Motorola Solutions Foundation Scholars Program

IMPORTANT INFORMATION: Applications for Motorola Solutions Foundation Scholars Program has been extended till 30th November 2021 11:59 PM.

Important Dates

October 8, 2021: Application Submission Opens
30, 2021: 
Application Submission Deadline
January 3, 2022:Motorola Solutions Foundation Scholars Program Orientation (First Day of the Program)
December 31, 2022:Motorola Solutions Foundation Scholars Program Last Day

*Decision regarding applicant status: whether accepted, not accepted, or pending.

**Once an applicant has been accepted to the program, a response from the applicant, either accept or decline, must be received within one week of the date of the offer letter. 

Support for Each Student Participant

  • Mentoring by a designated DePaul Data Science faculty and opportunities to interact with Motorola Solutions professionals
  • Research training in an applied field of data science including Biomedical & Materials Informatics, Deep Learning with Applications to Biomedical Data, and Transportation Analytics
  • Experience in a collaborative research environment
  • Opportunities for internships with Motorola in the summer of 2022
  • Stipend of $4,500 for 12 months

Program Requirements

  • Full-time undergraduate student with at least 1 year of college/university course work completed as of January 1, 2022
  • Students should meet both of the criteria below:
    • Students should have taken both of the following:

An introduction to problem solving, algorithms, and structured programming using a higher-level programming language. The course will focus on skills for developing algorithms, and for writing and debugging programs. Students will learn how and when to use loops, conditionals, and functional abstractions in the context of problems motivated by real world applications. MAT 130 or above or equivalent or Mathematics Diagnostic test placement into MAT 140 is the prerequisite for this class.
An intermediate course in problem solving, algorithms and programming. Programming skills are further strengthened through more complex and larger programming assignments. The assignments will also be used to introduce different Computer Science areas (e.g. a Client/Server application for the Distributed Systems area). Classes and object oriented programming are motivated and introduced. CSC 241 is the prerequisite for this class.
  • Students should have taken at least one of the following:

The course is an introduction to the Data Mining (DM) stages and its methodologies. The course provides students with an overview of the relationship between data warehousing and DM, and also covers the differences between database query tools and DM. Possible DM methodologies to be covered in the course include: multiple linear regression, clustering, k-nearest neighbor, decision trees, and multidimensional scaling. These methodologies will be augmented with real world examples from different domains such as marketing, e-commerce, and information systems. If time permits, additional topics may include privacy and security issues in data mining. The emphasis of this course is on methodologies and applications, not on their mathematical foundations. IT 223 (or MAT 137 or MAT 242 or MAT 341 or MAT 353) is a prerequisite for this class.
(FORMERLY CSC 323) Application of statistical concepts and techniques to a variety of problems in IT areas and other disciplines, using a statistical package for simple data analysis. Course topics include descriptive statistics, elementary probability rules, sampling, distributions, confidence intervals, correlation, regression and hypothesis testing. PREREQUISITE(S): MAT 130 or placement MAT 130 or above or equivalent or Mathematics Diagnostic test placement into MAT 140 is the prerequisite for this class.
Components of an image processing system and its applications, elements of visual perception, sampling and quantization, image enhancement by histogram equalization, color spaces and transformations, introduction to segmentation (edge detection algorithms), and morphological image processing. PREREQUISITE(S): MAT 150 or MAT 262 MAT 150 or MAT 262 are prerequisites for this class
Topics include multiple regression and correlation methods, model building and validation processes, analysis of variance, logistic regression, and regularized regression techniques. IT 223 or MAT 351 or MAT 137 is a prerequisite for this course.