Required Experience to land a Data Analyst Job

Do I need a data analyst degree?

At JobStep, we hear the question of “Do I need a degree to be a data analyst?” often. The short answer is: No. You do not need to have a degree to become a data analyst. In this guide, we will walk through the type of data analyst experience you will need to build your career in data analysis.


What is a good data analyst degree?

Having a bachelor’s degree in analysis (majors including Economics and Math) can be helpful, but the most important part is your data analyst experience. You should focus on getting basic experience with statistics, Excel, SQL, and a visualization tool like Tableau. You can get experience by: 

  1. Doing extra projects at work with company data.
  2. Trying your hand at a Kaggle Competition.
  3. Finding data on your own through APIs and public data sets to complete side projects.
  4. Doing freelance work for an organization.

When evaluating data analyst degree programs, make sure that you will have the opportunity to work on business problems rather than just academic problems. This will give you data analyst experiences that translate much better to a work environment.


Online Data Analytics Resources

There is such a high demand for analytical professionals that many companies will hire those who got educated via online courses. At JobStep, we have audited online courses and resources to share a short list for you below.


Resources for starting out and building your data analyst experience:

  • Mode Analytics SQL School: Great resource for data analysts and has the best explanations on important concepts behind SQL, including “joins” and “where” statements. 
  • Khan Academy: Khan Academy is a helpful resource for visual and video-based learners looking to learn data analytics.


If you want more practice:

  • Managing Big Data with MySQL (Coursera): This data analytics course includes great real-world practice on how to manipulate data. 
  • Code Academy: Code Academy has the best interface for getting used to coding Syntax. 
  • SQLZOO: Once you’ve gotten familiar with the data analyst basics, this is a helpful place to practice your technical SQL skills. 


Keeping your SQL education up to date:

  • 1000 PROJECTS: This is a great data analyst resource to practice your SQL skills. 
  • W3Schools: This is a great data analyst reference for when you get stuck and want to Google how to do something quickly. 


Finding the Right Data Analytics Bootcamp for You

Another option to build your data analyst experience is by enrolling in a bootcamp. 


Look for bootcamps where they’ll introduce you to former students. Ask them about what types of jobs they have and see if their level of data analysis skills before the bootcamp was similar to yours. 


Consider asking some of these questions:

Questions to Ask When Evaluating a BootcampAnswers You Want to Hear
Do you feel the curriculum is relevant to what you’re doing now?Yes! It is very relevant to the work I do now.
Do you know whether the curriculum has changed since you graduated? If so, for better or worse?It has. They do a great job of auditing their curriculum and keeping it up-to-date.
How quickly did you work through the course material? What happened if someone was stuck on a subject?When I was stuck, they provided office hours, practice problems, answers to questions, time in class to go over questions, and other resources to help.
Do you feel that you actually learned the material?Yes! The bootcamp pushed me to answer data problems on my own and figure out what situations to use the tools I was taught.
How did you choose the projects that you worked on?They provided some projects to reinforce the material and then provided an open-ended project where you had to find data, clean data, and decide which models/approaches to use yourself.
What were some pain points during your project period? How did you overcome them?There should be some pain points. Learning a new skill shouldn’t be easy.
What would you have done differently, with the benefit of hindsight? I would have asked for more resources upfront so that I could jumpstart my learning.
How long did it take you to find a job? Was your starting salary what you expected? What kind of job did you find?The bootcamp connected me to full-time roles with benefits in the salary target they promised. They also had a resume workshop day.
What did you not expect about the job search? What would you have done better?I was not confident talking about my new skills at first. I slowly got better at talking about the projects I did and the tools that I used. I made sure to share how my skills could transfer over into the career opportunities that I was applying for.
Given the cost of the bootcamp, do you feel the program was worth it? Would you consider a different program if you had to do it again?I think the cost was worth it. I learned a lot and landed an awesome job in data analysis!


When researching bootcamps, we suggest checking out the bootcamp’s placement stats that are evaluated by the Council on Integrity in Results Reporting (CIRR). JobStep also partners with select bootcamps. We find and apply on behalf of their students so they can focus on learning their new skills and acing interviews.


What data analyst experience is needed to land a job?

It can be stressful to worry about all the skills that you may need to have to land a data analyst job. We have done the research and you will want to have the following skill sets for an entry level role:

  • Experience with SQL.
  • Expert in Microsoft Excel and Google Sheets: specifically pivot tables, lookups, aggregating stats, and using formulas to run calculations.
  • Experience developing reports and dashboards utilizing BI visualization tools (Tableau, Microstrategy, Lookr, ggplot2, Power BI, etc.)
  • 1-3 analysis projects that you can walk through end-to-end.


To develop this experience, we suggest finding or creating 1-3 analysis projects. In these projects:

  • Decide what question you’d like to answer with data.
  • Compile and pull your own data.
  • Clean your data.
  • Explore your data to make a hypothesis.
  • Determine which type of analysis to run.
  • Build a visualization to be able to present your findings and recommendations.


This can be side projects you create, projects you work on in bootcamps, or previous projects from your work experience. Our data shows that if you are getting more than 1 interview for every 20 applications, your resume and your skills are just fine. If 40% of your first round interviews convert to a second round interview, you’re showcasing your relevant skills and experiences effectively.


Questions to Answer about your Data Analytics Projects

Here are questions about your projects that you should be ready to answer if you plan to land a data analyst position:

  1. What was the goal of the project? What was the complex business problem you were trying to solve?
  2. What was the data that you worked with? 
  3. Describe the data you were using. What features or variables were you looking at?
  4. How did you collect the data? Did you query it from a database?
  5. What approach did you use to analyze the data or research the key variables? 
  6. Did you find an answer? How did you prove you were right? What metric did you evaluate? 
  7. What kind of impact did you have on the company? Which metric did you use to measure business success? What insights did you learn from the project?
  8. How did you present your data to stakeholders/management? What decision was made after your presentation?


We have an example of answering the above questions about a data analysis project below following the SOAR framework:

Using the SOAR Framework to talk about your Data Analyst Experience

Situation: Please tell me when this was, where this was, what your role was, and enough context so I understand the situation.

When I worked at Acme Corp, the editorial team wanted a better way to share industry data with customers. It took me 5 business days to gather, clean and calculate the quarter and year-to-date percent change in market caps for 1000+ biopharma companies that would be used in the story and accompanying charts.

Obstacle: What was the problem, challenge, obstacle, or goal facing you? Why was it important to address?
While the data consolidation and market cap calculations were straightforward in Power BI, there was a lot of logic and data manipulation needed in order to handle edge cases and specific criteria requested by Editorial. I did most of this data manipulation within the Power BI application itself – which ended up being a mistake. It was the slowest report to update and refresh. This was extremely frustrating when testing out a logic tweak or changing a single parameter. Although the first iteration of the report “worked”, it usually took 1.5 – 2 hours to refresh, or would sometimes break because it took too long.
Action: How did you approach the obstacle? What steps did you take? How did you organize your work? Did you collaborate with others?
I was not satisfied with the first iteration of the report and wanted it to be more efficient. For the second iteration, I researched different ways to speed up Power BI. Rather than having the Power BI application do the heavy lifting of data manipulation, I realized a simple solution was query folding – have the source relational database perform most of the initial manipulation before importing into Power BI.
Result: What was the outcome of your effort? How did you know you succeeded? What did you learn?
The second iteration of the automated industry report was successful. It only took 30 minutes to refresh and calculate the market caps, giving me ample time to check the results for any anomalies before handing it over to Editorial. As a result, collecting and cleaning the data for the story went from about 5 business days to 1 business day resulting in an 80% reduction.


If you are planning to land a data scientist role, we suggest considering these additional questions:

  1. What technology did you use to train the model or build the prototype? What programming languages did you use?
  2. What approach did you use? What types of algorithms or analyses did you use?
  3. How did you evaluate your model? What did you do for validation and testing? What metric did you use? 
  4. How does your approach compare to prior art in this space? 


If you are interested in learning more about data science, check out our article here (COMING SOON!) explaining the difference between data analysis and data science.


The typical career path of a data analyst

The differences between data analyst roles are driven by industry context and type of work required. This can be separated into two different buckets.


Bucket One: Reactive data support vs project-based 

Data support roles are often embedded in Customer Success, Customer Support, and Operations teams. In these roles, you analyze the company’s data to find answers to questions as they come up. For example, a Customer Success team may want to know how many users logged into an application over the past month. The data analyst would query the database in reaction to this question. 


Project-based data analyst roles take on longer term tasks and responsibilities, such as building a dashboard that tracks monthly recurring revenue (MRR) for various products. A project-based data analyst may also research application logs to analyze the specific product features that customers use the most. These tasks are forward-looking, plugging into planning and roadmap activities. 


These projects often require a key understanding of business metrics coupled with SQL and a visualization tool like Tableau to create a live dashboard.


Bucket Two: Product creation vs business systems 

Many software companies are building data products, which requires data processing, programming, or algorithm development. These companies will often hire a team of data scientists to build these data products. Data analysts are sometimes hired as junior members of these development teams. If you are interested in growing into data science, this could be a great entry point to start your career.


Business systems data analysts typically serve a back office function that looks internally at the business. They will research a business’s Profits/Loss, sources of revenue, marketing, among other areas.


From there, you can map out your data analyst experience and career growth in a few ways.

Have more questions about the data analyst experience needed to land a data analyst job?

We know that this is a lot of information and you may be stressed with how daunting this process looks. We believe that job seekers should only have to focus on preparing for and showing up to interviews to land a job. Our team at JobStep specializes in helping job seekers break into and build data analyst careers. JobStep gets you 5 interviews in 6 weeks by finding and applying to jobs that are great for you.