Integrated Data Science (Certificate)
The past decade has witnessed an accelerating growth in the volume and complexity of data in many data-enabled science and engineering (DESE) fields. To maximize the discovery potential, we must employ advanced data analytics methods and algorithms, visualization techniques, and high-performance computing. We are faced with unprecedented and multi-faceted challenges making skills in advanced data analytics most critical: statistics, data mining, machine learning, signal/image processing and visualization, data management and programming are becoming essential for many areas of science and engineering research. These skills bridge several disciplines and push research frontiers: from the methods disciplines of computer science, electrical engineering, applied math, and statistics to domain disciplines across science and engineering. Furthermore, the non-academic professional sector already has high demands for data scientists and engineers with PhD/MS education. They are being recruited as high-level staff at national research labs and centers, as “data wizards” at non-profit organizations, from within the financial sector, and at many industries including online retailers, social media, healthcare, and pharmaceuticals. These professional sectors are looking for data-analytics experts who can not only answer questions quantitatively, but pose the questions no one has yet identified; the same technical skills needed for data-enabled science and engineering research are in great demand in the growing number of data intensive industries as well.
The certificate in Integrated Data Science aims to organize and recognize students in this area, integrating curriculum in methods and domain disciplines and offering students the wide range of education they need to succeed as data scientists inside and outside of academia. The graduate certificate curriculum aligns well with the Northwestern Data Science Initiative and allows for further expansion, as other units across the university develop and add courses to the curriculum.
How to apply
Enrolled PhD students in The Graduate School may pursue this certificate with the permission of their program. In order to petition to have a Graduate Certificate awarded and appear on the transcript, students must submit the Application for a Graduate Certificate once all Graduate Certificate requirements have been completed, but no later than the time that the student files for graduation (in the final quarter of study).
Who to contact
Please contact the program director, listed below, with questions about this program. Or, explore the Integrated Data-Driven Discovery in Earth and Astrophysical Sciences (IDEAS) program website for more information.
The following requirements are in addition to, or further elaborate upon, those requirements outlined in The Graduate School Policy Guide.
To complete the Integrated Data Science (IDS) Certificate requirements, students will take five courses including at least one course from group A, at least two courses from group B, at least one course from group C, and a fifth course from any group. The courses currently available in each curriculum group are described below, developed in connection to the NSF IDEAS traineeship.
Group A. Data Challenges in Domain Disciplines
|DATA_SCI 401-1||Data-Driven Research in Physics, Geophysics, and Astronomy|
|DATA_SCI 401-2||Data-Driven Research in Physics, Geophysics, and Astronomy|
|BIOL_SCI 354-0||Quantitative Analysis of Biology|
Group B. Core Data Analytics
|DATA_SCI 421-0/PHYSICS 441-0||Statistical Methods for Physicists and Astronomers|
|DATA_SCI 422-0/EARTH 353-0||Mathematical Inverse Methods in Earth and Environmental Sciences|
|DATA_SCI 423-0/ELEC_ENG 475-0||Machine Learning: Foundations, Applications, and Algorithms|
Group C. Electives in Data Analytics
|CHEM_ENG 379-0||Computational Biology: Analysis and Design of Living Systems|
|COMP_ENG 495-0||Special Topics in Computer Engineering|
|COMP_SCI 336-0||Design & Analysis of Algorithms|
|COMP_SCI 496-0||Special Topics in Computer Science|
|EARTH 327-0||Geophysical Time Series Analysis|
|ELEC_ENG 420-0||Digital Image Processing|
|ELEC_ENG 435-0||Deep Learning: Foundations, Applications, and Algorithms|
|ELEC_ENG 495-0||Special Topics in Electrical Engineering|
|ELEC_ENG 433-0||Statistical Pattern Recognition|
|ELEC_ENG 473-0||Deep Reinforcement Learning|
|ES_APPM 421-1||Models in Applied Mathematics|
|ES_APPM 448-0||Numerical Methods for Random Processes|
|IEMS 304-0||Statistical Learning for Data Analysis|
|MAT_SCI 458-0||Atomic Scale Computational Materials Science|
|STAT 320-1||Statistical Theory & Methods 1|
|STAT 350-0||Regression Analysis|
|STAT 365-0||Introduction to the Analysis of Financial Data|
|STAT 454-0||Time Series Analysis|
|STAT 457-0||Applied Bayesian Inference|
|STAT 461-0||Advanced Topics in Statistics|
Please note Machine Learning (COMP_SCI 349-0) will still count as an elective if taken before spring 2020. Data Management and Information Processing (COMP_SCI 317-0) is no longer offered; however it will still count as an elective if taken before Fall 2019.