The best online data science certifications

Data scientists are high in demand. Organizations are increasingly eager to employ data experts that can extract meaningful information from vast array of data. Certification may be crucial to break into this lucrative discipline or distinguish yourself from the competition.

Data science credentials not only allow you to learn skills that are difficult to find in your field but also assess and validate your data science expertise, allowing employers to know what they're getting if they hire you.

Whether you're looking to get certified by an accredited institution, gain experience as a recent graduate, improve vendor-specific skills, or show your understanding of data analytics, the following online certifications (listed in alphabetical order) are worth considering.

Cloudera data platform generalist certification

The Cloudera Data Platform (CDP) Generalist certification assesses an individual's understanding of the platform. The new test covers general information about the platform and is valid for various jobs, including the administrator, developer, data analyst, data engineer, data scientist, and system architect. The online exam has 60 questions and lasts 90 minutes.

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

The Data Science Council of America's (DASCA) Senior Data Scientist (SDS) certification program is for people with five or more years of research and analytics experience. Students should be familiar with databases, spreadsheets, statistical analysis, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. The five tracks of the program include four degree-level options, each with its requirements for application. To be considered for each track, you'll need at least a bachelor's degree and more than five years of data science expertise. Other tracks require a master's degree or prior certifications.

Data Science Council of America (DASCA) Principal Data Scientist (PDS)

The Principal Data Scientist (PDS) certificate from the Data Science Council of America (DASCA) is a three-track program for data scientists with 10 or more years of big data experience. The exam covers everything from the ground up, including basic to advanced data science topics such as big data best practices, company strategies for data, developing cross-organizational support, machine learning, natural language processing, theoretical modeling, and more. The credential targets "seasoned and high-achieving data science thought and practice leaders."

IBM data science professional certificate

The IBM Data Science Professional Certificate consists of nine courses in data science, open-source tools, data science methodology, Python, Databases and SQL, data analysis, data visualization, machine learning, and a capstone project. The online classwork for this certification is offered by Coursera, which provides a flexible schedule and takes an average of three months to finish. The course includes real-world exercises to help you create a portfolio demonstrating your data science skills to potential employers.

Microsoft Certified: Azure AI fundamentals

The Microsoft Azure AI Fundamentals certification verifies your understanding of machine learning and artificial intelligence ideas and how they relate to Microsoft Azure services. Because it's a foundation test, you don't need much expertise to pass it. It's a fantastic way to demonstrate your skills and knowledge to employers if you're new to AI or AI on Azure.

Microsoft Certified: Azure data scientist associate

The Microsoft Azure Data Scientist Associate credential helps you master the art and science of applying machine learning to your data on Azure. Candidates for the certification must demonstrate their knowledge of machine learning, artificial intelligence solutions, natural language processing, computer vision, and predictive analytics. You'll need to be knowledgeable in deploying and managing resources, controlling identities and governance, developing and managing storage, and configuring and administering virtual networks.

SAS certified AI and machine learning professional

The SAS AI and Machine Learning Professional credential validates your ability to apply open source technologies to data analysis using AI and analytics skills. The test includes machine learning, natural language processing, computer vision, and model forecasting and optimization. To acquire the AI and Machine Learning professional designation, you'll need to pass the SAS Certified Specialist examinations in Machine Learning, Forecasting and Optimization, and Natural Language Processing and Computer Vision.

SAS certified advanced analytics professional using SAS 9

The SAS Certified Advanced Analytics Professional Using SAS 9 credential validates your ability to evaluate big data using various statistical analysis and predictive modeling methods. You'll need experience with machine learning and predictive modeling techniques and their application to huge, distributed, and in-memory data sets. You should have previously worked with pattern detection, business optimization techniques, and time-series forecasting. Three examinations must be passed to obtain this designation.

SAS certified data scientist

The SAS Certified Data Scientist certificate is a mix of the other two data certifications offered by SAS. It covers data programming, management, and development, as well as data transformation, access and manipulation, and usage of popular dashboard tools. To earn the SAS Certified Data Scientist credential, you must first obtain the Big Data Professional and Advance Analytics Professional certifications. You'll need to finish all 18 courses and pass five exams between the two separate certifications.

TensorFlow developer certificate

The TensorFlow Developer Certificate program is a "foundational certificate for students, developers, and data scientists who want to demonstrate practical machine learning skills through the deployment and training of models with TensorFlow" that will help you build ML apps. The exam checks your understanding of and ability to apply machine learning to various tools and applications. To pass the test, you must know the fundamentals of ML and deep learning and the basic building blocks of ML models, image recognition algorithms, deep neural networks, and natural language processing.