Are you looking for guidance on how to become a data scientist without a degree?
Data science is a fast-growing field that’s generating a ton of interest. And it’s no wonder why – data scientists are responsible for turning data into insights that can help businesses make better decisions, improve operations, and drive growth.
If you’re interested in a career in data science but don’t have a degree, you may be wondering if it’s even possible to break into the field.
The good news is that with learning platforms like LinkedIn Learning and Skillshare it is possible to become a data scientist without a degree – but it will take some extra effort on your part.
Here’s what you need to know to become a data scientist without a degree.
What is a Data Scientist?
What does a data scientist do in more detail? Data scientists are responsible for analyzing data to find trends and patterns that can be used to make decisions about everything from product development to marketing campaigns.
They use their findings to help businesses improve their operations, grow their revenue, and better serve their customers. As a result, they work in a variety of industries performing different tasks. For example, a data scientist working for a retail company may be responsible for analyzing customer purchase data to find trends that can be used to improve the company’s marketing strategy.
On the other hand, a data scientist working for a healthcare organization may be responsible for analyzing patient data to find trends that can be used to improve the quality of care.
What Skills Do You Need to Become a Data Scientist?
A career as a data scientist is exciting, but also demanding. Success in the field demands having certain skills come easily to you from being highly developed. What are these skills? Here are the essential skills needed to become a good data scientist:
- Technical skills. Technical knowledge of statistical analysis, coding languages, and data visualization tools is important for data scientists. Statistical analysis skills are necessary for identifying patterns in data. Coding languages like Python are commonly used by data scientists to perform statistical analysis. Data visualization tools are used to turn complex data sets into easy-to-understand visualizations.
- Communication. Data scientists also need to be able to communicate their findings to non-technical staff and decision-makers. They need to be able to understand the business’s goals and objectives and align their findings with those goals. Additionally, they need to be able to present their findings in a way that is easy for non-technical staff to understand. For instance, they may need to present their findings in the form of a report, infographic, or presentation.
- Problem-solving skills. Data scientists need to be able to identify problems that can be solved with data and then use their technical skills to solve those problems. For example, a data scientist working for a retail company may identify a problem with the company’s marketing strategy. They would then use their statistical analysis skills to find a solution to that problem like identifying a new customer segment to target.
- Project management. Data scientists often work on projects with different moving parts. As a result, they need to be able to manage those projects effectively. They need to be able to monitor the progress of the project, identify risks, and adjust the project plan as needed. For instance, a data scientist working on a project to improve the quality of healthcare may need to manage a team of analysts, coordinate with different departments within the healthcare organization, and ensure that the project is completed on time and within budget.
A Step-by-Step Guide to Becoming a Data Scientist Without a Degree
Degrees are expensive, especially a degree in data science which can run upwards of $100,000. Degrees also take 4 years to complete on average. That’s 4 years of your life that you could be working and earning money instead of going to school.
So how do you become a data scientist if you don’t have the money to pay for a degree or the time to pursue it?
Here is a step-by-step guide:
1. Learn with Short Courses
A short course is a great way to learn data science basics without spending a lot of money or taking 4 years to complete a degree.
There are many different providers of online data science courses, but some of the most popular include Coursera, LinkedIn Learning, and Skillshare.
These courses typically range in price from $50-$200 and can be completed in a matter of weeks or months from the comfort of your home.
Additionally, most of these courses offer a certification upon completion which can be added to your resume.
2. Get Practical Experience
In addition to taking courses, it’s also important to get practical experience working with data. There are several approaches to getting the experience you need.
First, you can participate in online data science competitions. These competitions are a great way to hone your skills and show potential employers what you’re capable of.
Additionally, you can look for internships at companies that work with data. Even if the position isn’t explicitly for a “data scientist,” you’ll still gain valuable experience working with data that you can use on your resume and in future job applications.
You can also try to find a mentor who works as a data scientist. A mentor can help you learn more about the field, identify resources and opportunities, and give you advice on your career journey. If you don’t know anyone who works as a data scientist, there are several online communities (such as Women in Data Science) that can connect you with a mentor.
3. Build a Portfolio
Then, once you have the skills and experience necessary, it’s time to start building your portfolio. Your portfolio should showcase the projects you’ve worked on and the skills you have developed. For each project, be sure to include a description of what you did, the data you used, and the results you achieved.
Additionally, if possible, include links to any code you wrote or visualizations you created. By having a strong portfolio, you’ll be able to demonstrate to potential employers that you have the skills and experience necessary to be a successful data scientist.
4. Sign up for Freelancing Sites
Once you have a strong portfolio, you can start looking for data science jobs by signing up for freelancing sites. On these sites, you can create a profile that includes your skills, experience, and portfolio. Then, when companies post data science projects, you can apply to work on them.
This is a great way to get started in your data science career without having to commit to a full-time position.
Plus there are a lot of great benefits to freelancing. You bet to set your hours, work from anywhere in the world, and choose projects that interest you.
5. Network with Other Data Scientists
In addition to finding jobs through freelancing sites, it’s also important to network with other data scientists. This can be done by attending data science meetups or conferences, participating in online forums, or even just connecting with people on social media.
By networking with other data scientists, you’ll be able to learn about new opportunities, get advice and feedback on your work, and make connections that can help you further your data science career.
When networking with other data scientists, be sure to focus on building relationships rather than trying to sell yourself. You want to come across as someone interested in collaborating and learning, rather than someone who is just looking for a job.
6. Keep up With the Latest Tools and Trends
Finally, it’s important to keep up with the latest tools and trends in data science. This can be done by reading data science blogs, following data science leaders on social media, and subscribing to data science newsletters.
By staying up-to-date on the latest developments in data science, you’ll be able to make sure your skills are current and you’re aware of the latest job opportunities.
Additionally, keeping up with the latest trends will also help you be more successful in your data science projects. By using the latest tools and techniques, you’ll be able to work more efficiently and produce better results.
A Final Word on Becoming a Data Scientist Without a Degree
Data science is a career choice that offers a lot of opportunity and potential for growth. And while a data science degree can certainly help you in your career, it’s not required.
There are many ways to become a data scientist without a degree, including teaching yourself data science skills, building a strong portfolio, and networking with other data scientists.
So if you’re interested in a data science career but don’t have a degree, don’t let that stop you. With hard work and dedication, you can achieve your goals.
Do you have any questions on how to become a data scientist without a degree? Let us know in the comments below!