Contact Us: [email protected]
M.S. in Applied Data Science
As an online student, most of your interactions with the university will be done through various websites. For example:
Our number one priority is you! Our team has prepared a checklist of items that will set you up for success and clarify all action items as a new student. After you have enrolled for your first term, please complete and review all of the following before classes start.
This webinar will go over a comprehensive look at your program and what to expect as an online learner including resources and tips for success. Each webinar should last around 30-40 minutes. Please RSVP for your Welcome Webinar as soon as possible.
In this call, you’ll “meet” a member of your Student Success Team for your program. Once you attend the welcome webinar, you will be given the link to schedule your new student check-in call. This will be a chance for us to answer any additional questions you have before you start your first term. Please be sure to have any Blackboard-related, program-related, or finance-related questions prepared.
Once you have been registered in your classes, you will be able to access your New Student Orientation Course on Canvas within 24 hours. When accessing Canvas, please make sure to use Firefox or Chrome as your browser.
The New Student Orientation course is designed to help you navigate your way around the course’s layout prior to beginning your first class. You will learn where to find the syllabus, course schedule, assignments, and the discussion board.
Your Orientation is mandatory, and must be completed before the first day of class—so we encourage you to get started! Please plan to spend about 8-10 hours completing the Orientation course. You can move through the Orientation at your own pace, so schedule your time accordingly.
Looking for assistance?
We recommend that students start this planning early as some funding sources can take some time to process. Tuition payments should be completed in full by the first day of the semester. Visit the “Tuition & Payment Methods” section for more information.
Please make sure to review your student handbook prior to the first day of class, and reference it as needed throughout your program. The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
2023 Dates: Monday, July 24 – Wednesday, July 26, 2023
The Graduate Student Success Program is an optional opportunity for students to unpack the hidden curriculum of graduate school, connect with other graduate students across the university, and feel empowered to step into the new identity of being a graduate student and scholar. Through workshops and presentations, our virtual program will address questions such as:
Below is a list of significant dates regarding the registration process, payment deadlines, and other important academic and program deadlines.
Summer 2023 Dates & Deadlines
|Application Deadline||April 10, 2023|
|Registration Opens||March 6, 2023|
|Registration Deadline||April 21, 2023|
|Orientation Course Due Date||May 5, 2023|
|Last Day to Drop with 100% Refund||May 8, 2023|
|Payment Due Date||May 9, 2023|
|Semester Begins||May 9, 2023|
|First Course Start Date||May 9, 2023|
|Last Day to Drop with 95% Refund/ Drop Deadline||May 12, 2023|
|Last Day to Withdraw from Course A||June 5, 2023|
|First Course End Date||June 26, 2023|
|Second Course Start Date||June 27, 2023|
|First Course Final Grade Submission Due Date||July 11, 2023|
|Last Day to Withdraw from Course B||July 24, 2023|
|Second Course End Date||August 14, 2023|
|Semester Ends||August 14, 2023|
|Second Course Final Grade Submission Due Date||August 28, 2023|
Fall 2023 Dates & Deadlines
|Application Deadline||August 1, 2023|
|Registration Opens||July 5, 2023|
|Registration Deadline||August 14, 2023|
|Orientation Course Due Date||September 1, 2023|
|Last Day to Drop with 100% Refund||September 4, 2023|
|Payment Due Date||September 5, 2023|
|Semester Begins||September 5, 2023|
|First Course Start Date||September 5, 2023|
|Last Day to Drop with 95% Refund/ Drop Deadline||September 8, 2023|
|Last Day to Withdraw from Course A||October 2, 2023|
|First Course End Date||October 23, 2023|
|Second Course Start Date||October 24, 2023|
|First Course Final Grade Submission Due Date||November 6, 2023|
|Last Day to Withdraw from Course B||November 20, 2023|
|Second Course End Date||December 11, 2023|
|Semester Ends||December 11, 2023|
|Second Course Final Grade Submission Due Date||December 25, 2023|
Spring 2024 Dates & Deadlines
|Application Deadline||December 1, 2023|
|Registration Opens||November 1, 2023|
|Registration Deadline||December 15, 2023|
|Orientation Course Due Date||January 5, 2024|
|Last Day to Drop with 100% Refund||January 8, 2024|
|Payment Due Date||January 9, 2024|
|Semester Begins||January 9, 2024|
|First Course Start Date||January 9, 2024|
|Last Day to Drop with 95% Refund/ Drop Deadline||January 12, 2024|
|Last Day to Withdraw from Course A||February 5, 2024|
|First Course End Date||February 26, 2024|
|Second Course Start Date||February 27, 2024|
|First Course Final Grade Submission Due Date||March 11, 2024|
|Last Day to Withdraw from Course B||March 25, 2024|
|Second Course End Date||April 15, 2024|
|Semester Ends||April 15, 2024|
|Second Course Final Grade Submission Due Date||April 29, 2024|
You have most likely already filled out an Enrollment Agreement, which enables our team to register you for classes each term. No further action is required on your part.
If you are not able to register for both of your courses in a given term, please contact your program coordinator immediately. This often happens for students who need to take a leave of absence.
Students are required to have their textbooks on hand by the first day of class. Unless otherwise specified, students may select any vendor they prefer (such as Amazon.com, Half.com, Alibris.com, etc.) to purchase their course materials. In the event a specific vendor is required, it will be specified in the course materials list. The best way to ensure that you have the correct book is to search by the ISBN number(s) listed on the book list.
Physical copies of books are not on hand at the USD Torero Store. The USD Torero online store does offer price comparisons for different online vendors for some books.
Although all textbooks for all courses are listed, students only need to purchase the items for the classes they are taking for the semester.
If your course is indicated to have a “Digital Inclusive Access” textbook, you do have the option to use the integrated Vitalsource e-textbook without needing to purchase a textbook through an outside vendor. For more information, view the “Digital Inclusive Access” FAQs document.
Tuition at USD is billed per semester, not per course. Payment (or enrollment in an official USD payment plan) is always due by the first day of the semester. Students may not carry balances from one semester to the next.
Accounts with outstanding balances after the official payment due date may be subject to course cancellations/removal or a student account hold during the semester; related holds can prevent upcoming registration, graduation, or obtaining transcripts.
Remember: tuition is always due by the first day of each semester.
Once you have been registered for your courses, your student account will reflect the appropriate tuition costs according to your program. Your program’s tuition is the following:
Students who need to re-take or withdraw from a course may need to pay additional fees according to the Refund/Drop Deadline policies listed in your Student Handbook.
If you have any questions about your Student Account, please reach out to the One Stop Center via email at [email protected] or phone at 619-260-2700. All costs and fees are subject to change and are based on the academic year of enrollment.
Students will be registered for their prescribed courses each semester. All courses must be dropped prior to the first day of the semester to receive a 100% tuition refund and within the first three days of the start date of the semester to receive a 95% tuition refund. No refund (reversal of tuition) will be provided after the third day of the semester for any class.
You can track your progress toward earning your degree using the Degree Works feature in your MySanDiego student portal. Degree Works shows you which courses you have completed, grades, cumulative GPA, any outstanding graduation requirements, and more!
To access Degree Works:
Submitting your petition to graduate is a requirement for every student. About a semester before your final term, you will be reminded by your Program Coordinator to submit your petition to graduate. Once completed, your Academic Coordinator will review your academic record and contact you if there are any outstanding requirements or issues.
If you are planning on participating in the commencement ceremony (which means walking in your cap and gown here on campus), you will be invited to come to the University of San Diego in the month of May to participate in the ceremony. Commencement details and information will be sent from your Student Success Team around the month of February. Please note, there is only one commencement ceremony each academic year. Fall graduates will be invited to the commencement ceremony the May after they graduate, while Summer graduates will be invited to the commencement ceremony the May before they graduate.
The registrar will process their final audit of the degrees 6-8 weeks after grades are posted for your final semester. Once the degree is conferred in the system, the Registrar will order your diploma from the vendor and the vendor will send it to you directly to the address that was listed on your petition to graduate. Mailing time is an additional 6-8 weeks from the date of order, and you will likely receive your diploma in the mail in 3-4 months after you have completed your degree requirements. *Please note, if your mailing address changes after you submit your Petition to Graduate, please notify the Graduate Records office at (619) 260-2217 or [email protected].
The 30-unit program will consist of ten courses. Courses will be offered year-round with three semesters every year: Spring, Summer, and Fall. Each semester will last 14 weeks. Students will take two courses per semester. Courses will run for seven weeks each with a one- or two-week break in between semesters. This intensive format will allow students to focus on one course at a time and to still complete the degree program in 20 months.
This course is an introduction to probability and statistical concepts and their applications in solving real-world problems. This prerequisite course provides a solid background in the application of probability and statistics that will form the basis for advanced data science methods. Statistical concepts, probability theory, random and multivariate variables, data and sampling distributions, descriptive statistics, and hypothesis testing will be covered. The use of computer-based applications for the performance of basic statistics will be utilized. Covered topics include the numerical and graphical description of data, elements of probability, sampling distributions, probability distribution functions, estimation of population parameters, and hypothesis tests. This course will combine the learnings from texts, case studies, and standard organizational processes with practical problem-solving skills to present, structure, and plan the problem as it would be presented in large enterprises, and execute the steps in a structured analytics process.
This course is an introduction to fundamental concepts of programming and problem-solving techniques for data science. Python and R are the languages used to analyze and deliver insights from real-world datasets. Topics include the basics of Python and R, data acquisition, integration and transformation, problem understanding, data preparation, standardization, and exploratory data analysis. In addition, command line tools and editors are explored in UNIX, and methods to access and analyze RDBMS databases are examined. The course ends with introducing students to the basics of machine learning models.
This course covers an introduction into the methods, concepts, and ethical considerations found and practiced in the field of professional data science. Topics include defining and structuring the problem, managing the business, the CRISP-DM and Agile processes, ensuring the science in data science using the scientific method, project management, managing ethical concerns and model bias, and the importance of performing exploratory data analysis. This course will combine the learnings from case studies, texts, and standard organizational processes with practical problem-solving skills to present, structure, plan, and present the problem as it would be done in large enterprises, including executing steps in the data science work-stream.
Data Mining is one of the most important topics in the data science field. This course discusses theoretical concepts and practical algorithms for both supervised and unsupervised learning techniques. The course provides data mining principles, methods, and applications with a variety of integrated theoretical and practical examples in classification, association analysis, cluster analysis, and anomaly detection. This course also includes applied examples associated with each topic in data mining using R and Python languages.
This course provides a working knowledge of applied predictive modeling. Students will obtain a broad understanding of model training and development procedures, applications to real-world business problems, the differences between predictive model types and uses, and introduce best practices to manage data science projects, create web applications, and present results to technical and non-technical audiences. Course topics include linear and non-linear regression modeling methods, linear and nonlinear classification modeling methods, model selection, variable importance, variable selection and model applications, code and R package management, and web application development using RStudio and R Shiny.
Study of supervised and unsupervised algorithms for machine learning. Emphasis on formulating, choosing, applying, implementing, evaluating machine learning models to capture key patterns exhibited in cross-sectional data, time-series data, and longitudinal data. Considerations of model complexity, results interpretations, and implementation in real-world applications using Python and associated packages.
Data science skills are in high demand across a wide variety of industries. This course focuses on real-world use cases of data mining applications, including predicting consumer purchase behavior, brand loyalty, product prices, sales up-lift, basis of purchase, direct marketing campaign cost-effectiveness, rideshare cancellations, competitive online auctions, recommendation engines, and segmenting and identifying important customers. This course covers practical, business-oriented examples and use cases associated with each topic in data mining using both R and Python languages. Data visualization, effective data storytelling, and analytical communication are being taught. Tableau, as one of the most popular business analytics and dashboard tools, is practiced in this course.
Many datasets naturally have a time series component: records collected over time, financial data, biological data signals such as brain waves or blood glucose levels, weather, and seasonal information. Practicing data scientists need to identify when they encounter time series data when to apply suitable techniques. This course will cover the major topics in time series analysis and forecasting (prediction) including stationary and non-stationary models, autoregressive and integrated autoregressive models, models for estimation, and spectral analysis. Different methods of estimation will be leveraged including maximum likelihood, Bayesian, and spectral estimation. These approaches will be applied to real-world datasets, culminating in a complete analysis from end-to-end.
In this course, students will learn about the discipline of data engineering and how it relates to the field of data science. Topics will include data architecture, data warehousing, SQL, data pipelines, ETL, streaming, security, and software engineering best practices.
This course covers the fundamental concepts of cloud computing as it impacts the field of data science. Course topics include cloud economics, distributed storage, SageMaker ecosystem, distributed processing, model tuning, natural language processing, and model deployment considerations in the cloud. This course will combine the learnings from texts and relevant technical articles, with practical hands-on skills to design, implement, and recommend solutions for the business problem as it would be presented in the business world, and execute the steps in a structured model development process.
This course focuses on natural language processing and data mining of text using Python and R. Topics include collecting and preparing text data, linguistic feature engineering, comparisons of groups of text, building classification models, sentiment analysis, topic modeling, and an introduction to advanced deep learning models.
The Master’s level capstone includes a comprehensive, in-depth data science project implementation in two individual courses to provide students with an opportunity to collect, process, and apply various data science techniques and tools to address analytic real-world interdisciplinary problems. The team-based, collaborative project will be conducted to practice cases similar to analytic projects in industry, government, nonprofit organizations, and academic research. Teams will submit a consolidated report and provide technical presentations to the project advisors on the entire project process.
This list is helpful resources that will set you up for success. Haven’t written in APA formatting since your undergraduate program? We’ve got you covered! Want to know what type of computer you will need? No problem. We have listed helpful resources below.
This program will utilize a variety of technologies and multimedia.
To complete the activities in your courses and to access course content, please verify that you have the following technologies and plug-ins available:
All writing assignments must be formatted according to APA standards. Discussion posts must contain the appropriate APA citations. If you want additional writing support, we recommend Purdue Online Writing Lab ([email protected]). In addition to general writing support, the website includes a special section dedicated to APA formatting guidelines.
Another helpful writing resource is the School of Leadership and Education Sciences (SOLES) Graduate Student Writing Center. Enrolled students can submit assignments for review by a writing professional.
Students at the University of San Diego are able to download Microsoft Office 365 for free! If you don’t have it already, you can download the Microsoft Office 365 suite using your USD student email.
TimelyCare is a provider of 24/7, no-cost telehealth services for USD students to address common conditions that can be safely diagnosed and treated remotely. TimelyCare services are available at no cost to the student. Services include:
Each time your course starts, you will be invited to join a Slack messaging channel for your MS-ADS program. In the channel, you’ll be able to easily message your peers and faculty in a dynamic way. New to Slack? No problem – there are a variety of introductory resources to get you up to speed.
The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
USD does not offer subject-specific tutoring resources, so students who are looking for tutoring support are encouraged to identify a tutor using Wyzant.com. Please note that Wyzant is not a USD-managed resource, so use them at your own discretion.
It is the policy of the University of San Diego to adhere to the rules and regulations as announced in this brochure or other University Publications. The University nevertheless hereby gives notice that it reserves the right to expand or delete or otherwise modify this online publication whenever such changes are adjudged by it to be desirable or necessary. Changes will be made periodically as needed.
In your program, you can think of Canvas as your virtual tool to share information with professors and peers. You will use Canvas to access your course content, find course syllabi, review your assignments, and more. Be sure to use your USD login credential to log in. If you have any difficulty logging into your course, be sure to contact ITS at (619) 260-7900 or [email protected].
The concept of netiquette covers proper communication online. Read our guidelines to help cultivate a supportive and productive online environment.
At USD, you join a community of individuals who are all committed to one common goal: your success. As you familiarize yourself with your team, take the opportunity to virtually meet and connect with the resources available to you as a student. Click on the profiles below to learn more about each office or staff member and watch a brief video about their role in supporting you through graduation.
Whether you’re hoping to find a new job or earn a promotion, USD has a wealth of resources available to prepare you for your dream role.
Tuition for the MS-ADS program is $925 per unit.
The MS-ADS program is a total of 30 units.
Students will enroll in two prescribed courses each semester for a total of 6 semester units.
The University of San Diego considers 9 or more units as full-time student status. Your program is designed to be part-time, and students enroll in just 6 units per semester. There is no full-time option for this program.
All students are manually enrolled each semester by the USD Student Success team. If you are not able to enroll for a term or if you need to drop your courses, it is your responsibility to notify your Program Coordinator. All students will be held to the respective drop deadlines and refund schedule detailed in your Student Handbook.
Login to your my.sandiego.edu student portal. Under the “My Student Account” tab, review the tutorials for directions on how to view and pay your bill, set up a payment plan, and enroll in eRefund (Direct Deposit). View the “Tuition & Payment Methods” on your Student Success Center for further details.
Log into your my.sandiego.edu student portal and navigate to the “Torero Hub” section on the sidebar. Click on the “My Academics” tab and locate the “View My Grades” link in the top-middle section. Alternatively, you can view your program progress at a glance using the “Degree Works” link.
If you notice a grade inconsistency between Blackboard and your MySanDiego portal, please email your instructor to verify what the final grade should be. Your instructor has the ability to update the posted grade.
Log into your my.sandiego.edu student portal and then use the “Degree Works” link to view your degree audit.
You can find the “Degree Works” link in the Torero Hub under the “My Academics” page. If you are interested in requesting a tailored degree plan, please email [email protected]
If you need to take time off from your program, please email your Program Coordinator or the Student Success team at [email protected]. Since you have submitted your enrollment commitment, our team will automatically register you in courses each term unless you have previously notified the team about taking a break.
To order your official, unofficial, or e-transcript(s), view the transcript ordering options page. Otherwise, you can view unofficial/order official transcripts through your MySanDiego portal. Under the “Torero Hub” sidebar option, click on the “My Academics” page, then click on “Request Official Transcript” under the “My Classes” section.
Congratulations on finishing your program! Diplomas are mailed about 6-8 weeks after the degree requirements have been met and processed. Diplomas are mailed to the current address on file at the time degree requirements are completed. (To check your address information, login to your my.sandiego.edu student portal and view your personal information under My Torero Services.)
You will first be emailed a copy of your e-diploma from Parchment prior to receiving your mailed physical diploma.
Throughout your program and after graduation, your Student Success team is here to help! We recommend contacting your Program Coordinator directly, but you can also email our team address at [email protected]
In addition to our team, your Academic Director is a great resource!
All writing assignments must be formatted according to APA standards. Discussion posts must contain the appropriate APA citations. If you are unfamiliar with APA formatting, or simply require additional writing support, we recommend referencing the Purdue Online Writing Lab (also called [email protected]). In addition to general writing support, the website includes a special section dedicated to APA formatting guidelines.
To further support your writing, we highly recommend using the School of Leadership and Education Sciences (SOLES) Graduate Student Writing Center. Students are encouraged to submit written course assignments via the digital submission form for online feedback from a professional writing coach. See site for details.
This course moves very quickly, and it is important that you turn in all assignments on or before their due dates. If, because of an emergency, you have missed a week or more of course work, please contact your professor immediately to inform them. While there is no guarantee that you will be allowed to make up your work, informing your professor early is the best way to get back on track and finish your course successfully.
Please do not wait more than a week without informing your professor. If your instructor’s email is not already visible on the Blackboard course, please use the USD directory to find their contact information.