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How to Become a Data Analyst and Land Your First Job

Discover what you'll need to launch a career as a data analyst, from developing your skills and portfolio to reaching out to potential employers.
May 2022  · 18 min read

In recent years, the data analyst role has become increasingly popular, and this comes as no surprise with the massive amount of data that is rapidly accumulating in the modern world. Companies in all sectors need specialists who can harness this data, analyze it, extract meaningful data-driven insights from it, and use those insights to help them to solve key business problems.  As a result, data analysts have become highly sought after and extremely well-paid. In fact, not only is the job market for data analysts booming, it’s not showing signs of slowing down anytime soon. 

In this article, we will discuss the essential ingredients to becoming a successful data analyst, including: 

  • A natural curiosity about the data 
  • Key programming languages and skills
  • A portfolio of relevant projects
  • A properly written resume 
  • Compelling profiles on LinkedIn and similar websites  
  • Networking with other data specialists

Step One: Learn Data Analysis

Getting Started

Naturally, learning how to analyze data is the very first step; however, as simple as it sounds, this can be the most challenging part, and many people get overwhelmed or dissuaded from continuing at this stage. Why is this? 

First, popular belief is that to start learning data analysis, one has to be good at mathematics, statistics, or programming. While it's true that a background in these fields provides a solid technical basis, it doesn't mean that a career in data analysis is unapproachable for people from other educational and professional backgrounds. There are plenty of success stories from career-changers who entered data analysis from totally unrelated spheres, made swift progress, and went on to launch successful careers in data science.

Secondly, learning to analyze data will require some intensive study, dedication, and a great deal of practice. You have to maintain a certain level of optimism even when you are stuck, exhausted, discouraged, or can't see any progress. Doubts, fears, excuses, lack of discipline, and ineffective time-management are potential obstacles you might face and need to push past in the beginning. 

Finally, you should keep in mind other qualities that are fundamental to being a successful data analyst: An aspiring data analyst should be creative and curious about data, have an exploratory mindset, be able to think analytically, be able to work both independently or in a team, and be willing to dedicate the necessary time and effort.  These are all qualities that will help ensure your success.

Selecting the Right Program to Learn Data Analysis

Now you’ll need to decide how and where you will learn. For example, should you opt for a university degree or search for a online course?

Several universities offer bachelor's and master's degrees in data analytics, but undertaking this path will require a substantial investment of both time and money: you’ll need to devote 2-4 years to full-time study, and it could cost anywhere from $30,000-$200,000. Additionally, if you enroll in a bachelor's degree program, you’ll be required to fulfill course requirements outside of data analytics.  In short, it’s not the most efficient way to learn - neither from an economic or a time perspective.  

Fortunately, university study is not your only option; you can successfully learn data analysis in an online boot camp. Learning online in this way will give you much more freedom to manage your time, allow you to dig deeper into the concepts that you find most interesting, skip topics where you have enough expertise, and organize your learning process more comfortably and efficiently.  You’ll also have the flexibility to learn from wherever you are in the world, provided you have a computer and internet access. What’s more, learning through an online program is dramatically less expensive than learning at a university:  for example, you can complete this online Data Analyst Career Track and Certification in less than a year, and it will cost you less than $500.  

An ideal online self-study program includes an exhaustive and well-balanced curriculum that covers the most important data analysis topics and techniques – along with plenty of opportunities to practice them. DataCamp has several programs that fit the bill. You can select from interactive, information-packed, and beginner-friendly career tracks that will give you all the necessary knowledge and skills to become a well-rounded data analyst:

Whichever path you choose, your learning path will look something like this:  

Step Two: Practice Your Skills and Create Your Portfolio of Data Analysis Projects

The Importance of Individual Projects

You will have abundant opportunities to put your new skills to work by doing various exercises and completing the data analysis projects suggested by your curriculum. Practicing your skills and solving mock or real-world problems will give you a solid basis for your future work experience. At this stage, having access to some clean datasets and preselected ideas to explore will help maintain your interest in learning and avoid the distractions of additional searching or brainstorming.

However, the time will come for you to prepare yourself for real-world work experience as a data analyst, and you’ll need to proceed with more advanced studies: In order to best “sell” yourself, you’ll want to showcase your ability to work and research independently to a potential employer. Hence, you will need to undertake individual projects where everything will be your responsibility: selecting the topic, fetching the necessary data, contemplating the direction of your research, designing the project structure, making and checking hypotheses, effectively communicating your findings, and laying out the way forward. As a result, individual projects usually take much more time than the guided ones, but they will help you to stand out from the crowd when applying for a job.

When working on individual projects, you may discover that you need to strengthen some particular skills or investigate narrow-focused topics. In this case, you might find DataCamp's vast catalog of courses useful.

Find Free Datasets for Your Individual Data Analysis Projects

As soon as you come up with a good topic to develop in your project, your next step is to find the relevant data to explore. For this purpose, there are numerous online repositories offering a variety of free datasets:

  • Kaggle – The most popular website that stores thousands of free datasets on various topics, both real-world and synthetic.
  • UCI Machine Learning Repository – Contains open-source datasets. Most of which are clean, well-structured, and well-documented.
  • FiveThirtyEight – Here you will find interactive data-driven articles on different mainstream topics, as well as the datasets used for these articles.
  • Google Dataset Search – A keyword-based search engine, just like normal Google search. It stores more than 25 million free public datasets.
  • DataCamp Workspace – An online integrated development environment (IDE) with available datasets for writing code, analyzing data, and practicing your skills.

Create Your First Portfolio of Data Analysis Projects

When you first come to the market as an entry-level data analyst, it's ok if your initial portfolio of projects contains mostly guided capstone projects from your online boot camp or data-related university work. At this stage, it is also perfectly fine and expected to have many disparate boot camp projects on different studied concepts, showing a variety of tools and techniques. Consider also including any freelancing or volunteer work, internship projects, or contributions to GitHub open source projects, if applicable. Later, when you feel that you are ready to explore a specific business area of your choice, you may start focusing on gaining domain knowledge and making individual projects related to that particular sphere.

Moving forward, your individual data analysis projects will start having more weight over the boot camp-guided ones. At this point, feel free to revise your portfolio: Remove your earlier, less relevant projects or those you don't feel particularly proud of. Recognizing and responding to the need to prioritize the projects in your portfolio is a good sign that you’re becoming a more mature data analyst.

You can keep your portfolio of data analysis projects on GitHub, DataCamp Workspace, or Kaggle free of charge. They are not the only free platforms for hosting such portfolios, but these two are widely popular and are the best choice for an entry-level data specialist as they ensure good visibility for your projects. You might also want to consider creating a personal website.

Be Sure You Have the Required Skills 

Before starting your job hunting process, you may want to make a quick revision of your data analyst skills and compare it with the requirements for this role in the modern market. A good place to start is to take a look at the descriptions of several job positions for a data analyst and write down the skills that are currently most in demand. To give you general guidelines, here are the most basic technical skills that companies usually expect to see in a data analyst:

  • Python or R (especially their specialized libraries for data analysis)
  • SQL
  • The command line
  • Statistics
  • Data cleaning and wrangling
  • Data analysis
  • Data visualization
  • Web scraping
  • Debugging
  • Storytelling
  • Unstructured data

If the list above looks overwhelming to you, don't feel discouraged; you won't likely need all these skills for every data analyst job. Usually, each company looks for a different set of skills in a suitable candidate. The best way to find out the specific requirements of a certain employer is to read the corresponding job description. If at this point you feel that you lack some crucial skills, consider upskilling.

Finally, don’t forget that the role of a data analyst implies also having some important soft skills:

  • Analytical thinking
  • Multitasking
  • Curiosity
  • Creativity
  • Communication skills
  • Flexibility
  • Ability to work both independently and in a team
  • Decision making
  • Business domain knowledge

Create a Professional Resume

Now it's time to write your data analyst resume. At a first glance, it may seem to be an easy task. However, in reality, it's worth dedicating some time and effort to building a compelling and professional-looking resume that can capture the attention of recruiters. You may find the following article helpful: Tips to build your data scientist resume. While this article talks mostly about creating a resume for a data scientist role, the majority of tips from here are applicable to any other data-related profession. Let's briefly outline the most important suggestions from this article:

  • Fit your resume on one page.
  • Select an appropriate resume template. You can create it from scratch or use an online resume builder with a variety of resume templates. You may wish to consider the following resources: Resume, Zety, Resume Builder, Canva, CakeResume, VisualCV, ResumeCoach, or use free resume templates from Google Docs or MS Word collections. Give preference to simple and clean templates and use a suitable consistent format, especially if you create your resume from scratch.
  • Create your master resume. This can be a long-form, very detailed version of your resume with many pages and a lot of bullet points. Here you can include all of your work experience (even previous unrelated work experience if you are a career-changer), studies, projects, technical and soft skills, etc. You can easily use this giant version of your resume as a basis for applications for any data analyst’s job position: just create a copy of it, remove redundant information, and adapt it for each role you apply for.
  • Customize your data analyst resume to each job description you apply for. Read the job description carefully, figure out the requirements that the company is looking for in a candidate, and incorporate/highlight the necessary skills and keywords in your resume. In addition, you can explore the company's website (its mission, values, products, etc.), and refer to it to make your resume reflect that you are the perfect fit for that company.
  • Be concise but informative.
  • Use plain but efficient language.
  • Check for errors and typos.
  • Consider including the following sections:
    • Contact Information
    • Objective
    • Work Experience
    • Projects
    • Skills
    • Education

The order of appearance of the last four sections depends on your real relevant experience and, hence, on what you want to showcase first.

  • Additional sections you may want to include:
    • Certifications
    • Publications
    • Conferences
    • Awards
    • Competitions
    • Volunteering
    • Languages
  • Double-check your contact information.
  • Only include relevant projects, if your portfolio of projects permits it. For each project, include the project title, the link to it in your portfolio, and a clearly and concisely stated project goal.  Also, include a brief description of data sources, techniques, programming languages, libraries, tools, and skills used (without overusing technical jargon), and complete your project listing with your individual contribution to the project (if it was a group project), and any quantitative results of your work.
  • Don't include your skill level. Such evaluation can be extremely subjective and influenced by the Dunning–Kruger effect: your "expert" could be "basic" by someone else’s standards and vice versa. Hence, avoid selling yourself short or overselling yourself.
  • Don't include your soft skills since they are also rather subjective. You will demonstrate your soft skills in action in the Projects or Work Experience sections, which should reflect how they worked in combination with your technical skills to obtain real data-driven results. Remember that the whole style of your resume can tell a lot about your communication and storytelling skills.

Maintain Your Online Presence and Visibility

Your LinkedIn/Kaggle/Medium/GitHub or any other relevant professional profile should be in line with your data analyst resume or even represent your resume in miniature. The main goal here is to let your readers know that you are a data analyst, even if you don't have real work experience yet in this sphere. In other words, you have to promote yourself and create a unique personal brand to enter the competitive labor market of data analysis.

Below are some useful tips:

  • Keep your professional profile and portfolio of projects updated.
  • In the headline, write Data Analyst instead of your current profession, if you are a career-changer. Avoid adding the word aspiring to your headline.
  • Include your photo and maybe a data-related cover picture from or similar.
  • Provide the best way to contact you. Use a professional-looking email address such as [email protected], instead of a frivolous email like [email protected]
  • Include any relevant licenses, certifications, skills, accomplishments, recommendations, and cross-links to your other professional profiles.

Some of the suggestions for writing an efficient resume are also applicable here. Be concise but informative, use plain but efficient language, check for errors and typos, double-check your contact details, and avoid including your skill level.

Network with Other Data Professionals

Since you are trying to enter a completely new sphere, you need to start growing your professional network in the data world. Creating a compelling LinkedIn, GitHub, or similar profile is a great first step. However, you can be even more proactive by joining various data communities or Facebook groups (especially volunteering in them and answering other people's technical questions), participating in online and live meetup events and conferences, following and connecting with the right people on social networks, commenting on social media data-related content, publishing articles on data analysis topics, etc. All in all, it's vitally important to surround yourself with data professionals from the very beginning. At DataCamp, you will find a friendly and helpful community of data enthusiasts where you can get and give help and support, and broaden your contacts in the data world.

Step Four: Start Applying for Data Analyst Job Openings

Finally, after completing all the steps we’ve outlined, you are perfectly ready to start looking for a job as a data analyst. But where should you start your job hunting process?

The first and easiest way is to browse free job listing websites. They can be general job portals like LinkedIn, Indeed, Google for Jobs, SimplyHired, AngelList, Hired, etc., or more data-oriented niche job boards like DataCamp Jobs, KDnuggets, DataJobs, AmazonJobs, StatsJobs, etc. In addition, consider websites advertising remote jobs only: Upwork, Remote, JustRemote, We Work Remotely, or even specialized job boards exclusively dedicated to data-related remote jobs, such as Outer Join.  Also keep in mind that some online data science certification platforms also offer career support: if you obtain your Data Analyst Certification through DataCamp, you’ll receive job-search support that’s tailored to your individual needs from their career services team.  

Reach out to potential employers directly 

In parallel with this job searching approach, you can try a less conventional, more time-consuming, but also more efficient method: reaching out to companies of interest directly. To go this route, first find their official website, explore their home and career pages, and find their contact details. Read about their mission and values, their services, products, etc. Try to figure out how you could be an ideal candidate for this employer. Now that you are more informed about what their business looks like, you can send them an email with your data analysis resume customized exactly for that company and demonstrating that you are a perfect fit for them. This is indeed a safe way to stand out from the crowd in the eyes of that particular employer.

Keep good records and adjust your strategy along the way

When sending your resume, whether to various job portals or directly to an organization, keep a record of the resume versions you sent and the corresponding company names and job descriptions. Don't be discouraged if you don't manage to find a data analyst job immediately. It is absolutely normal if your job search process takes some time, and remember that rejections are an inevitable hurdle for most. Your failures shouldn't frustrate you or make you lose hope. Keep applying for new job positions and continue sharpening your technical skills. Try to analyze what could be improved in your resume, your portfolio, or your job hunting process and make alterations accordingly. In the case of rejections, always ask for feedback and, if you get it, try to make the most of this information by reinforcing your strengths and working on your weak points. If you follow all the suggestions from this article, then landing your first job as a data analyst is just a matter of time, persistence, and hard work.


In this article, we talked in detail about what practical actions you should take to become a data analyst. We followed the whole process step-by-step, from how to learn data analysis to how to land your first job in this sphere. We discussed: 

  • the prerequisites you need to have to start learning data analysis
  • how to select the optimal program 
  • why individual projects are so important for your portfolio and where to search for datasets
  • other projects that can be added to your portfolio
  • the technical and soft skills companies usually look for in a data analyst
  • the nuances and tricks for creating an outstanding resume
  • the importance of your online presence, visibility, and interaction with data specialists
  • where and how to search for a job and how to keep optimistic and confident whilst applying

With all this information in hand, it's time for you to go ahead and start learning.

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