How to Break Into Data Analytics: A Roadmap

I love sharing my story about pivoting from a marketing career to analytics & data science. As a result, I get so many questions from people who are also interested in a career in analytics.

I share lots of advice on TikTok, Instagram, LinkedIn, and my data career newsletter. But I wanted to create a single career plan roadmap for landing a job in data analytics, more or less in order of how I would approach things if I were to try to break into data analytics today.

Summary — keep scrolling for more on each bullet point:

• Shortcut: Try to pivot roles at your current company or do data analysis in your current job to get experience.
• Start networking.
• Learn the technical skills.
• Build a portfolio to demonstrate skills and problem solving.
• Develop the transferable skills (aka “soft” skills).
• Optimize your LinkedIn profile and resume.
• Start applying for jobs.
• Prepare for interviews.
• Repeat or revisit any steps as necessary.
• Seek additional help (mentors, coaches, education) when necessary.

Shortcut: Transition Roles at Your Current Company

If you are currently working for a company that has data — can you pivot into a Data Analyst role? This is how many folks, myself included, made the transition.

If you can’t make that pivot, the next best step is to start doing data analysis in your current role, even if that’s not in your job description. Doing that is what led to my opportunity to pivot to an official analytics role at a former company. Being able to demonstrate that you can solve problems with data is the most important thing to be able to do in a job interview, so if you can start doing that in your current role, regardless of your job title, you can use that experience to get an analytics job.

Step One: Start Networking

Don’t wait until you’re job searching to start networking— by then, it’s too late. You want to have a strong network available before you start asking for job referrals. Additionally, having a network to turn to will make it easier to get answers to your questions and get advice. Learn more about the benefits of networking.

Not sure where to start? Check out my complete Networking Guide, and check out my suggestions for where to find people to network with, my networking tips for introverts, and a list of Slack & Discord communities for data analytics — great places to find people to network with.

Step Two: Learn the Technical Skills

Without the basic technical skills on your resume or LinkedIn profile, you’re going to have a hard time landing interviews or getting through interview questions or technical assessments.

What technical skills are employers looking for when it comes to data analytics roles? It varies, but the most common are:

• Excel — knowing pivot tables, formulas, and visualizations is often enough to start delivering business value
• SQL — are you comfortable writing queries to get the right data? Joining tables? Aggregating data? Creating new columns using CASE WHEN or calculations?
• Tableau or PowerBI —can you import or connet to data and create visuals that quickly answer questions and tell as story? Also, don’t worry about becoming an expert at both. If you are comfortable with one you can learn the other if necessary.
• Basic quantitative abilities — are you comfortable calculating mean, median, percentage, rate, lift? And can you figure out when to use which for your analysis?

Once you’re comfortable with the above list, I’d also recommend:

• Python — data cleaning with Pandas, and data visualization using a package like Matplotlib, Plotly, and/or Seaborn. (Knowing how to do similar tasks in R is also valuable, and you’ll easily be able to learn Python if necessary.)
• Basic statistics — especially hypothesis testing.

And if you’re comfortable with all of the above, you’ll stand out even more if you learn predictive algorithms like linear and logistic regression.

Want a list of specific topics to target for each item above? Check out the list of specific technical skills I’ve used on the job.

Not sure where to start? Click the links above or check out my list of books for learning analytics, and also check out online courses via Analytics Mentor or the Google Data Analytics Certificate.

Often I get asked — what is necessary? Are online courses enough? What about certificates? College degrees? Do you need a master's degree? There is no single answer but this post of mine goes into much more detail. Ultimately, it depends on what other experience you have and what your goals are.

Step Three: Build a Portfolio with Projects

Learning the skills is not enough — you need to demonstrate that you can apply them as well. You’re going to be asked in interviews about your experience solving problems with data.

The best route to do this in your current job — if you’re able to get access to data at work, even if it’s not in your job title or job description — start digging in and applying what you’ve learned and see if you can start solving business problems.

If that’s not an option, you can do your own projects. I recommend doing at least 3–5 projects — but more is better. The goal is to have enough projects to demonstrate the skills that you have (SQL, Tableau, Python, etc). But the more projects you have, the more you have to talk about during job interviews.

You can also do projects via competitions (like the WiDS annual datathon or check out Driven Data) or volunteer with a group like DataKind, Delta Analytics, National Student Data Corps, or Viz for Social Good.

Not sure where to start? Check out my resources for

• how to do data analytics projects
• what makes a good data analytics project
• a list of project ideas
• free public datasets to use for projects.

You can host your projects on GitHub (examples) and add a link to your resume and LinkedIn profile and easily share your portfolio with hiring managers.

Step Four: Practice Soft Skills

While technical skills will help you get interviews, it’s the soft skills that will help you get a job offer. This includes things like:

• Problem solving
• Communication
• Collaboration
• Project management
• Critical thinking

How can you practice these skills?

If you have any work experience, even if it’s not data-related, you are probably developing all of the skills above.

If you are currently in school, joining a student organization — and getting a leadership role — is a good way to start developing these skills. Getting an internship will also help you develop these skills as well

If you’re not in school, join a local industry meetup group (search MeetUp.com or Google) and ask if you can help organize events. This will help you develop all of the skills above. Toastmasters International is also a great group to join if you are uncomfortable with public speaking. Like it or not, presenting your work to groups big and small is part of working in data analytics.

When you do your own projects, you will start developing problem solving and project management skills. If you create a blog to showcase your projects (or use the READ ME files on GitHub), you will start developing communication skills.

Networking is also a great opportunity to talk to other people about your work — explaining your projects will help you develop communication and presentation skills.

Also read up on case studies and how companies approach solving their business problems with data. Case study questions are very common in data analytics interviews.

Step Five: Optimize Your LinkedIn

Having a LinkedIn profile is such an important tool for your career search.

First and foremost, recruiters use LinkedIn to proactively look for job candidates. If you have a great LinkedIn profile, you could get approached for a job without even submitting an application.

Additionally, make sure you always have a resume ready to go. Here is a resume template you can use.

Need to improve your resume and/or LinkedIn? Check out my tips on how to optimize your LinkedIn profile and resume for analytics and data science jobs. I also offer resume and LinkedIn reviews.

Beyond the job search, LinkedIn is also a great resource for research.

• If you have an interview scheduled, look up the profiles of the people you’ll meet with.
• If you have a dream job and/or company you want to work for, look up the profiles of people doing that job or working at that company. What experience or degrees did they obtain prior to landing their job?
• If you’re researching certificate or graduate programs, look up the profiles of people who have already completed those and see what they are doing now. Does that match your goals?

Step Six: Start Applying

Really, you can start applying at any time. You don’t have to wait until all of the above is done. As soon as you decide that you’re interested in a career in analytics, regardless of your skills and experience — just start applying. See if you get any responses. Accept any requests for interviews — even if you bomb an interview, you’ll get insight into where you need to focus when it comes to developing skills.

Good resources for finding jobs:

• Job listings on DataAnalyst.com, DataJobba, LinkedIn, Indeed, Otta, and similar websites
• Your newsfeed on LinkedIn — many people post open roles at their companies or repost open roles on behalf of others in their network — this is a great way to get a referral
• Slack & Discord communities — many have a channel dedicated to posting job opportunities, and this is another great way to get job referrals
• Research the companies that hire interns and new grads
• I also share entry-level job openings on LinkedIn

Step Seven: Practice for Interviews

Interviewing is … tough. Every company has a different process, asks different types of questions, has different expectations, etc. Read more about data analytics interviews.

Even if you’re experienced, you still need to prepare and practice for interviews. This includes:

• Brainstorm examples of your projects that you can use for all the typical “tell me about a time you…” interview questions. Review the STAR method for answering questions.
• Come up with a list of potential questions you’d like to ask during interviews. Download my list of 100+ potential questions.
• Practice your technical skills like SQL. Sites like Dataford, Interview Query, and others are great for getting hands-on experience.
• Research the company. Check out their website, their About page, their mission & vision, their Wikipedia page, and search Google News for any recent articles. Make sure you understand what the company does, and have an answer for “why are you interested in this role/company?”

If you’ve been networking, see if you can find someone who is interested in practicing or doing a mock interview with you. Additionally, if you schedule an interview with a company, see if you can find someone in your network (or Slack communities) who works for that company and is willing to share their insights into the company, the interview process, etc.

Step Eight: Repeat

None of the steps above are things you’ll do once and never do again. I’m always learning and practicing my skills. I’m always updating my LinkedIn and my resume. I’m always networking. The amount of time I spend doing the above varies depending on what else I have going on in my life or if I’m actively job searching or not. But you’re never “done,” when it comes to your career journey, there is always room for improvement.

Step Nine: Need more help?

If you’ve done all the above and still have questions, it might be worth finding a mentor or career coach to help you. You can book time with me if you think that would be helpful.

What do you think? Anything else you’d add? Any other questions about a career in analytics?

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