Apply Anytime

With a self-paced program, there is no application deadline — you can apply and start the program whenever you'd like.

Application Steps

Note: If you are a graduate of the  Certificate in Data Science, or have successfully completed the Data Science: Machine Learning Techniques course prior to Spring 2018, you can just submit an application form and pay the $50 nonrefundable application fee. You do not have to submit the materials listed in Steps 1 and 2.

Step 1: Complete the programming assessment 

Applicants must take our online programming test.

Step 2: Gather the following materials

  • A brief letter (250-word maximum) that describes your relevant experience, transferable skills, knowledge of the field and commitment to professional growth
  • A resume that highlights how your education and any applicable experience fulfill the program's admission requirements

Be sure to include the name used on your programming test in your letter of application.

Step 3: Apply

Complete your application and submit the materials listed in step 2, along with your $50 nonrefundable application fee.

After Applying

We’ll contact you within seven business days of receiving your complete 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 course, including information on paying your course fee. Note: You have six weeks to register for your first course after being accepted. After that, you'll need to reapply to the program before you can register.

Scholarship Information

You may qualify for an income-based scholarship for this program. To learn about eligibility requirements and how to apply, visit the UWPCE Certificate Scholarship page

Related Resources

ADMISSION REQUIREMENTS

Our students are a diverse group of professionals that include statisticians, applied mathematicians, data scientists and experienced programmers. Applicants are evaluated on their skills and background, and everyone is required to take a programming test (see exceptions below).

Admission requirements vary depending on your field. Review the requirements that pertain to you below.

If you are a programmer, software engineer or another kind of engineer:

  • Three years of recent professional programming experience in a high-level language such as C, C++, Java or Python or equivalent personal projects such as in Kaggle
  • Undergraduate mathematics courses covering linear algebra, calculus and probability  
  • Undergraduate courses in statistics (or completion of the Foundations of Statistics course)

If you are a statistician, applied mathematician or data scientist, or have a Ph.D. in another quantitative field:

  • Two years of experience as a statistician, data scientist or applied mathematician OR a Ph.D. in a quantitative field (ABDs — All But Dissertation people  — welcome)
  • Familiarity with programming in a high-level language as demonstrated by the programming assessment

If you don't meet the requirements above, you might consider enrolling in the Certificate in Data Science first. Or you may go ahead and apply to the Certificate in Machine Learning, and you may be given options for fulfilling the requirements before the program begins.

Exceptions to the Requirements Above

If you are a graduate of the Certificate in Data Science program, or you have successfully completed the Data Science: Machine Learning Techniques course, you'll be automatically accepted into the program after you submit your application form and fee. You do not have to take the programming test or submit any additional application materials.

English Proficiency

If you're not a native English speaker, you’ll need to have at least basic English language skills to enroll. To learn more, see English Language Proficiency Requirements.

Technology Requirements

You must have access to a computer, and we recommend a high-speed internet connection.

Earning the Certificate

You earn the certificate by adhering to the program's attendance policy and successfully completing all required courses. For more information, see Earning the Certificate.