3 Ways to Break Into Data Science: Your Pathway to a Data-Driven Career

Data science is making a significant impact in today's business world, providing a competitive advantage in various industries. If you aspire to enter the world of data science, here are three main pathways that can open doors for you:

Education and Self-Learning

One of the most common ways to enter the field of data science is to acquire relevant education and start self-learning. Developing fundamental skills in statistics, mathematics, and programming languages will provide you with a solid foundation for your data science journey. Online platforms and courses offered by universities cover training in popular data science tools such as Python, R, and SQL.

Project-Based Experience

Transforming your theoretical knowledge into practical applications through your own projects is crucial. Developing applications that can solve real-world problems will offer tangible evidence of your capabilities to potential employers and showcase your willingness to work on projects. Sharing your projects on platforms like GitHub can help expand your professional network and attract attention.

Work Experience and Internships

Completing a data science internship or starting a position related to the field is an effective way to quickly enter the industry. Working on real projects, collaborating within a team, and learning from industry professionals can accelerate your career. Additionally, the professional connections established during internships can provide an advantage in your future job search.Keep in mind that data science is a continually evolving field, so you should always be open to learning and updating your skills. Determination, continuous learning, and practical experience are crucial elements for success in the world of data science throughout your career journey.


With the rising popularity and demand for data scientists, coupled with the well-documented shortage of skilled professionals, an increasing number of individuals are expressing interest in pursuing a career in data science. As time has progressed, I've encountered a growing number of inquiries on how to embark on a journey as a data scientist. Similar to many other professions, securing the initial job proves to be the most challenging, as numerous employers insist on candidates having prior experience. This situation often results in a challenging Catch-22: how does one secure their first job when most positions require pre-existing experience?

In this article, I aim to provide guidance based on my personal experience transitioning into data science several years ago, as well as my current role overseeing a data science department where I interview numerous candidates and review hundreds of applications annually.


The STEM Career Change:

• Advanced academic degree in a technical/scientific field.
• Several years of work experience in a related field.
• Strong background in mathematics and research.
• Capable of understanding the mathematics behind machine learning models.
• Transferable skills can facilitate a relatively quick transition into data science.

The Data Science New Grad:

• Recent graduates from MSc programs in data science.
• Programs often include statistics, electrical engineering, or industrial engineering departments.
• Comprehensive training that a short bootcamp may not cover.
• Thesis and publications provide an opportunity to discuss in-depth work during interviews.
• Evaluation focuses on alternative approaches, decision-making, and feedback handling.

The Optimist:

• Limited formal data science training and lacking an extensive statistics/math background.
• Experience in data analytics within a specific vertical (e.g., finance, healthcare).
• Desires to complement current skills to transition into a data science role.
• Emphasizes the importance of a general understanding of tools and algorithms over vertical-specific knowledge.