Key Takeaways
- To become a data scientist, you need a solid education, technical expertise, hands-on experience, and a strong portfolio that showcases your skill set.
- Key skills for this role include programming, statistics, machine learning, data visualization, problem-solving, domain knowledge, and effective communication.
- Becoming a data scientist requires committing to continuous learning and community engagement in order to stay updated, build connections, and tackle challenges effectively.
In recent years, technology has advanced at an incredible pace, completely transforming the way we work and live. Businesses everywhere are now embracing data science and tapping into its potential to derive valuable insights from massive volumes of information. This shift has definitely elevated the importance of skilled data science professionals and their ability to apply their expertise across many industries.
For those interested in this field and who want to learn how to become a data scientist, the most important question is whether to focus on hands-on experience, education, or mastering specific tools. The answer is a blend of all three and a few more steps.
6 Steps to Become a Data Scientist
Becoming a data scientist is one of the most rewarding career choices you can make. It’s all about persistence, curiosity, and willingness to develop a blend of technical and analytical skills needed for the responsibilities associated with the role.
Although there are different approaches one could take in pursuing data science, the following steps to become a data scientist offer a clear and practical option toward success.
Step 1: Earn a degree
The foundation of a career in data science begins with an undergraduate degree in a relevant field that can help you master programming languages, data handling, and statistical methods. Some popular choices include data science, computer science, mathematics, or statistics.
At Syracuse University’s iSchool, an excellent option for this step is the Bachelor’s in Applied Data Analytics. It offers a blend of technical and practical training that prepares graduates for a variety of roles in data science and analytics across industries like consulting, finance, technology, nonprofits, sports, and more.
After learning the “what” of data science through undergraduate programs, you can focus on the “how” and “why” through a master’s degree program that covers a broader range of topics related to data science, like machine learning, artificial intelligence, and data visualization.
At Syracuse University, the Master’s in Applied Data Science program turns learning into doing. This applied approach ensures you master the skills needed for a career in data science.
You can always build upon this knowledge through certifications, as they’re a great way to validate your skills and show employers you’ve mastered specific tools and techniques.
The iSchool’s Certificate of Advanced Study in Data Science, for example, trains students in practical skills that cover the entire data lifecycle—from gathering and analyzing data to securely archiving it.
Some other options worth exploring would be the Google Data Analytics Professional Certificate or certifications offered by organizations like the Data Science Council of America (DASCA).
Step 2: Build technical expertise
Technical expertise in data science is all about having the right mix of skills to make sense of the collected data and turn it into actionable insights. It starts with mastering programming languages like Python, R, and SQL, which are the go-to tools for many of the processes data scientists execute.
Mathematics and statistics are other areas of high importance for data scientists, making developing expertise in these areas essential. Machine learning and artificial intelligence add to that, enabling professionals to use algorithms like regression and neural networks to automate decision-making and uncover patterns in data.
What truly ties it all together is the ability to apply the knowledge and skills gained in all these areas within the context of a specific industry.
Step 3: Develop soft skills
In addition to technical expertise, becoming a successful data scientist requires developing soft skills such as critical thinking, problem-solving, communication, creativity, collaboration, and adaptability. You will need these skills in order to analyze data, draw meaningful insights, and then communicate those insights and visualize the data in a clear way that informs key business decisions
Most of these soft skills will naturally develop as you progress through your education and gain experience in real-world projects. As you work on data science tasks and engage with stakeholders, you’ll refine these skills and better equip yourself to apply data-driven insights and contribute to business success.
Step 4: Gain hands-on experience
It’s one thing to learn the theory behind cleaning, analyzing, and visualizing data, but it’s an entirely different challenge to work with real datasets that are messy, complex, and unpredictable. Therefore, through internships, freelance projects, or even hackathons, aspiring data scientists should aim to develop hands-on experiences.
Syracuse University’s Center for Computational and Data Science (CCDS) often presents students with different opportunities to push beyond textbooks and apply their skills to different projects. They provide research opportunities that immerse students in advanced computational techniques and emerging technologies.
Experiences like this prepare you to tackle the different challenges of the job market and set you apart as someone capable of putting theory into practice.
Step 5: Develop a portfolio and resume
When it comes to landing a data science role, your portfolio is where you showcase what you’ve accomplished and, more importantly, how your skills come to life in solving real-world problems. Share the thought process behind each project: Why was the problem worth solving? What impact did your solution have?
When creating your portfolio, you can use platforms like GitHub or a personal website to present your work in a visually engaging and easy-to-follow way.
Your resume, on the other hand, is how potential employers will create their first impression of you. It’s your chance to connect your experience with what the job demands. So, it’s smart to tailor it to every application.
It’s important to include measurable results—did your analysis lead to a 15% increase in efficiency? If so, make sure you mention it. Moreover, highlight certifications and training that validate your knowledge.
Together, your portfolio and resume should complement one another: the portfolio shows what you can do, and the resume explains why you’re the perfect fit for a specific role.
Step 6: Apply for entry-level data science roles
After building the skills, gaining experience, and showcasing your work, the final hurdle is actually landing your first role. So, start by tapping into networking opportunities—attending industry meetups, engaging with professionals on LinkedIn, or connecting with alumni from your program. Building genuine relationships can often open doors that traditional applications can’t.
Make sure your LinkedIn profile is polished and aligned with your career goals. Use the space to, once again, highlight your technical skills, projects, certifications, and any relevant experience. Recruiters and hiring managers often use LinkedIn to scout talent, so your profile should act as an extension of your resume.
If you join us at Syracuse University, the iSchool’s Career Services will provide immense support at this stage. From resume workshops to mock interviews and personalized guidance, we help you with all of your career-related needs.
Essential Skills Required to Become a Data Scientist
The core skills every aspiring data scientist should develop include:
- Proficiency in programming languages
- Strong foundation in statistics and mathematics
- Knowledge of machine learning algorithms
- Data visualization skills
- Familiarity with big data frameworks
- Data cleaning and preparation techniques
- Understanding of database management systems
- Problem-solving and critical thinking
- Domain knowledge relevant to the industry you work in
- Competence in A/B testing and experimental design
- Collaboration skills
- Understanding of ethical considerations and data privacy laws
- Adaptability to learn and use new tools and technologies
- Communication skills for explaining insights to non-technical audiences
Common Career Paths in Data Science
For many people interested in data science, the ultimate goal is a role as a data scientist. This career displays incredible growth potential, with employment projected to grow by 36% between 2023 and 2033. On top of that, the median annual wage for data scientists sits at a substantial $108,020.
But the thing is, the skills and education gained in data science don’t limit you to this one title. In fact, the expertise you develop opens doors to many exciting career paths. Some of these roles include:
- AI engineer
- Business intelligence analyst
- Computer vision engineer
- Data analyst
- Data consultant
- Data engineer
- Data science manager
- Healthcare data analyst
- Machine learning engineer
- Marketing analyst
- Natural language processing engineer
- Predictive analytics specialist
- Quantitative analyst
- Research scientist
Challenges in Becoming a Data Scientist and How to Overcome Them
Pursuing a career as a data scientist can be as demanding as it is rewarding. For many, one of the biggest hurdles in this pursuit is the steep learning curve. Learning programming languages like Python and R, along with complex algorithms and statistical concepts, can be difficult—particularly for those who don’t have any knowledge or skills relevant to computer science or math.
For career changers in particular, this challenge is often compounded by imposter syndrome—the persistent feeling that you’re not skilled enough or don’t truly belong. This doubt can be fueled by comparing yourself to experienced professionals or feeling unprepared to deal with technical challenges despite your efforts.
However, imposter syndrome isn’t exclusive to career changers. Even those with relevant backgrounds can face these feelings when venturing into a field as broad and demanding as data science.
Addressing and understanding these challenges, or any others you may face, is the first step toward managing them. Some additional actions you can take to help you overcome these challenges include:
Break down the learning curve
Becoming a data scientist can feel overwhelming if you try to master every skill at once. A better strategy is to tackle one skill at a time. Start with SQL—it’s a fundamental tool for working with databases. Once you’ve got the hang of it, move on to Python, R, and any other language you would like to explore.
From there, as your confidence builds, you can start with machine learning algorithms and other more complex topics. By breaking your learning into small, manageable steps, you’ll find the process less overwhelming.
Tackle imposter syndrome head-on
It’s important to remember that all experts were once beginners and that nobody knows everything. So, acknowledge your progress, no matter how small, and celebrate your successes along the way.
Additionally, never hesitate to reach out to mentors or other experienced professionals for advice, as they’ve likely been in your shoes and can offer guidance or simple reassurance. The key is to focus on steady growth and learning rather than aiming for perfection right out of the gate.
Embrace continuous learning
In a field like data science, where new tools and methods emerge all the time, staying up-to-date is necessary, and it requires a commitment to continuous learning. So, explore online courses to build your expertise in areas that interest you or where you feel less confident. Get involved in active communities like Kaggle or Reddit to learn from others and share your own insights.
Don’t underestimate the value of industry conferences and workshops—they’re fantastic for not just gaining knowledge but also meeting others who share your passion.
Engage with the community
Platforms like LinkedIn are great for finding professionals, starting conversations, and gaining inspiration. Similarly, local meetups and virtual events provide plenty of opportunities to swap stories, solve problems together, and learn from peers who are navigating similar paths.
Building such relationships can help you stay motivated and remind you that you’re not the only one going through all this or facing challenges.
The Path Forward in Data Science
Pursuing a career in data science is a decision full of possibilities, where you can solve important problems and contribute innovative ideas. Even if this choice comes with its own set of challenges, it’s all manageable when you have a clear plan—knowing which skills to develop, understanding what the job entails, and being prepared for an industry that’s always evolving.
Our programs are designed to help you grow in tandem with the field itself, consistently incorporating courses that reflect the latest advancements and focusing on the tools and expertise graduates need to succeed. They are designed to ignite your growth, fuel your learning, and open doors to endless opportunities in data science and beyond.
Frequently Asked Questions (FAQs)
How long does it take to become a data scientist?
On average, it takes around 4–6 years if you start with a bachelor’s degree and then build on that foundation with certifications or hands-on experience.
How do you become a data scientist with no experience?
You can begin by mastering some of the most important skills for the role, like coding and data analysis, then work on real-world projects to build a portfolio and seek out certifications or internships to gain even more practical experience.
Can I become a data scientist without a degree?
Technically, yes. However, relying solely on self-learning or online courses is far more challenging, as these options often lack the structured guidance, comprehensive foundation, and opportunities for practical application that formal degrees provide.
Is coding required for data science?
Absolutely. Coding enables data scientists to cover tasks like analyzing data and creating machine learning models, with Python and SQL being two of the most widely used languages.
What degree is best for becoming a data scientist?
Degrees in fields like data science, analytics, computer science, statistics, or mathematics are some of the best options for gaining the skills required in this career.
How hard is it to become a data scientist?
It can be challenging, but with dedication, it’s entirely doable. The path requires learning technical skills, developing relevant soft skills, and a commitment to continuous learning.
What is the difference between a data scientist and a data analyst?
While data analysts focus on examining data to answer questions, data scientists seek deeper insights by developing predictive models and advanced algorithms.