Data science is all about extracting meaningful insights from structured and unstructured data. It’s a technical field that requires skills such as mathematics, statistical analysis, programming, machine learning, data visualization, etc. Data science helps to find patterns within data so we can solve problems and make better decisions.
Data science careers are some of the fastest-growing jobs in the tech industry. Most data scientists start by obtaining degrees in computer science, data science or other related fields. However, it’s possible to break into the field without a degree. Here’s how to get started in data science without a degree.
1. Learn the Fundamentals
Anyone interested in data science must start with the fundamentals. If you already know basic mathematics, make sure you understand some important concepts such as probability, variance, standard deviation, integrals, linear algebra, and calculus. Then, you should also learn more about statistics and probability.
2. Choose a Specialty
Data science is a field that consists of many branches, such as data engineering, data visualization, machine learning, data analytics, business intelligence, database management, and natural language processing (NLP). Although these specialties are under data science, they are different from each other.
Most companies prefer to hire data scientists who have specialized skills over those who identify as generalists. So, aspirants are encouraged to focus on a single subdomain and become an expert in that area. You can choose a specialty based on your strengths, interests, or background experience.
3. Get Some Practical Projects Under Your Belt
Once you’ve learned statistics, programming, and databases, it’s time to build some projects. You can only build confidence and mastery by applying your theoretical knowledge to real projects. Data science projects for beginners can include chatbots, fake news detection, traffic sign recognition, and much more.
There are several online groups, applications, and resources to enhance your skills. For example, there’s a repository full of practice problems for pandas. They can help you gain a better understanding of data analysis. We also have Kaggle, a large data science community that runs machine learning competitions.
4. Take Short Courses
Although data science doesn’t require degrees or certificates, you can stand out from the competition by taking short courses. Ideally, they are meant to help you understand your chosen data science specialty. If you’ve been self-learning for a while, a short course would help to validate your knowledge and skills.
When trying to get into data science without a degree, you should consider completing a certificate in data science. A certificate can help to boost your portfolio and increase your chances of landing an internship or job. There are online data science certificate courses that can be completed in 18 credit hours.
5. Create a Kickass CV
A CV/resume is a written overview of your qualifications, skills, and work experience. As a data scientist your resume is as important as your portfolio. List all your previous experience, including personal and group projects, competitions you won or ranked high in, bootcamps and internships, etc.
Remember, you’re going up against degreed candidates, so your CV/resume needs to shine. You should showcase relevant data science projects and highlight some key data analysis or machine learning skills that helped you complete each project. Mention any degree or data science certificate that you may have.
6. Market Yourself
If you’re starting a new career as a data scientist, make sure to put yourself out there. You need to let people know that you’re a data scientist who’s looking for opportunities. There are many ways to showcase your data science projects and market yourself.
You need to set up a Github portfolio/repository and add all your projects. You can create a personal website or blog where you can write articles or blog posts about data science. Make sure to be active on social media and be a part of data science communities too.
7. Hone Your Interview Skills
Once you’ve received an invitation to interview, you should start preparing for it. It’s not always the person with the most qualifications that lands a job. Make sure to research the role, understand what your responsibilities or tasks are and check for any tools or technologies that you need to be familiar with.
Most data science interviews include technical and behavioral interview questions. They are used to measure a job candidate’s qualifications and skills. A data science interview would also test your experience with algorithms, SQL, product development and analytics, and so on.
8. Get a Job in a Related Field
Most entry-level data science roles require candidates to have a degree and some work experience. But working in a similar field such as computer science, statistics, information technology (IT), or business analytics can help you move into an entry-level data science job without needing a degree.
You can also apply to smaller companies and startups that are ready to work with data science interns. An internship would help you gain exposure and tackle real-world problems that are in the data science field. You can also take on pro bono data science projects for NGOs and small businesses.