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Is a degree compulsory for data science?

Data science’s success has increased its marketability and importance to different companies. As a result, more businesses are embracing data and using it to get fantastic results. Therefore, it is not surprising that so many companies are setting budgets aside to hire qualified data professionals to hold different capacities.

But does one need a degree to land a job in data science? Answered simply, we can say no. You don’t need a degree to start your career in data science. With that said, we need to note that you do need high education level to get a position in data science. More than half of data scientists with active roles are master’s holders. About 20 percent of those in data science have a bachelor’s degree. So even though a degree is not compulsory for data science, it boosts your employability chances.

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Entering data science with a degree that is not technical

Many opportunities can help you get into data science, even with a non-technical degree.

You could be a holder of a degree in physics, medicine, economics, finance, or statistics before realizing you have some interest in data science. Then, you can make a switch. But unfortunately, many data scientists have made it without getting a STEM degree.

You must often work in a given industry before realizing that your passion is entirely elsewhere. Making a switch brings questions and doubts, especially about qualifications. The beauty of data science is that you can make the switch even when your academic background is different.

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Learning data science with no degree

A university degree is usually closely guided by a clear syllabus that determines the educational journey you are about to take. You could face issues when you choose to learn by yourself without a curriculum backing. The best thing is to work on creating a proactive attitude to help develop a road map that is ideal for you. Like other learning curves, you should work with what you already know.

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Data science fundamentals

With data science, you can lay a firm foundation using familiar concepts you may have tackled, even without specialization. They include:

1.Probability

2.Statistics

3.Linear algebra and mathematics

These are what make data science what it is today. Those who can handle linear regressions, normal distributions, and calculus equations have a great chance of success. When the basics have been covered, more complex topics can be tackled. These include deep learning and machine learning. In addition, AI algorithms, computer vision, and neural networks should be covered. All areas don’t need mastering unless you want to be a machine learning engineer. All the basics should be covered.

Data science programming

Programming is not restricted to software developers alone. Data professionals use code to develop the most effective models and derive workable insights using data. This is why you should fluently learn a programming language for a successful career in data science. This could be intimidating, but possible.

The main issue with programming is that many options are available today, and choosing is not always easy. It is easier to master Python because the syntax is beginner friendly with many functionalities and libraries.

SQL is highly recommended as well. This language is helpful in the management of databases and data extraction.

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Getting data science experience without a degree

In many careers, employers look for work experience when hiring. The same is true with data science. To offer a company value, you must understand their needs and use skills to improve their performance and work. This is possible when you are experienced. However, what happens if you don’t have a degree in data science and zero experience? Getting into data science may be easier for graduates because they are already exposed to the field. However, boosting your career or profile without theoretical bounds is still possible.

Build a portfolio using projects in data science

After finding the best platform to suit your needs, you can become fluent in data science. As such, you must prove to the employers that you know what is required in the field. Working on data science projects is the best way to get skills. These have to be mastered over time, and practice can go a long way to help you create a portfolio to forge forward.

You should start with ready-made projects. Then, you can follow codes to help you recreate results independently.

The projects you pick to work with are essential and play a big role. For example, if you are passionate about a job opportunity or industry, you can collect data related to that field or choose related data sets. Such efforts propel you forward and should be viewed as a motivation.

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Data science competitions

Data science hackathons and competitions are a good way of practicing the skills you have learned along the way. On Kaggle and other such websites, events are hosted to assist organizations. In many cases, a prize is offered after the competition. Shining in such an event helps you get noticed, especially when your performance stands out.

Volunteer for tasks in data science

This is another way to work on your portfolio. You can offer services in data science. Some companies haven’t adopted data approaches in running businesses. Take time to identify companies that may need such services and approach the managers. You can give them a taste of what data can help them achieve. Volunteering in one company can lead to recommendations and more paying gigs.

Internships in data science or roles at the entry levels

Many companies will hire data scientists based on their experience. To be competitive, you need some relevant sections in your resume. To work on this, find an internship program and get started. Even if you qualify for bigger roles, you can choose to start at the entry-level. The experience you get helps build your resume. You may also have a chance of being absorbed by the company you work for.

Data Science Placement Success Story

Conclusions

If you are passionate about data science, there are many ways of getting started, and a degree is not compulsory. You need to work on acquiring the needed knowledge and skills to thrive in data science. Experience is also essential and can be gained in different ways. Regardless of your level, you need to network with others with the same interests as you in the data science world. This way, you will be in a community for support and meaningful connections. 

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