Key Takeaways
- To become a data analyst, you need formal education, relevant skills, hands-on experience, and a commitment to ongoing learning.
- Data analysts rely on technical expertise in programming, statistics, and data visualization, combined with strong problem-solving, communication, and teamwork skills.
- The iSchool at Syracuse University is ideal for aspiring data analysts, offering a curriculum tailored to industry needs, hands-on experience, and a 93% employment rate.
Every purchase made, flight booked, ad clicked, and social media post liked contributes to the ever-growing pool of data organizations can tap into. But simply having access to data isn’t enough—it’s what businesses do with it that makes the difference. That’s why the demand for data-literate professionals is rising. One such role that has become indispensable across many industries is that of a data analyst.
How to become a data analyst? This is a question many aspiring professionals ask, but the answer isn’t as simple as just learning a few technical skills. It’s about formal qualifications, hands-on expertise, and the right approach to finding your ideal position in this role.
Step-by-Step Guide to Becoming a Data Analyst
While you can get started in this field in multiple ways, the preferred steps to becoming a data analyst include building a strong foundation through formal education, developing key skills, gaining hands-on experience, and continuously refining your expertise.
Build a strong foundation
To become a data analyst, you need to build a foundation in the core principles of handling and interpreting data. Some people may attempt to do so through self-study, but relying solely on online courses and tutorials can leave gaps in knowledge and limit exposure to real-world applications. Formal education, on the other hand, provides structured learning, mentorship, and access to resources that help build a well-rounded skill set.
At Syracuse University’s iSchool, students can build a strong foundation in data analytics through programs like the Applied Data Analytics Bachelor’s Degree, Applied Data Science
Master’s Degree, or the Applied Data Analytics Minor. The programs offer a blend of technical coursework and real-world case studies, and the minor allows students from different disciplines to gain data skills alongside their primary field of study.
The curriculum of all our programs is structured in such a way that it helps students gain technical expertise and an understanding of how data fits into the larger business and organizational world.
Gabriel Davila-Campos, a student in Applied Data Analytics, offers a firsthand look at what makes the iSchool experience so valuable. His words reinforce just how immersive and practical the program is, as he expresses that:
“That’s the thing about Syracuse – it’s more than just a university. It’s a community where you’re encouraged to explore, make mistakes, and grow… In my classes, whether it’s ‘Data in Society’ or ‘Intro to Networks & Cloud,’ I find myself constantly challenged and intrigued. It’s not just about learning the theories; it’s about seeing how these concepts come alive in the real world.”
Learn the right skills
To become a data analyst, you need to master a set of technical skills. It’s through these skills and knowledge that you’ll be able to extract insights from data. Some of the most essential ones include:
- Proficiency in Python, R, and SQL
- Expertise in data visualization tools such as Tableau and Power BI
- Strong command of advanced Excel functions
- Understanding of statistics and hypothesis testing
- Skills in data cleaning and preprocessing
- Ability to query and manage databases
Though these abilities are crucial, it’s just as important to know how to communicate findings and solve problems. Therefore, in addition to the technical skills, you also need to develop the following soft skills:
- Strong problem-solving abilities
- Effective communication skills
- Keen attention to detail
- Ability to collaborate in team environments
- Adaptability to changing tasks and challenges
- Efficient time management skills
Get hands-on experience
Knowledge alone isn’t enough—experience is another major factor in landing a job as a data analyst and succeeding in it. Employers want candidates who have worked with real-world datasets and are sure to know how to best apply their skills in practical scenarios. Many data analyst requirements specifically mention experience with problem-solving, data cleaning, and visualization tools.
A common misconception about formal education is that it’s mostly theoretical, but at Syracuse University’s iSchool, hands-on experience is integrated directly into the curriculum. But, in addition to that, some other ways you can gain practical experience include:
- Internships
- Freelancing
- Data competitions
Create your portfolio
A strong portfolio is one of the best ways to stand out when applying for any job, including data analyst positions. Through it, employers learn more about your skills, such as how you approach problems, the tools you use, and the results you produce.
When creating your portfolio, be sure to:
- Include diverse projects that showcase different aspects of data analysis.
- Clearly define problem statements to show the business challenges you tackled.
- List the tools and techniques used, such as SQL queries, Python scripts, or Tableau dashboards.
- Provide visual results, such as graphs, charts, or interactive dashboards.
Some good platforms to host your portfolio include GitHub, Tableau Public, and personal websites.
Land your first job
Once you’ve earned your degree, built the right skills, and put together a strong portfolio, the next step is actually entering the job market. As you prepare for your first data analyst role, it’s important that you:
- Craft a strong resume and LinkedIn profile that best highlights your skills and experience.
- Tailor your applications to the specific job descriptions provided instead of sending the same generic application to everyone.
- Network through industry events and online communities.
- Prepare for both technical interviews (which assess skills like SQL, Python, and data visualization) and behavioral interviews (which evaluate problem-solving, teamwork, and communication abilities).
At the iSchool, we offer Career Services & Support to help students easily transition from education to employment. Some of the services include:
- One-on-one career counseling to refine job search strategies.
- Resume workshops and mock interviews for application preparation.
- Recruiting events and job fairs to connect with employers.
- Handshake platform for job and internship listings.
Keep growing
Lastly, since data analytics is a field that is constantly evolving and advancing, then continuous learning is needed in order to stay competitive. To achieve this, consider:
- Earning certifications in specialized tools like Tableau, Google Analytics, or AWS.
- Pursuing an advanced degree, such as a master’s in a relevant field
- Exploring new technologies like machine learning and automation to expand your skill set.
- Engaging with the other professionals in the community through forums, conferences, and networking events.
How Long Does It Take to Become a Data Analyst?
The time it takes to become a data analyst depends on your approach, your starting point, and how much time you dedicate to learning. Those who follow the steps we’ve highlighted and begin their pursuit with a formal education through a bachelor’s degree in a relevant field typically take four years to complete.
Alternative options like boot camps, certification programs, or self-study can take anywhere from a few months to a year. However, these options often lack the depth and hands-on experience a university program offers.
Regardless of the way you get there, in all cases, learning doesn’t stop after landing a job. Data analysts should continue refining their skills, pursuing advanced certifications, and staying updated with industry trends to remain competitive and grow in their careers.
Common Challenges Aspiring Data Analysts Face
Many aspiring analysts may feel overwhelmed, question whether they have the right skills, or even doubt if they’re cut out for the role at all. It’s natural to face moments of hesitation, especially when confronted with the following challenges:
Lack of clarity on where to start
The sheer volume of information about data analytics can be overwhelming. Beginners, in particular, can struggle with questions about what they should learn first or whether they’re on the right track. This uncertainty makes the career feel intimidating, leading some to hesitate before even taking the first step.
That’s exactly why we emphasize the importance of formal education. Instead of trying to piece together knowledge from countless resources, a well-designed academic program offers clarity regarding what you need to learn to break into the field. These programs have been designed by industry experts and educators who understand what it takes to succeed in this career.
Difficulty in learning technical skills
The technical side of the job can also be challenging. Learning programming languages like Python, R, and SQL, or working with tools like Tableau and Power BI, can feel overwhelming—especially for those who don’t come from a technical background.
Unlike some fields where a basic understanding of tools may be enough, data analytics requires true proficiency in these skills, making it impossible to simply “get by” with surface-level knowledge.
However, learning, in general, becomes less intimidating if you just break the process into small, achievable steps and practice consistently. Instead of trying to learn everything at once, focusing on one tool or concept at a time makes it much more manageable.
Imposter syndrome
Doubt in oneself can easily creep in when comparing yourself to experienced professionals or seeing someone else land a job you wanted. Imposter syndrome can make you feel like you don’t belong, even if you’re fully capable.
It’s important to remember that growth matters more than perfection. While job competition is inevitable, opportunities in data analytics are abundant—especially for those who have the right skills and education.
At the iSchool, we take pride in having the highest job placement rate (93%) of any school at Syracuse University. This means our students are well-prepared for the job and step directly into rewarding careers in data analytics.
Building a Future in Data Analytics
If you’re serious about pursuing a career in data analytics, the best time to start is now. At Syracuse University’s iSchool, our programs are designed to adapt to the evolving demands of the field. We seek to equip students with both the technical expertise and real-world experience they need to stand out.
As Davila-Campos puts it:
“For anyone considering Syracuse, especially the iSchool, know that it’s a place where dreams are given the space to grow. It’s where your passion for technology and innovation will find a nurturing home, and where your academic journey will be as exciting as it is enlightening.”
So take that first step and join us!
Frequently Asked Questions (FAQs):
How long does it take to become a data analyst?
It takes around four years to earn a relevant bachelor’s degree or one to two years (for those with a bachelor’s) to earn a master’s degree—arguably the most important step for most people aspiring to become data analysts. On top of that, gaining real-world experience can add extra time.
Can I become a data analyst without coding skills?
Certainly! Many entry-level roles focus on tools like Excel and Tableau rather than programming. However, learning languages like SQL, Python, or R can open up more job opportunities.
What industries hire data analysts the most?
Since data plays an important role in decision-making, nearly every industry needs data analysts. Some of the biggest employers include business intelligence, healthcare, finance and banking, retail, and entertainment.
How do I stay updated with the latest trends in data analytics?
Reading industry blogs, taking online courses, attending webinars, joining data-focused communities, and networking can help you keep up with the latest tools and trends.
What’s the difference between a data analyst and a data scientist?
Both roles deal with data, but a data analyst mainly collects, organizes, and presents data to help businesses make decisions. A data scientist takes it further by using more advanced techniques like machine learning to predict future trends and find hidden patterns.