Student Success Center
M.S. in Applied Data Science
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Welcome!
As an online student, most of your interactions with the university will be done through various websites. For example:
- You’ll use your USD Email Address for all university-related communications.
- You’ll use Blackboard for all of your coursework and assignments.
- You’ll use your MySanDiego Portal for tuition payments and to access most campus resources.
- You’ll use this site, your Student Success Center, to get specific information about textbook information, deadlines, and more!
(619) 260-2933
C0MPLETE YOUR MANDATORY NEW STUDENT CHECKLIST
Our number one goal is for your success in this program so we have created the following checklist items of everything you need to complete and review before classes start.
1. View Your Mandatory New Student Welcome Webinar
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.
If you’ve already viewed the webinar or are unable to attend any of the listed timeslots, you can view a recording of the Welcome Webinar.
2. Schedule Your New Student Check-in Call
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.
3. Complete Your Blackboard Orientation Course – Due May 6, 2022
Once you have been registered in your classes, you will be able to access your New Student Orientation Course on Blackboard within 24 hours. When accessing Blackboard, 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 Blackboard 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? View the guide for navigating your Blackboard Orientation course.
4. Make Your Tuition Payment Plan
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.
5. Review your Student Handbook
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.
IMPORTANT DATES
Below is a list of significant dates regarding the registration process, payment deadlines, and other important academic and program deadlines.
Spring 2023 Dates & Deadlines
| Important Dates | Date |
|---|---|
| Application Deadline | December 2, 2022 |
| Registration Opens | November 2, 2022 |
| Registration Deadline | December 16, 2022 |
| Orientation Course Due Date | January 6, 2023 |
| Last Day to Drop with 100% Refund | January 9, 2023 |
| Payment Due Date | January 10, 2023 |
| Semester Begins | January 10, 2023 |
| First Course Start Date | January 10, 2023 |
| Last Day to Drop with 95% Refund/ Drop Deadline | January 13, 2023 |
| Last Day to Withdraw from Course A | February 6, 2023 |
| First Course End Date | February 27, 2023 |
| First Course Final Grade Submission Due Date | March 13, 2023 |
| Second Course Start Date | February 28, 2023 |
| Last Day to Withdraw from Course B | March 27, 2023 |
| Second Course End Date | April 17, 2023 |
| Semester Ends | April 17, 2023 |
| Second Course Final Grade Submission Due Date | May 1, 2023 |
Summer 2022 Dates & Deadlines
| Important Dates | Date |
|---|---|
| Registration Opens | March 7, 2022 |
| Registration Deadline | April 22, 2022 |
| Orientation Course Due Date | May 6, 2022 |
| Last Day to Drop with 100% Refund | May 9, 2022 |
| Payment Due Date | May 10, 2022 |
| Semester Begins | May 10, 2022 |
| First Course Start Date | May 10, 2022 |
| Last Day to Drop with 95% Refund/ Drop Deadline | May 13, 2022 |
| First Course End Date | June 27, 2022 |
| First Course Final Grade Submission Due Date | July 11, 2022 |
| Second Course Start Date | June 28, 2022 |
| Second Course End Date | August 15, 2022 |
| Semester Ends | August 15, 2022 |
| Second Course Final Grade Submission Due Date | August 29, 2022 |
Fall 2022 Dates & Deadlines
| Important Dates | Date |
|---|---|
| Registration Opens | July 5, 2022 |
| Registration Deadline | August 15, 2022 |
| Orientation Course Due Date | September 2, 2022 |
| Last Day to Drop with 100% Refund | September 5, 2022 |
| Payment Due Date | September 6, 2022 |
| Semester Begins | September 6, 2022 |
| First Course Start Date | September 6, 2022 |
| Last Day to Drop with 95% Refund/ Drop Deadline | September 9, 2022 |
| Last Day to Withdraw from Course A | October 3, 2022 |
| First Course End Date | October 24, 2022 |
| First Course Final Grade Submission Due Date | November 7, 2022 |
| Second Course Start Date | October 25, 2022 |
| Last Day to Withdraw from Course B | November 21, 2022 |
| Second Course End Date | December 12, 2022 |
| Semester Ends | December 12, 2022 |
| Second Course Final Grade Submission Due Date | December 26, 2022 |
Download a pdf of your academic calendar
Class Registration Directions
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.
Textbook information
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.
PAYing for your program
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.
Program Tuition
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:
- Per Unit (classes are typically 3-units) = $925
- Per Semester (assuming 6-units per semester) = $5,550
- Total Program Cost (including 5 semesters) = $27,750 (without prerequisites)
- Total Program Cost (including 6 semesters) = $33,300 (with prerequisites)
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 onestop@sandiego.edu or phone at 619-260-2700. All costs and fees are subject to change and are based on the academic year of enrollment.
Refund Policy
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.
DEGREE PROGRESS & COMMENCEMENT
DegreeWorks
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:
- Log in to your MySanDiego student portal.
- Under the “Torero Hub” tab, click on “My Academics.”
- On the right-hand side under “My Academic Resources”, click on “Degree Works.”
Petition to Graduate
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.
Commencement and Diploma Details
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 usdgradrec@sandiego.edu.
Course Requirements
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.
Course Titles and Descriptions
Probability and Statistics for Data Science (ADS 500A)
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.
Data Science Programming (ADS 500B)
This course is on fundamental and advanced concepts of programming and problem-solving for data science. Python and R languages are used to solve problems on real-world datasets. Topics include the basics of Python and R, data acquisition, cleansing and transformation, problem understanding and data preparation, standardization, and exploratory data analysis. In addition, this course is on advanced concepts of programming and modeling for data science. Topics include data partitioning, validation, model building with Decision trees, Naïve Bayes classification, Neural networks, clustering and regression, model evaluation, dimensionality reduction, association rules, and generalized linear models.
Foundations of Data Science and Data Ethics (ADS 501)
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 integration of data science in business, the CRISP-DM and Agile processes, “ensuring the science” in data science using the scientific method, managing ethical concerns, and model bias by analyzing case studies, and performing exploratory data analysis with visualizations using BI tools. This course will combine the learnings from case studies, texts, 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 the data science workstream.
Applied Data Mining (ADS 502)
The course covers data exploration and data mining principles, techniques, and applications with a variety of integrated theoretical and practical examples in classification, association analysis, cluster analysis, and anomaly detection. This course also includes a wide variety of applied examples associated with each topic in data mining.
Applied Predictive Modeling (ADS 503)
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 problems in the industry, and the differences between predictive model types and uses. Course topics include linear and non-linear regression modeling methods, linear and non-linear classification modeling methods, model selection, variable importance, variable selection and model applications.
Machine Learning and Deep Learning for Data Science (ADS 504)
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.
Applied Data Science for Business (ADS 505)
This course covers the role Data Science plays in enabling business strategy and competitive advantage. Topics include alignment of data science solutions to business strategy and the fundamental concepts of solving problems in the industry with Data Science. Other topics include data-analytic thinking, problem identification to data and analytics solutioning, segmenting with k-Nearest Neighbor (k-NN), Naive Bayes Classifier, Dimensionality Reduction, Classification, and Regression Trees.
Applied Time Series Analysis (ADS 506)
Many data sets naturally have a time series component: records collected over time, including 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 and apply suitable techniques. This course will cover the major topics in time series analysis and forecasting (prediction). Students will be familiar with the major types of time series models commonly used in industrial practice and apply them to real datasets, culminating in a complete analysis of a nontrivial real-world dataset from end-to-end.
Practical Data Engineering (ADS 507)
This course will cover database table structures, column data types, and the T-SQL language components that go into querying against a database. Reading data from a table, row filtering, and column-based functions with be discussed. We will also write queries that combine data from multiple tables, use conditional logic, and create aggregated result sets (sum, average, min, max). Database administration features of SQL will be discussed. Finally, we will discuss how to create objects in SQL Server such as tables, views, and stored procedures, and how to manipulate data in existing objects using insert, update, and delete statements.
Data Science with Cloud Computing (ADS 508)
This course is on fundamental concepts of cloud computing as it impacts the field of data science. Course topics include cloud economics, distributed storage, MapReduce, Hadoop ecosystem, Apache Spark, Machine Learning with MLLib, natural language processing with Spark, and data governance considerations in the cloud.
Applied Text Mining (ADS 509)
This course focuses on the major applications and techniques used in textual data mining and analyzing using Python and R. Topics include text preprocessing, vectorization and word document frequencies, linguistic feature engineering, topic modeling, text and document classification, sentiment analysis, introduction to advanced deep learning models.
Capstone Project (599)
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.
Student Resources
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.
Technical Requirements
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:
- Computer – A PC or Mac with 16GB of RAM, an i5 or i7 quad-core processor, and 100GB of free drive space is highly recommended.
- Browser Plug-ins – Windows Media Player, QuickTime, Flash, Adobe Reader, and Java.
- Mozilla Firefox is the recommended browser.
- Microsoft Office is required. If you are using Office 2003 or earlier, Download and install the Microsoft Office Compatibility pack.
- Speaker or headset – to listen to multimedia
- Webcam – for recording video (if needed).
- High-speed internet connection strongly encouraged (DSL or better)
Graduate Writing Expectations and Tips
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 (OWL@Purdue). 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.
Download Microsoft Office 365 (Free for USD Students)
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 – Telehealth for USD Students
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:
- Live and on-demand self-care sessions (yoga, breathing, stretching)
- Access to on-demand counselors for emotional health support
- Access to schedule a session with a licensed counselor (up to 12 times per year)
MS-ADS Student Handbook
The handbook is where you can find information on academic expectations, drop and refund policy, technology requirements, curriculum, frequently asked questions, and more.
Student Code of Rights and Responsibilities for Graduate Students
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.
Blackboard (Bb) @ USD
In your program, you can think of Blackboard as your virtual tool to share information with professors and peers. You will use Blackboard 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 help@sandiego.edu.
Netiquette Guidelines
The concept of netiquette covers proper communication online. Read our guidelines to help cultivate a supportive and productive online environment.
your partners toward success
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.
