Written by Coursera Staff • Updated on
Data scientists are among the top jobs in the US. Learn how to become one with this guide.
Data scientists bring enormous value to companies by harnessing the power of data and using it to make predictions and draw actionable insights. Between 2022 and 2032, the US Bureau of Labor Statistics (BLS) projects data scientist jobs to grow by 35 percent. This figure represents an average of 17,700 job openings annually [1]. Data scientists also earn higher-than-average salaries and generally feel satisfied with their work-life balance [2].
In this article, we'll discuss how to become a data scientist—with little to no experience—and key skills and technologies you'll want to learn to succeed in this career path.
How long does it take to become a data scientist?
The amount of time it takes to become a data scientist depends on your previous experience, education, and skills. If you're transitioning into data science from a related career such as software engineering or machine learning, gaining the required competencies through certificate programs or online classes may only take a few months.
However, if you do not have a background in data and wish to pursue a data science career, you may need to earn at least a bachelor's degree or gain foundational knowledge and experience in the field through certificates and training to pursue this career. Entry-level data scientists are often expected to know SQL, data visualization tools, and statistical computing [3].
If you're considering earning a degree to break into the field, you'll spend around four to five years earning abachelor's degree (in computer science or a related field) and an additional two years earning amaster's degree. This timeline can vary depending on your chosen course and method of study.
How to become a data scientist without a degree
Many employers request at least a bachelor's degree for entry-level data scientist roles. However, it's possible to pursue a data science career without a degree.
Some employers are willing to hire candidates with the proper skill set who are up-to-date with the latest technologies and methods. Focus on building your data science skill set and earning alternative credentials (sometimes called micro-credentials) to demonstrate your competency to employers.Follow the suggestions below for more guidance.
Build technical skills in data science.
The list below outlines some of the most in-demand technical skills for data scientists.
Database design
Data cleaning and visualization
Machine learning techniques
Statistical modeling
Data warehousing and querying
Multivariate statistics, such as regression principal components analysis and clustering
Big data management
Learn at least one programming language.
A few of the most commonly used programming languages among data scientists include:
Python
R programming
SAS
SQL
Ruby
PHP
VBA (Visual Basic for Applications)
Read more:Python or R for Data Analysis: Which Should I Learn?
Familiarize yourself with key data science technologies.
Core platforms and technologies data scientists may use day to day include:
Hadoop
MapReduce
MySQL
MongoDB
Oracle
Tableau
Microsoft Excel
Power BI
Microsoft Copilot
Read more:7 Skills Every Data Scientist Should Have
Strengthen workplace skills for data scientists.
Data scientists must competently determine the best way to process, analyze, and articulate data findings to people of various backgrounds. Here are a few of the most crucial workplace skills to sharpen for a successful career in data science:
Problem-solving skills
Verbal and written communication skills
Teamwork
Curiosity
Persistence
Agreeableness
Leadership
Articulation
Further your education in data science.
Certifications and certificates are an excellent way to fill the gaps in your knowledge and enhance your resume, whether you've earned a degree or not. Top certifications for data scientists in 2024 include:
Certified Analytics Professional (CAP)
SAS Certified Data Scientist
Open Certified Data Scientist (Open CDS)
IBM Data Scientist Professional Certificate
Cloudera Certified Associate (CCA) Data Analyst
Dell EMC Data Scientist Track (EMCDS)
Google Professional Data Analytics Certification
Tensorflow Data Scientist Certificate
Read more:Your Guide to Data Science Certifications in 2024
I liked that the program had introductory courses covering a wide range of topics with practical assignments, engaging and clear video lectures, and easy-to-understand explanations. As with the other courses I took on Coursera, this program strengthened my portfolio and helped me in my career.
— Mo R., on taking the IBM Data Science Professional Certificate
How to become a data scientist with no experience
This next section walks through the steps to become a data scientist without experience. Keep in mind that everyone's journey is different. The step-by-step below follows the career path of someone wanting to earn a degree, build a portfolio, apply for an entry-level role, and work up to the data scientist title.
1.Obtain the necessary education.
Fifty-one percent of data scientists have a bachelor's degree, 34 percent have a master's degree, and 13 percent have a doctorate. Typically, data scientists major in computer science, statistics, or mathematics. Relevant but rarer majors for this role include physics, electrical engineering, economics, and computer engineering [3].
Read more:Data Science Major: What You Need to Know Before Declaring
2.Sharpen your skills and enhance your resume.
The skills you should focus on may vary depending on the industry you'd like to work in. Consider browsing job postings for positions you'd like to apply to. You can gain insights about industry-standard tools and technologies and in-demand programming languages. Then, you might supplement your education by earning a certificate in a particular programming language, taking online classes, or becoming certified in a specific platform.
Another way to stand out from other candidates is to build a strong portfolio. Your portfolio can include academic projects, freelance work, and even personal projects as long as they adequately demonstrate your skills and represent your own work. If you're looking for ways to bulk up your portfolio, consider checking out the list of Guided Projects below. You'll accomplish job-relevant tasks with expert-level guidance every step of the way.
Data Analysis with SQL: Inform a Business Decision
Build a Data Science Web App with Streamlit and Python
(FREE)Data Science Coding Challenge: Loan Default Prediction
(FREE)Data Science Challenge
Exploratory Data Analysis with Python and Pandas
3.Apply for an entry-level data scientist job.
Now that you've gained the necessary skills and experience, you can start applying. Data scientist jobs requiring little or no experience will often be listed as Data Scientist (Entry-level) or Junior Data Scientist. Here are a few other entry-level data science titles to keep an eye out for during your job search:
Data analyst
Juniordata engineer
Junior data modeler
Junior database administrator
You might also consider preparing with an industry leader by enrolling inIBM's Data Scientist Career Guide and Interview Preparationcourse.
Data scientist job search resources
Data Science Jobs: Resources and Career Guide
30 Data Scientist Interview Questions + Tips (2024 Guide)
Data Scientist Salary Guide: What to Expect
How to Land a Data Science Internship: 2024 Guide
6 In-Demand Data Scientist Jobs in 2024
4 Data Analyst Career Paths: Your Guide to Leveling Up
Further your data science knowledge with Coursera
IBM'sData Science Professional Certificatestrongly emphasizes applied learning. Once you've earned your certificate, you can add popular tools like Jupyter, GitHub, R Studio, and Watson Studio to your resume.
Or learn how to leverage generative AI, specifically Microsoft Copilot, in your data science workflows with the Microsoft Copilot for Data Science Specialization. You'll use Copilot to analyze data, generate insights, streamline data preparation, create visualizations, and effectively communicate findings.
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