Acquire Valuable Insights From Data Sets at Any Scale
Next start date
October 4, 2016
Businesses and organizations today have ready access to huge amounts of data, but they’re less adept at making sense of it. The role of the data scientist – turning statistical information into meaningful, actionable insights – is increasingly crucial as companies strive to stay ahead of the competition.
In this three-course certificate program, you’ll discover how to apply cutting-edge tools and techniques to extract meaning from data sets ranging in size from gigabytes to petabytes. Use statistics, machine learning, algorithms and other techniques to analyze real-life data scenarios to make informed business decisions. Take your data analytics abilities, and your career, to the next level.
Technically oriented professionals who work with data and have some background in statistics, SQL and programming.
Applicants must earn a total score of at least 18/30 on the data science qualifications assessment quiz and have some background in statistics, programming and SQL, including:
• At least one year of experience using a programming language, preferably Python, Java, R or C#
• Fundamental understanding of databases and SQL
• Basic conceptual knowledge and familiarity with statistics and linear algebra concepts such as vectors, matrices, eigenvectors and eigenvalues, simple matrix calculus (derivatives), distributions, quintiles, average and standard deviation as measures of distribution, and the concept of uncertain estimates of quantities as embodied by confidence limits or error bars
To participate in this program, you’ll need to have at least basic English language skills. To learn more, see English Language Proficiency Requirements.
International students are welcome to apply to an online offering of this program, which doesn’t require a visa. To enroll in a classroom offering, you must have a visa that permits study in the United States. This certificate program does not enable students to obtain or maintain F-1 visa status. For more information, see Admission Requirements for International Students.
You must have access to a computer with the ability to run VMware or VirtualBox, with a minimum 4GB of RAM. Classroom students must bring laptops or an equivalent device that has remote access to a computer. A high-speed Internet connection is recommended.
You earn the certificate by regularly attending class and successfully completing all required courses. For more information, see Completing Your Program.
At the end of the program, you’ll solve a real data science problem through an online competition.
Complete the courses listed below to earn the certificate. You may be able to take individual courses without enrolling in the certificate program; check the course pages for details.
Introduction to Data Science
Methods for Data Analysis
Deriving Knowledge from Data at Scale
Certificate in Data Science
Approved by the UW Department of Computer Science & Engineering and developed under the guidance of the eScience Institute.
View this program's advisory board.
Each of our programs uses an advisory board to review content, guide design and recommend updates to ensure the program remains current as the field of study evolves. By tapping the minds of the top thinkers, doers and leaders in the field, we offer a transformational learning experience. The following individuals serve as the advisory board for this program.
Erik Bansleben, Director, Academic Programs, UW Professional & Continuing Education
Roger Barga, General Manager and Director of Development, Amazon Web Services
Naomi Bogenschutz,Program Manager, UW Professional & Continuing Education
Rovy Branon, Vice Provost, UW Educational Outreach
Jonathan Brown, Catastrophe Risk Analyst, Guy Carpenter
Paul Brown, VP of Software Engineering, Salesforce.com
Vitor Carvalho, Senior Staff Engineer, Qualcomm Research
John Clouse, Chief Data Scientist, The Everett Clinic
Adam Cornille, Senior Data Scientist, Deloitte Digital
Olly Downs, Chief Scientist/CTO, Globys, Inc.
Mario Garzia, Data Science and Big Data Consultant
Jeffrey Heer, Associcate Professor, UW Computer Science & Engineering
Peter Hoff, Professor, UW Department of Statistics
Bill Howe, Senior Scientist, UW eScience Institute
Simon Kahan, President, Biocellion
Aaron Kimball, CTO, Zymergen, Inc
Nathan Kutz, Professor and Chair of Applied Mathematics
Valentin Kuznetsov, Data Scientist and Researcher, Cornell Univeristy
Mike Lazarus, Vice President of Analytics, Atigeo
Bill McNeill, Software Engineer, Intelius
Dev Nambi, Data Scientist, UW Enterprise Data & Analytics Group
Saskia Schott, Senior Instructor, Quickstart Intelligence
Daren Vengroff, Chief Scientist, RichRelevance
Jon Wakefield, Professor, UW Statistics and Biostatistics
Buck Woody, Senior Technical Specialist, Microsoft
Date: Tuesday, August 16, 2016
Time: 12:00 p.m. - 1:00 p.m. Pacific Time
Download the application form (PDF)
Apply online, or submit an application packet that includes:
We’re currently accepting applications and will be reviewing them in the order they’re received. We’ll accept applications until the program closes.
We’ll contact you within two weeks of receiving your application materials to let you know if you’ve been accepted to the program.
If you’re accepted, we’ll send you details about your first-term course, including information on paying your course fee. Your course fee is due two weeks before the first class.
With the exception of the one-time application fee, certificate program fees are charged on a quarterly basis. Payment is due when you register.
Course fees do not include any costs for class materials such as textbooks and software.
Drops, Withdrawals & Refunds
Attend class in person and engage face to face with your classmates and instructors as part of a highly interactive curriculum. Assignments and other course materials are delivered through Canvas, a web-based learning management system that also serves as a communications hub.
You must attend at least 60 percent of your class sessions in person. You may view the remainder online, either as recorded sessions or in real time (limit of two live-streamed sessions per quarter).
Courses are streamed online in real time from the classroom. You interact with your instructors and fellow students via chat, using Adobe Connect web conferencing software. Assignments and other course materials are delivered through Canvas, a web-based learning management system that also serves as a communications hub.
For added flexibility, each quarter you may view recordings of up to 40 percent of your class sessions instead of attending in real time. You also can attend the classroom sessions in person as often as you like.
Our special blended programs combine the convenience of online study with the vibrancy of in-person classes for a unique learning experience.
Not available for this program
Enterprise Architect, Solavei
Managing Director, Quantia Analytics
Principal Data Scientist, Data and Decision Sciences Group, Microsoft
Senior Data Scientist, Zestimate Group, Zillow
Senior Program Manager, Machine Learning & Data Science Team, Microsoft
Explore distributed computing and the practical tools used to store and process data. Gain experience with the kinds of data flow situations commonly used to inform key business decisions.
Explore different aspects of data modeling and database design, and develop the conceptual knowledge and skills to create a midsize data warehouse.
Learn how to gather, store and interpret large-scale data in meaningful ways that enable businesses and organizations make effective, data-driven decisions.
Use the AWS platform to explore key aspects of cloud application architecture and development, and learn to build a scalable cloud service and design always-available applications
Explore and evaluate the different cloud solutions offered by Amazon, Google and Microsoft, and discover how to design, implement and manage databases using the cloud.
Examine the mathematical, statistical and econometric principles that underlie the quantitative management of financial investments.
Explore ways to organize and derive meaning from vast amounts of data by using visual presentation tools and techniques, and develop your ability to make data make sense for everyone.
Acquire a strong foundation in the fundamentals of machine learning, and explore how to use machine learning principles to meet business and user needs.
Gain the deep technical knowledge and interdisciplinary skills needed to turn massive data sets into insights that organizations can use.
Explore the power and potential of natural language processing in a variety of fields, and learn to bridge the gap between people and machines using a variety of NLP applications.
Gain practical skills in the essential elements of functional programming using the Scala programming language. Access diverse data sources using Spark, and learn to build Web applications using Akka.
Learn to use scientific computing tools and technologies to help solve complex problems in the physical, biological and engineering sciences.
Learn to use R programming to develop a thorough understanding of statistical models and computing methods and to problem solve within marketing analytics, Web analytics and other large data-set manipulation.