From Beginner To Data Analyst: How To Change Careers And Excel In Data Analytics

Jun 15

Written By L T

women teaching each other about career change to data analyst

Today, I want to share with you a high-level view of what it takes to change career paths to become a data analyst. I've been in your shoes—curious about making a career change into the world of data analytics without previous experience. And no, AI is not getting rid of the data analyst role, but new data analysts will need to learn the necessary skills that complement the technical savvy that AI-powered software brings to the table. If you crave the chance to learn new skills that offer high value and problem-solving abilities while gaining a deeper understanding of business and helping drive impactful decisions, then a role as a data analyst may be perfect for you.

Why Consider a Career Change to Data Analytics

More and more, companies rely on data-driven insights to thrive in today's competitive landscape. As a result, having skilled analysts who understand how to extract meaning from large datasets is crucial. This high demand means job security and growth potential for those who have transferable skills from other areas and are entering the field. In fact, the Bureau of Labor Statistics predicts a 21% increase in jobs for data analysts by 2030!

What's more, the skills you cultivate as a data analyst are highly transferable. Whether your background is in finance, teaching, or engineering, abilities like problem-solving, communication, and critical thinking will serve you well in data roles. Plus, you'll develop new proficiencies with technologies like machine learning and artificial intelligence that open up advanced opportunities down the road.

Data Analysts earn well. A junior data analyst on my team earns between $68,000 to $72,000. With generous compensation packages on top of that, there are also strong financial incentives to join the field and begin your journey in data analytics. For those motivated by making an impact, data analysis lets you fuel important business decisions across industries from healthcare to nonprofit organizations, as it has been in my case. Does this career path start to appeal more?

What Does a Data Analyst Do?

As a data analyst, your day-to-day responsibilities may include activities like:

Gathering data from sources like databases, web analytics, and customer surveys
Cleaning, organizing, and structuring raw data
Performing statistical analysis using techniques like linear regression, hypothesis testing, and more
Creating compelling data visualizations in tools like Tableau, Power BI, or Excel
Identifying trends, patterns, and outliers that provide answers or raise new questions
Presenting insights and recommendations to stakeholders in an accessible way
Testing assumptions and hypotheses to solve problems or support new initiatives
Ongoing optimization by continually improving processes and reports

The key is extracting meaning and value from data through organization, examination, and storytelling. You'll collaborate with teams across an organization to help inform business decisions at all levels.

Building Your Technical Foundation

Of course, you can't jump straight into these roles without developing your technical skillset first. Or so you think, right?

As a hiring manager for analysts, I strongly believe that a curious mind, business acumen, and the disposition to learn are the most important things for this role. Technology savvy is very important and essential, yes, but secondary. The good news is learning opportunities abound—whether through online classes, programming tutorials, or data science bootcamps. A few core technical skills to focus on include:

SQL: The lingua franca for querying and managing relational databases
Python: A versatile language well-suited for data analysis tasks like cleaning, modeling, and visualization
Statistics: Concepts like descriptive analysis, hypothesis testing, regression, and probability theory
Data visualization tools: Tools like Tableau and Power BI allow you to share insights visually

Online programs from edX, Coursera, and Udacity offer affordable, self-paced paths to gain these skills. Bootcamps offer live instruction and sometimes career coaching. For example, I completed Google Analytics and Coursera's Data Analytics Track when I first started learning about data (over 10 years ago) and found the syllabus, resources, and mentorship extremely valuable.

Finding Your Ideal Learning Path

Not everyone learns in the same way. Here are some alternatives worth considering:

University Degree: Popular options include a Bachelor's in Statistics, Computer Science, or Information Systems. Degrees provide a solid theoretical foundation. Plus many universities offer affordable online degree programs.
Online Certificates: Self-paced certificate programs through Coursera, edX, and IBM give an introduction for more moderate costs than degree programs. Plus, you can often progress from beginner to advanced levels.
Internships: Unpaid or paid internships are invaluable for applying skills in real work settings. Many tech giants offer programs designed to lead to full-time analyst roles after completion.
Kaggle Competitions: Kaggle hosts real-world data science challenges with cash prizes. Completing these projects exposes you to valuable problem-solving experience.

Continuous learning through online courses and certifications will remain key as this field evolves rapidly. I also recommend connecting with local analytics user groups to stay up-to-date and build your professional network.

Leveraging Your Existing Skills & Work Experience

Fortunately, you don't need to feel like you're starting from scratch. Many transferable attributes that serve you well in your current role can be leveraged as a data analyst. For example:

Project managers' ability to coordinate tasks and deliver reports on time is highly applicable.
Teachers excel at communicating complex topics simply through visualization and storytelling.
Engineers problem-solve by breaking large challenges into discrete steps.
Healthcare workers are accustomed to using data to inform care decisions and monitor outcomes.

When crafting your resume and cover letter, be sure to highlight these soft skills along with any numerical, technical experience you have. Hiring panels and recruiters love when applicants can clearly draw connections between their prior experience and the data analysis profession during the interview process.

Learning Soft Skills Essential for Success

Naturally, technical skills alone won't cut it. Developing these soft attributes will prove invaluable in your budding data career:

Communication: Whether presenting findings to executives or working cross-functionally, conveying analyses clearly is paramount.
Problem-Solving: Being able to break down complex, open-ended issues and devise logical solutions will impress.
Attention to Detail: Data analysis requires accuracy down to the decimal place. Analytical skills are not just focused on the quantitative aspects, but also the qualitative components of a business problem.
Curiosity: Staying inquisitive about the why, not just the what, will lead you to discover richer insights.
Adaptability: Expect requirements to evolve—be flexible in your approaches.
Project Management: Meeting deadlines amid shifting priorities requires strong planning, excellent communication skills, and prioritization.

Leverage online courses in business writing, public speaking, and more to sharpen these soft traits. Better yet, seek out roles like volunteering as a virtual tutor where you can practice explaining concepts patiently.

Gaining Valuable Hands-On Experience

While learning core technical and soft skills lays your foundation, nothing beats real-world experience in data analysis. Here are some of the most effective ways to build up your portfolio:

Pursue analytically-focused projects on Kaggle competitions and open data repositories like GitHub.
Provide pro-bono data consulting services or donate your skills on platforms like DataKind.
Complete freelance projects posted on sites such as Upwork to get client exposure.
Volunteer your services at local non-profits or do analysis for community organizations.
Consider paid internships at startups through sites like AngelList or major companies' programs.
Contribute solutions and participate actively in forums like Stack Overflow to help others learn.

The projects you produce should highlight your abilities to perform each step of an analysis from data collection to insights presentation. Be sure to document your processes and publish your work online for potential recruiters to review.

Landing Your First Role & Growing Your Career

Once you feel equipped technically and experientially, it's time to start applying. To stand out, focus your resume on quantifying your accomplishments in a clear, scannable format. Highlight relevant skills, project outcomes, and metrics.

For interviews, be prepared to discuss methodologies in detail using real examples. Expect technical scenarios, probability questions, and case studies testing your analytical abilities too. Come armed to ask questions showing interest in their data challenges and goals.

Network actively through analytics group gatherings and on sites like LinkedIn. Read job listings closely to understand what employers truly value. Look beyond just job titles too—how can your portable skillset transfer to adjacent roles involving data science, business intelligence, or data engineering?

Growing Your Career

Women mentoring one another in data analyst coaching group

Once you land your first role, aim for advancement by gaining experience across industries, tools, and techniques. Climb from associate to senior analyst levels by pursuing certifications in machine learning or big data platforms as these skills become ever more crucial. Eventually, opportunities may emerge to manage analytics functions or transition into specialized industries entirely.

To stand out, focus your resume on quantifying your accomplishments in a clear, scannable format. Highlight relevant skills, project outcomes, and metrics. It's also important to sell yourself confidently. Research shows women face already a myriad of barriers towards equality in STEM roles, so emphasize your strengths without apology. Don't feel you need a "perfect fit" to apply either. Many roles can be learned on the job.

Networking is key as most roles are filled through referrals. Connect with women's networking groups in data and tech. Participating in We Are Analytics, Anthropic's mentorship programs or gaining intro at a conference can open doors. On LinkedIn, engage actively by supporting other women.

Employers also seek diversity. If passionate about a particular domain, consider healthcare-focused analytics at clinics/hospitals or working with educational non-profits. Roles in marketing analytics teams that serve female customers are also popular within women who want to support other women.

Getting involved in diversity initiatives can position you for manager roles where influence over culture matters. Technical skills alone won't take you to the top; committing to the growth of others around you plays a key part too.

The last tidbit of advice is to cultivate your leadership and gain experience across industries, tools, and techniques as you climb the ladder from associate to senior analyst levels, and mentor other women along the way. That is why I'm motivated to share the knowledge acquired through years of hard work and what I have also acquired from other women in the field (and men!) who were so generous with their mentorship. Let's pay it forward.

Diverse career paths and self-starting spirits have led many down this fulfilling road. With focus, passion, and your existing skills, I have no doubt you'll excel too. Stay encouraged—you've got this!

L T