This repostitory is a path through my graduate journey at Northwestern. I was initally accepted into the Analytics Management Certificate program, but wanted to go deeper into the program. There will be a seperate branch for the specific course, which I will share the projects/content in accordance to course/university policy.
Program overview: https://catalogs.northwestern.edu/sps/courses-az/graduate/msds/
I was waived from MSDS 400, 401, and 476 due to adequate substitute courses. I enrolled in multiple algorithms courses, and earned an A at UIUC's CS 401. Additionally, my statistics background was also waived due to high performance in: Time Series Analysis, Applied Econometrics, and another statistical equivalance of Econometrics from computational side. For MSDS 476, I was waived due to performance in courses regarding: Performance Managements and Business Analytics Introduction (graduate level, simulation intro).
My first class was MSDS 460, which was extremely relevant. I recommend this be taken early on in the program, as it will lay foundation for deeper classes to come. It was an applied linear programming course, touching into heuristics. This should be a pre-requisite for MSDS 422 in my opinion - Dr. Kline is an amazing professor and makes the material interesting, and is very encouraging. The grading was generous.
- In between this course, I spoke with a professor in the PhD program, and realized that there will be a need to go deeper into the theoretical frameworks to truly understand and build original algorithms. I don't believe leet coding will be sufficient, so I am planning on enrolling in a bundle of theoretical courses, similiar to here:
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(UIUC IE Advanced Analytics Certificate) is an additional path I am considering before the Thesis, to get a deeper understanding of the theory. I'm just taking one course at a time.
- Convex Optimization UIUC, Online Option - perhaps touches and refers directly to Dr. Ozdaglar's work.
- Stochastic Processes Course UIUC, Online Option - should provide theoretical understanding of direction I want to go down
- How to get into stochastic analysis - surprisingly really good outline.
- Integer Programming UIUC, Online Option - Introduces concepts relating to topics professor showed me, introduces Combinatorial Optimization and Computational Complexity.
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Also considering the option of the program here: Statistics Graduate Certificate or Artificial Intelligence Graduate Certificate, where I could build a could complimentary of the depth courses in the MSDS program.
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My second course will be MSDS 420. It is a prerequisite for some other courses, and should give a solid introduction for accessing, organizing, and structuring data.
The above image shows the core courses necessary, in addition to MSDS 476. I waived three, so the other five are necessary. The seven electives I am thinking are:
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Analytics Systems Engineering (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=4774)
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Artificial Intelligence and Deep Learning (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=4783) pre-requisite and enabling to deeper into the two below.
- Intelligent Systems and Robotics (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=5012)
- Applied Probability and Simulation Modeling (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=4779)
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Data Pipelines and Stream Processing (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=5289) OR Full Stack Data Engineering (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=5054).
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Analytics Application Engineering Possible 6th course (https://sps.northwestern.edu/masters/data-science/program-courses.php?course_id=5011)
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The seventh course will be most related to understanding edge computing.
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Finally, the last step would be a Thesis Research project, which would be guided by Learning repository throughout the program of applied research and projects.
Refer to my Learning branch to see project applications.