

If you’re switching to data analysis from another field, start to develop your experience by working with data. Many degree programs, certificate courses, and online classes include hands-on projects with real data sets. You can also find free data sets on the internet (or scrape your own) to gain experience collecting, cleaning, analyzing, and visualizing real data.


professional certificate

Google Data Analytics

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.

(102,930 ratings)

1,447,513 already enrolled


Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

Frequently asked questions (FAQ)

How many years does it take to become a data analyst ‎.

I can take anywhere from several months to several years to become a data analyst. The amount of time it takes you will depend on your current skill set, what type of educational path you choose, and how much time you spend each week developing your data analytics skills.

Learn more: Is Data Analytics Hard? Tips for Rising to the Challenge ‎

Can I be a data analyst without a degree? ‎

Yes, though a degree in a relevant field will likely improve your chances. While many positions will list a bachelor’s degree as a job requirement, it is possible to get hired with the right set of skills and experience. If you don’t have a degree (or a degree in a related field), be sure to spend extra time developing your portfolio to validate your abilities. ‎

Are data analysts in demand? ‎

Demand for skilled data analysts is growing — the World Economic Forum Future of Jobs 2020 report listed this career as number one in terms of increasing demand [ 2 ]. And hiring data analysts is a top priority across a range of industries, including technology, financial services, healthcare, information technology, and energy. ‎

What qualifications do you need to be a data analyst? ‎

Data analytics is a skill-based profession. Many positions will look for candidates with proficiency in SQL, Microsoft Excel, R or Python programming, data visualization, and presentation skills. Check some job listings in the industry you’re planning to apply to for more specific qualifications. ‎

Article sources

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance ," Access March 15, 2022.

World Economic Forum. " Data Science in the New Economy ," Accessed March 15, 2022.

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Develop career skills and credentials to stand out

Coursera Footer

Start or advance your career.

Popular Courses and Certifications

Popular collections and articles

Earn a degree or certificate online


Vicky Yu

Oct 31, 2020


How to Get Your First Data Analyst Job Without Experience

Answer — it’s not just about having the technical skills.

T he most common advice I’ve read to get a data analyst job without experience revolves around getting an analytics degree, networking, and building a data analytics portfolio. What if you’ve already followed this advice and you’re still unemployed?

More from Vicky Yu

Musings of a data scientist turned data analyst. Sharing my data experiences one story at a time.

About Help Terms Privacy

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store

Text to speech


  1. How to Get a Job as a Data Analyst

    first job data analyst

  2. Data Analyst Job

    first job data analyst

  3. Excel in Your Data Career: 9 Tips To Ace Your First Job

    first job data analyst

  4. Data Analyst Career Path

    first job data analyst

  5. Introducing Women4IT Digital Job profiles: Data Analyst

    first job data analyst

  6. Data Analyst CV: Example and How to Write [Expert Tips]

    first job data analyst


  1. Hiring Data Analyst skilled in data analysis, data analytics, SQL

  2. Data Analyst Salary in 2023

  3. How I became a Data Analyst (without Experience or Degree) Data Analyst Roadmap

  4. Data Analyst Job Research

  5. Want to pursue your career as data analyst ?

  6. Data Anylist Road map 2023


  1. Why Are Jobs Important?

    Jobs are important for several reasons: they provide workers with personal feelings of self-worth and satisfaction and produce revenue, which in turn encourages spending and stimulates the larger economy. Jobs provide personal and economic ...

  2. What Is Presenting Data?

    In the field of math, data presentation is the method by which people summarize, organize and communicate information using a variety of tools, such as diagrams, distribution charts, histograms and graphs. The methods used to present mathem...

  3. What Is the Definition of “presentation of Data”?

    The presentation of data refers to how mathematicians and scientists summarize and present data related to scientific studies and research. In order to present their points, they use various techniques and tools to condense and summarize th...

  4. How to get an entry-level data analyst job with no experience or a

    8 Steps to becoming a Data Analyst without a college degree or experience · Step 1: Identify your transferable skills. · Step 2: Learn the

  5. How to Become a Data Analyst with No Experience

    A hands-on curriculum that contributes to your portfolio · Some form of mentorship · A certificate of completion · A focus on job preparation and

  6. How I got my first job in Data Analytics with no prior full-time

    2. Internship experiences. Internships are hands-down the best gateway to gain real-world data analytics experience. · 3. Networking and having a

  7. How to Become a Data Analyst in 2023: 5 Steps to Start Your Career

    When you first come to the job market as an entry-level data analyst, it's understandable if your initial portfolio of projects contains mostly guided

  8. How To Become a Data Analyst with No Experience: 2023 Guide

    Refine the Skills Needed for Your Target Job. Consider Related Jobs Where You Can Transfer into Data Analytics. Your first step is to

  9. 8 Entry-Level Data Analyst Jobs to Start Your Career

    Having the appropriate educational background is a surefire way to get a recruiter's attention when you're applying for a data analytics job. If

  10. How To Get Your First Data Analyst Job

    A practical "How to" guide for becoming a data analyst, getting your first job & breaking into the exciting field of analytics and business

  11. How To Get a Data Analyst Job (with No Experience)

    How To Get A Data Analyst Job With No Experience | In this video we talk about all of my tips and tricks to help you get a job as a Data

  12. How to Become a Data Analyst (with or Without a Degree)

    You can find data analytics jobs in all sorts of industries, and there's more than one path toward securing your first job in this

  13. How to Get Your First Data Analyst Job Without Experience

    Apply to consulting firms which are normally more willing to hire people with no experience. Working a couple of years in consulting will help

  14. What should I do to get my first data analyst job?

    In order to get started as a data analyst, its good if you master the required skills for it. This will make it easier for you to be recruited as a data analyst