How To Become a Data Scientist With No Experience | 9 Steps

A data scientist uses analytical tools to pull information from data. They collect and categorize data and decide which is useful for projects. Data scientists also use data visualization software to present findings.

While starting this career sometimes requires a bachelor's degree, becoming a data scientist without experience is possible. Learning fundamental concepts and programming languages are two ways to start a data scientist career with no background in the field. You can also build a strong portfolio, get hands-on experience, and network with experts in the field.

Below, we'll outline a roadmap for finding a data scientist job without experience. We'll discuss why you should master data analysis and machine learning. We'll also detail how you can tailor your job search strategy.

Can I Start a Data Science Career Without Experience?

You can start a data science career without experience in the field. The key is building your data science skills and knowledge. Once you've gained the right background, you could secure data science jobs that include:

• machine learning scientist
• data analyst
• statistician
• business intelligence developer
• enterprise architect
• data engineer
• business analyst

You can also qualify for jobs in software engineering and artificial intelligence. There are plenty of great options for people interested in data science. Next, we'll review the steps you can take to enter this field without experience.

How To Get a Data Science Job Without Experience

A bachelor's degree in math, statistics, or computer science can help you start a data science career. However, it's not your only option. Keep reading to learn how to secure a data science job with no experience.

Step 1: Learn the Fundamentals

Learning the fundamentals of data science is crucial to starting a career in the field. Core concepts include:

• data analysis
• computer science
• mathematics
• statistics

Data science courses, data science boot camps, and independent study can teach you fundamentals like using computational tools to extract patterns from data sets. Courses to consider taking include data analytics, modeling, linear algebra, and statistical inference and classification.

Online Courses and Resources

Professional organizations and sites offer free data science courses online. Some include:

• IBM SkillsBuild: free data science courses and resources for students just getting into data science who want to learn its foundations and fundamentals
• Udemy: free data science courses and tutorials covering Python, logistic regression, and SQL for data analysis
• Azure Cloud Advocates at Microsoft: 10-week, 20-lesson data science curriculum for beginners that teaches data science ethics, statistics and probability, and big data

Step 2: Learn Essential Programming

Data science experts must know how to develop and test algorithms for accuracy. They also clean raw data, which means structuring data to make them readable by software programs. A programming course in data science covers these topics and teaches programming languages like the following:

• Python
• R
• SQL
• Julia
• VBA (Visual Basic for Applications)
• SAS
• Java
• C++
• Swift

Learn more: Where Can I Take Free Online Programming Courses?

Step 3: Master Data Analysis and Visualization

Collecting and analyzing data is a primary duty in data science. Experts also use data visualization software to present findings and communicate their analyses to technical and non-technical audiences.

To work in data science, you want to become familiar with regression, time series, and cluster analysis. Understanding network and geographic visualization, neural networks, and deep learning is also important.

Data manipulation tools to learn include Pandas and NumPy. Tableau, Power BI, and Matplotlib are important visualization tools.

Step 4: Become Familiar With Machine Learning

Machine learning is a subset of AI focused on learning models from data. Once learned, the models are used to label and predict future data. Data scientists develop models and algorithms to support programs for machine learning. Scientists with a strong coding background might go deeper into the field by building machine learning algorithms.

Key machine learning concepts to explore for data science include:

• regularized regression
• principal component analysis
• deep neural nets
• ensemble methods with trees
• cross-validation

Step 5: Build a Portfolio With Real-World Projects

After gaining a strong data science background, building a portfolio to showcase your knowledge and technical skill set to hiring managers is next. You can use code-based portfolios to highlight real-world data science projects like the following:

• interactive website or app
• data analysis for a trending topic
• dashboard built around a specific data set
• scraping a data set

You could also consider a content-based portfolio that includes blog or video coding tutorials or a podcast in which you share your knowledge or interview data science experts. Your portfolio can include a story or overview of your work experience, relevant skills, specializations, certifications, and journey to data science. GitHub is a good place to share your portfolio.

Step 6: Network and Engage With the Community

Connecting with data science experts is a great way to open doors to entry-level positions in the field. Professionals can guide you to data science programs, boot camps, and tech companies seeking candidates. They can serve as mentors who help you build your data science background and prepare for your chosen career path.

You can use social media sites like LinkedIn and online IT forums to find data science experts for networking. You can also attend in-person meetups and conferences.

Step 7: Gain Practical Experience

Practical experience helps you prove to potential employers that you have the right data science knowledge and skills. If you're enrolled in a degree program, consider gaining experience with an internship at a tech company. You could also take freelance projects with solo clients and small businesses. Volunteering for nonprofits or working on projects for friends and family can add experience to your portfolio.

Step 8: Tailor Your Job Search Strategy

Developing a job search strategy is important when searching for data science roles. You want to focus your search on entry-level positions that don't require a lot of prior experience and allow you to learn on the job.

Customizing your resume for data science jobs and creating a LinkedIn profile that outlines your experience and shares a link to your portfolio can enhance your search. Practicing your interviewing skills with your mentor or a career services center can boost your confidence and improve your ability to secure a role.

According to the U.S. Bureau of Labor Statistics (BLS), employment for data scientist roles is projected to leap 36% from 2023 to 2033. This increase is much faster than average when compared to all occupations. The median annual wage for data scientists was more than double that of all occupations in May 2023.

Step 9: Continue Learning and Advancing

Data science is an evolving field that requires continued education to excel. Consider enrolling in a certificate course like the IBM Data Science Professional Certificate, which teaches the latest in data science, including predictive modeling and how to clean data sets. Microsoft offers training for data scientists that could lead to certification. With more education and work experience, you could move into advanced or leadership data science roles.

Learn Data Science Today

If you're ready to start a data science career, explore the accredited degree programs offered by schools on Learn.org. Contact one of these colleges to learn how their programs can help you reach your career goals.