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DSFE Frequently Asked Questions

Some of the most frequently asked questions about the Data Science for Engineers series include:

  • Who is this series meant for?

    The courses are designed for engineers of many backgrounds and will be relevant to anyone with an understanding of basic mathematics currently working in process-intensive technical fields. The courses are especially ideal for individuals who are seeking to move from a role “on the factory floor” to a data analysis or strategic decision-making position. The combination of domain area expertise with hands on-experience in data analysis and programming will make you competitive for data-centric roles.

    Companies may find this course an affordable alternative to paying an external consultant to help jumpstart the application of data science with their organizations.

  • Why is this tied to UW ChemE? Is it centered around chemical engineering?

    The series was developed in partnership with the UW Chemical Engineering department and is equivalent to ChemE courses 545 & 546. The courses were developed by data scientists from ChemE as part of an interdisciplinary curriculum for engineering and science students that was funded by the National Science Foundation (NSF Research Traineeship program – NRT). The program has trained hundreds of students from BioE, MSE, EE, Chemistry, Molecular Engineering, ME, Urban Planning, and Civil Engineering.

  • What kind of data should I bring? Do I have to bring data?

    If you incorporate your own dataset from your current job when taking the whole series, the final project deliverables should be immediately relevant to you in your current job. You do not have to bring your own data; however, and can choose to work instead with a data set provided by the instructor. Either way, you will leave with a practical set of data science skills, which are extremely marketable across a broad range of engineering disciplines.

  • What should I do if my manager is concerned about the privacy of our company’s data?

    Data brought to the program will not be shared with others in the course, though may be visible if shared with the instructor during coding review and project work. Instructors will maintain strict confidentiality and if required, are permitted to sign a confidentiality agreement to facilitate work on a sensitive project. Please contact if this is required by your company and be sure to allow a few weeks for processing and approval within the UW.

  • Do I need to take all three courses or have previous coding experience in Python?

    Knowledge of Python does allow you to skip the first course in the series. That said, Course 1 also introduces basic concepts of machine learning as well as NumPy/Pandas problems that are particularly useful for data analysis. Case study problems from course 1 are continued into courses 2 and 3, but you should have no issue jumping into Course 2.

  • Does my company have to pay for the course?

    Individuals can register and pay for courses themselves. However, we believe these courses create value for your organization as well and the hope is that depending on your situation your organization would be open to sponsoring your participation in this series. You may want to check your HR benefits for available professional development funding or to see if credit-bearing courses are subsidized by your company. We are happy to provide your company an invoice for your participation.

  • How will this course series help me in my current job?

    Upon completion of this course you will obtain a final project deliverable that is centered around the dataset you bring from your current job. You will also leave with 21 digital interactive chapters containing examples, texts, and code blocks that you can use in your current role and beyond. These chapters should aid in any other data science courses you may take in the future. At the conclusion of the series, participants will be able to confidently manage complex sets of data and use them to predict and model processes, automate tasks, reduce downtime, improve margin velocity, enable line and product level consolidation, reduce changeover, and other means of optimization that have a direct impact on your company’s bottom line.

  • What are the career prospects after this certificate?

    This course series provides a way to quickly upskill in preparation for a role that requires the application of data analysis methods. You will be highly competitive for data analyst/scientist roles that require subject domain area expertise.

  • How is this different than Coursera, LinkedIn Learning offerings, or other online courses that cover data science or Python?

    Our courses are fully synchronous. Dynamic faculty instructors and guest speakers ensure the course is fully interactive and tailored to the interests and needs of attendees. We realize that our students get the best learning experience when they bring data from their own work, are able to tackle real problems, and can learn from and with colleagues at other companies.

    We offer an intensive introduction to Python (Course 1: Build Your Base) that is customized to the interests of engineers (and much less monotonous than an online tutorial.!) but there are plenty of Python bootcamps you could choose to take online instead. If you are interested in building on your existing knowledge, Course 2: Model Your Process and Course 3: Leverage Your Model provide the opportunity for you to learn from relevant case studies and apply what you have learned to your own data or provided datasets, so that you can confidently analyze, correlate, and visualize your data using modern data science tools. Instructors and teaching assistants provide training and real-time feedback. Consider our courses your company’s affordable alternative to a consultant who can help you jumpstart the application of data science methods right away!

DSFE Course Credit Information


Participants in the full Data Science for Engineers (DSFE) series can receive 6 transcriptable credits from the University of Washington either as a nonmatriculated (NM) or graduate nonmatriculated (GNM) student. The registration and application process will differ depending on which student status you choose. It is recommended to register as a NM student unless you intend to apply your credits towards a specific graduate degree.

Please note that the costs are slightly different when you are taking the course for credit. Rather than paying the posted GIX PLP rates, you will pay $2,667 for each UW course (CHEM E 545 and CHEME 546), as well as a $55 registration fee and a $33 tech fee. There is a one-time application fee of $75 for GNM student applications. In total you will pay about 75% of the PLP listed price. This is equivalent to the 25% discount on PLP courses that is offered to all current UW staff and students. If you have any questions about the registration process or need assistance, please contact GIX Professional Learning Programs.




Reserve your spot in the Data Science For Engineers series and indicate your interest in credit.


GIX will send you department-approved registration forms so you can register as a nonmatriculated student. Scan and email the completed forms to AND

Full payment (or a purchase order or letter authorizing third-party payment) is due at the time of registration. If you’d like to pay by credit card, do not include your credit card information in your e-mail. Instead, please call 206-543-2310 with the information. Click here to learn about payment options.


Attend the course and complete all required work with a passing grade to receive credits.  Please note there may be additional project work outside of class for the credit option.



Students interested in applying credits towards a specific graduate degree should apply for GNM student status and follow all UW application deadlines.

Start by reserving your spot in the Data Science For Engineers series, email with your intent to register for GNM status, and follow the application process online here.