Most universities now offer certificate programs in data science, reflecting the huge demand for data scientists. These graduate certificates are typically offered by the computer science, mathematics or in some cases the school's engineering departments. However, the certificates draw on many disciplines, such as environmental sciences, health and social studies, because of the demand for data science in these other fields.
Who Are Data Science Certificates For?
Professionals who want to understand the science that underlies the trends and patterns about information in today's world often pursue graduate and professional data science certificates. Graduate certificates are often offered as a pathway to a master's degree. But they are worthy, both for those already working in data science, and need the accreditation for career advancement, and for those working in fields that depend on data science to produce more efficient and deliberate operations.
Certificates take about one-third the time of a master's degree. Often the credits gained in certification can be applied toward a master's degree. In many cases, the courses can be audited or attended for free, such as those offered by EdX, but getting the certificate requires paying a per-credit fee or tuition that is comparable to that paid for a standard master's degree.
Admission to data science certification programs offered by accredited institutions generally requires proof of academic experience, particularly showing some proficiency in analytical disciplines, such as statistics, math or computer science. That means turning over copies of college transcripts, letters of recommendations and sometimes a personal essay.
Data Science Certificate Courses
There are hundreds of online data science programs to choose from and thousands of individual classes within those programs. Here is a sample of 25 programs, with links to the providers, a description of sample courses and estimates of costs.
Data Science, EdX/Harvard University (Cambridge, MA)
- Data Science: R Basics: Using a real-world data set about crime in the United States, learn the R skills you'll need to answer important questions about crime disparities between states.
- Machine Learning: Build a movie recommendation system and learn the science behind training data. Look at how to use a set of data to discover potentially predictive relationships.
Cost: Free; verified certificates for these two courses cost $99 for Machine Learning and $149 for R Basics.
IBM Data Science, EdX/IBM (Cambridge, MA)
- Data Science Tools: Learn about data science tools such as Jupyter Notebooks, RStudio IDE and Watson Studio. Find out what each tool is used for and how data scientists use these tools today.
- Machine Learning with Python: In this practical Introduction, get the tools you need to get started with supervised and unsupervised learning.
AI in Healthcare, Stanford Online/Coursera (Stanford, CA)
- Fundamentals of Machine Learning for Healthcare: Designed for those with non-engineering backgrounds, get an understanding of the fundamental concepts and principles of machine learning as it applies to medicine and healthcare.
- Introduction to Clinical Data: Get an introduction to a framework for successful and ethical medical data mining. Learn how to construct analysis-ready data sets and apply computational procedures to answer clinical questions. Explore issues of fairness and bias.
Cost: Courses are free to audit on Coursera. It's $79 per course to receive certificate. Students must complete four courses to be eligible for a certificate.
Marketing Analytics, Berkeley/EdX (Berkeley, CA)
- Marketing Analytics: Marketing Measurement Strategy: Learn how to execute market sizing, identify market trends and predict future conditions.
- Marketing Analytics: Competitive Analysis and Market Segmentation: Learn how to analyze your competition and effectively segment your market to improve overall customer satisfaction and company profits.
Cost: $896 for four courses
Data, Economics, and Development Policy, MITx (Cambridge, MA)
- Foundations of Development Policy: Advanced Development Economics: This course provides a deeper dive into how to design and evaluate effective policies to improve the lives of the poor.
- Political Economy and Economic Development: Explore why and how political institutions affect economic development. Apply key theories and empirical techniques to real-world examples ranging from voting and corruption to the role of the media.
Cost: Between $250 and $1,000 per course, depending on your household income.
Statistics and Data Science, MITx (Cambridge, MA)
- Data Analysis for Social Scientists: Get an introduction to the essential notions of probability and statistics. Learn about techniques in modern data analysis, including estimation; regression and econometrics; prediction; experimental design; randomized control trials (and A/B testing); machine learning; and data visualization.
- Data Analysis: Statistical Modeling and Computation in Applications: This is a hands-on introduction to the interplay between statistics and computation for the analysis of real data.
Data Science, American University (Washington, D.C.)
- Statistical Machine Learning: Explore supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis.
- Statistical Programming in R: Learn about basic programming, basic data structures, data wrangling, data cleaning, data visualization, exploratory data analysis, data import and export, relational data sets and data presentation.
Cost: $1,631 per credit hour; 12 credit hours required for certificate
Government Analytics, Johns Hopkins (Baltimore, MD)
- Probability and Statistics: Learn about the building blocks of descriptive and causal inference. By the end of the course, you should be able to conduct a statistical analysis to answer a meaningful policy question and be prepared to take more advanced methods courses.
- Quantitative Methods: Gain the knowledge and skills needed to perform a statistical analysis.
Cost: $4,234 per course; five courses required
Data Sciences, Columbia University Data Science Institute (New York, NY)
- Probability and Statistics for Data Science: This course covers the fundamentals of probability theory and statistical inference used in data science; probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem, statistical inference, point and confidence interval estimation, hypothesis tests and linear regression.
- Machine Learning for Data Science: An introduction to machine learning, the course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms.
Cost: Students enrolled in the Certification of Professional Achievement program pay Columbia Engineering's rate of tuition. Also, the online certification program has an additional non-refundable technology fee of $395 per course.
Data Science, Indiana University (Bloomington, IN)
- Computing Tools for Scientific Research: Get an introduction to computer-based tools useful for analysis and understanding of scientific data. Learn about basic methods of computation, data processing and display in systems such as MATLAB combined with elementary practical C/C++ programming.
- Machine Learning for Signal Processing: Find out about advanced signal processing topics as an application of machine learning. Learn to build an intelligent signal processing system in a systematical way.
Cost: $690 per credit hour, plus $30 fee
Data Science, Iowa State University (Ames, IA)
- Data Acquisition and Exploratory Data Analysis: Topics covered include file structures; web-scraping; database access; ethical aspects of data acquisition; types of data displays; numerical and visual summaries of data; pipelines for data analysis (filtering, transformation, aggregation, visualization and simple modeling); good practices of displaying data; data exploration cycle; graphics as tools of data exploration; strategies and techniques for data visualizations; basics of reproducibility and repeatability; web-based interactive applets for visual presentation of data and results.
- Concepts and Applications of Machine Learning: Topics covered include training and test sets; feature extraction; principles of machine learning techniques; regression; pattern recognition methods; unsupervised learning techniques; assessment and diagnostics (overfitting, error rates, residual analysis, model assumptions checking and feature selection); ethical issues in data science; and communicating findings to stakeholders in written, oral, visual and electronic form.
Cost: Requires the completion of 21 credit hours at $336 per credit for in-state residents and $968 for out-of-state residents.
SAS Data Science, California University of Pennsylvania (California, PA)
- Data Preparation and Cleaning: Get an introduction to methods for data cleaning and why they are needed. Learn about methods for locating and handling invalid values, out-of-range values and missing values along with methods for managing data sets. The course uses SAS software.
- Data Analysis Capstone Project: Get hands-on experience in the area of data science using real world data and SAS.
Cost: Online costs vary. An online tuition calculator is offered.
Enterprise Optimization, Elmhurst College (Elmhurst, IL)
- Data Mining and Business Intelligence: Examine emerging methods for recognizing patterns and making predictions through hands-on experimentation using software and case studies.
- Data Analysis for Business Transformation Projects: Learn to apply the project management tools and techniques needed to effectively plan and lead project teams on a business transformation initiative. You'll complete data science and project management assignments working individually as well as in teams using collaboration software.
Cost: $870 per credit
Data Analytics Engineering Graduate Certificate, George Mason University (Fairfax, VA)
- Analytics and Decision Analysis: Focusing on prescriptive and predictive analytics, learn about operations research techniques and their application to decision making such as mathematical optimization, networks modeling, statistical modeling and multi-objective modeling.
- Big Data to Information: Get an overview of big data and its use in commercial, scientific and governmental areas. Topics include technical and non-technical disciplines required to collect, process and use enormous amounts of data available from numerous sources.
Cost: $930 per credit hour
Data Science, George Washington University (Washington, D.C.)
- Introduction to Data Mining: Explore concepts, principles and techniques related to data mining as well as strengths and limitations of various data mining techniques, including classification, association analysis and cluster analysis.
- Data warehousing: Learn about the fundamentals and practical applications of data warehousing, including planning requirements, infrastructure, design and maintenance. To take this course, you should have an undergraduate degree with a strong background in science, mathematics or statistics.
Cost: $1,765 per credit
Business Analytics, Husson University (Bangor, ME)
- Data Exploration and Visualization: Find out how visual information is perceived, how to create effective visualizations, and what dangers exist with data visualization and how to avoid them. Design and create summaries and visualizations to transform data into information in a variety of context and complete a visualization project.
- Data Mining: In this introduction to data mining techniques and best practices, learn about classification, prediction, data reduction and data visualization as it relates to understanding the shape of your data. Get an understanding about advanced (multiple and logistic) regression, network and cluster analysis.
Cost: $516 per credit hour
Data Science, Lewis University (Romeoville, IL)
- Large-Scale Data Storage Systems: Learn about the design and operation of large-scale, cloud-based systems for storing data. Topics include operating system virtualization, distributed network storage, distributed computing, cloud models (IaaS, PaaS and SaaS) and techniques for securing cloud and virtual systems.
- Machine Learning: Gain an understanding of algorithms for enabling artificial systems to learn from experience; supervised and unsupervised learning; clustering; reinforcement learning and control. As part of the course, you write programs that demonstrate machine learning techniques.
Cost: $810 per credit
Data Science, Marquette University (Milwaukee, WI)
- Ethical and Social Implications of Data: Get an introduction to the ethical and social consequences of collecting, curating and analyzing data in academia, public and private contexts. A socio-technical stance is taken in unpacking issues of algorithmic biases, fairness, transparency and accountability.
- Data at Scale: Study concepts related to parallel databases, distributed systems and programming languages to analyze data at scale. Cost: $1,170 per credit
Data Science, Michigan Technological University (Houghton, MI)
- Predictive Modeling: Learn about the application, construction and evaluation of statistical models used for prediction and classification. Topics include data preprocessing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.
- Computer Simulation in Physics: Discover the role computer simulation plays in physics with emphasis on methodologies, data and error analysis, approximations and potential pitfalls.
Cost: $1,212 per credit
Big Data Management & Analytics, Missouri University of Science & Technology (Rolla, MO)
General Course Description: Gain proficiency in big data analytics across interdisciplinary areas including computer science, business information technology, mathematics and statistics and electrical and computer engineering.
Cost: $435 per credit (Missouri resident), $1,231 per credit (Non-resident), $1,200 per credit (Online)
Data Analytics, Northeastern University (Boston, MA)
- Introduction to Computational Statistics: Get an introduction to the fundamental techniques of quantitative data analysis. When you finish the course, you'll be ready to apply a wide variety of analytic methods to data problems, present your results to nonexperts and progress to more advanced course work.
- Information Design and Visual Analytics: Learn about the systematic use of visualization techniques to support the discovery of new information as well as the effective presentation of known facts. Requires proficiency in R.
Cost: $1,571 per credit
Artificial Intelligence, Saint Mary's University (Minneapolis, MN)
- Python for Artificial Intelligence: Focusing on using Python, explore performing, creating, and scripting. Gain experience in programming Python scripts and data science applications.
- Deep Learning: Special Types of Neural Networks and Applications: Study deep neural networks by building special types of neural networks for various applications. Explore different neural network architectures, such as recurrent neural networks, and compare them to basic neural networks.
Cost: $690 per credit hour
Advanced Study in Data Science, Syracuse University (Syracuse, NY)
- Advanced Big Data Management: Analyze relational and non-relational databases and corresponding database management system architectures. Learn to build complex database objects to support a variety of needs from big data and traditional perspectives. Explore data systems performance, scalability and security.
- Information Visualization: Get a broad introduction to data visualization. You'll develop a portfolio of resources, demonstrations, recipes and examples of various data visualization techniques.
Cost: Requires completion of 15 credits at $1,782 per credit.
Statistical and Computational Data Science, University of Massachusetts at Amherst (Amherst, MA)
- Data Visualization and Exploration: Learn the fundamental algorithmic and design principles of visualizing and exploring complex data. The course covers multiple aspects of data presentation including human perception and design theory as well as algorithms for exploring patterns in data such as topic modeling, clustering and dimensionality reduction.
- Advanced Natural Language Processing: This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals. After completing the course, you should be able to read and evaluate current NLP research papers.
Cost: The estimated total tuition of the online program is $5,400.
Data Analytics, Cornell University (Ithaca, NY)
- Understanding and Visualizing Data: Develop a working familiarity with the grounding principles of data analysis. Learn to derive the greatest benefit possible from the data available while ensuring you develop valid conclusions.
- Using Predictive Data Analysis: Learn to identify uncertainty in a business decision and how to choose variables that help reduce uncertainty. By the end of this course, you will have a decision model that you can use to make predictions related to your decision.