With 328.77 million terabytes of data being created every day, the role of a data scientist has now become increasingly popular worldwide.
This surge in data creation has sparked interest among many people, including those without a formal degree. However, there is often confusion about whether it is possible to join this field without a degree and, if so, how one can actually achieve it.
If you, too, have this confusion, you have come to the right place. In this article, we will explore the reality of becoming a data scientist without a traditional degree. So, let’s get started!
Can you become a data scientist without a degree?
Traditionally, a degree, particularly in fields like computer science or mathematics, has been required for data science roles. However, things have changed now.
Due to the availability of extensive online resources, it has now become easier than ever to become a data scientist without a formal education.
Also, companies now value practical skills and real-world experience more than mere academic degrees that do not guarantee you have the skill set required for that particular role.
While a degree can be beneficial for credibility, it is not an absolute necessity for a career in data science.
An 8-step guide to becoming a data scientist without a degree
1. Start with self-learning
The journey to becoming a data scientist without a formal degree begins with self-learning. Self-learning offers numerous benefits, including flexibility and diversity.
Platforms like Coursera, Kaggle, DataCamp and edX provide courses designed by top universities and industry leaders. These courses often include hands-on projects, providing practical experience that is highly valued in the industry.
Many YouTube channels complement these courses by offering free, high-quality tutorials that make complex concepts accessible. Moreover, books and academic journals can also provide in-depth understanding.
Self-learning can be hard for some people as they do not have a mentor or environment that pushes them to work hard. To make this journey successful, remember that the key is to maintain a structured approach.
2. Learn key concepts and skills
To excel in data science without a degree, it is essential to gain an understanding of key concepts and skills. This involves concepts from various disciplines, such as statistics, mathematics, programming, machine learning, and data analytics.
Statistics form the backbone of data science as it allows you to understand and apply various analytical techniques.
Similarly, programming is crucial as these are the primary tools used in data analysis and machine learning. Python, SQL, R, and VBA are the top programming languages that a data scientist must know.
Additionally, you should have a strong grasp of machine learning concepts to understand complex data patterns.
3. Gain proficiency in data science tools
After learning the basic skills and gaining enough knowledge, it is time to learn data science tools. These tools are required for transforming raw data into meaningful insights.
Start by mastering Python libraries like TensorFlow, Keras, Pandas, and NumPy. They are required for data manipulation and analysis.
Familiarize yourself with data visualization tools like Power BI, Datawrapper, and Infogram. They will help present complex data findings in an understandable and visually appealing manner.
Learn version control systems, like Git, to track and manage code changes while collaborating with others.
You should also get expertise in big data technologies such as Hadoop, Spark, and Apache Kafka. They will all help you analyze large-scale data sets and extract value from them.
4. Work on your soft skills
In data science, soft skills are as important as technical skills are. These skills allow you to communicate your findings, collaborate with teams, and make better data-driven decisions.
Develop strong verbal and written communication skills. They will allow you to explain complex data insights more simply to non-tech individuals in your team.
Work on your problem-solving skills as well. Data scientists often face challenging scenarios where critical thinking and quick decision-making are required. Having these skills will help them navigate through complex data challenges effectively.
Time management and project management skills are also essential. These help prioritise tasks, meet deadlines, and smartly manage resources and projects.
Enhancing all these soft skills not only improves your professional effectiveness but also significantly increases your value as a data scientist in any organization.
5. Gain hands-on experience
Gaining practical experience is invaluable in the journey to becoming a proficient data scientist without a degree. It allows you to apply theoretical knowledge to real-world scenarios, sharpening your skills and understanding.
Start with personal projects or data challenges on platforms like Kaggle. It offers a multitude of problems to solve that not only help build your technical skills but also showcase your problem-solving capabilities.
Internships or part-time roles in data science can also provide practical experience. These opportunities often offer mentorship from experienced professionals. Even roles like data analysis or business intelligence that are somewhat related to data science can provide relevant experience.
These experiences are not just about technical skills. They help develop soft skills like problem-solving, critical thinking, and creativity.
6. Create an online presence
Showcase your skills, experiences, and interests to a global audience by creating an online presence.
Start by identifying your professional goals and the audience you want to reach so you know what platforms to use and which kind of content to create.
Next, create profiles on relevant platforms. LinkedIn is the preferred choice for professionals. It is where you can show your experience, share industry-related content, and connect with peers and leaders in your field. Platforms like X (Twitter) and YouTube can also be equally beneficial.
Since the competition is high on these platforms, you must maintain consistency by regularly updating your profiles. Share recent accomplishments, projects, or articles you have written. Boost your visibility by engaging with others and contributing to discussions.
Turn your online presence into an evolving portfolio that attracts interested hiring companies and helps them see your worth.
7. Connect with professionals
Networking is essential for building any career, especially if you do not have a formal degree. It allows you to connect with industry experts and learn from them. Additionally, it opens doors to new opportunities and insights.
Platforms like LinkedIn, Meetup.com, and dedicated data science forums are excellent for connecting with peers. Participating in discussions, webinars, and Q&A sessions on these platforms can enhance your visibility.
You should also try attending industry conferences, seminars, and workshops. They will allow you to learn about the latest trends and technologies in data science.
Volunteering for data science projects can also be a great way to network and gain experience at the same time. It can showcase your commitment to the field and lead to meaningful connections.
8. Stay updated with industry trends
The field of data science is continually evolving, which is why a commitment to lifelong learning is essential.
You can achieve this goal by reading industry blogs, attending webinars, enrolling in advanced courses, and participating in forums. Learning about all the updates and trends ensures that your skills remain relevant and competitive in the job market.
Entry-level data scientist jobs
Entry-level data scientist roles provide an opportunity to enter into this evolving field. These roles are typically assigned to recent graduates or individuals transitioning from related fields.
Some examples of these roles include:
- Data coordinator
- Business analyst
- Data collector
- Machine learning engineer (entry-level)
- Data science consultant (junior level)
- Quantitative analyst
- Statistical analyst
- Research analyst
- Data engineer (entry-level)
- Operations analyst
- Marketing analyst
- Business intelligence analyst
- Junior data scientist
What does a data scientist do?
A data scientist’s role is multifaceted. He/she has numerous responsibilities in an organization, including:
- Data collection and processing: Gathers raw data from various sources and cleanses it for analysis.
- Statistical analysis of the data: Applies statistical methods to interpret data.
- Machine learning and predictive modelling: Develops models using machine learning techniques.
- Data visualization: Creates graphs, charts, and other visual representations of data to help others understand data findings.
- Problem-solving and strategy development: Uses data-driven insights to solve business problems and help build better strategies.
- Collaboration and communication: Works with other teams and communicates findings to stakeholders.
- Continuous learning: Stays updated with the latest trends and adapts to new tools as required.
How much does a data scientist earn?
Data science is a high-paying field. However, the salary of a data scientist can vary widely depending on various factors such as location, industry, and experience.
On average, a data scientist can earn from $131000 to $189000 in the US. It includes the base and additional salaries (commissions, bonuses, etc.).
Conclusion: Let your data science journey begin
The journey to becoming a data scientist without a traditional degree is both challenging and rewarding. It requires dedication, consistency and determination.
Start this journey with self-learning. Gain knowledge through online resources and become proficient in essential data science tools. Sharpen your skills by doing personal projects. Build an online presence and connect with others.
Once you have enough experience and knowledge, apply for your dream job to start your career, where your findings can make a huge impact.