Master of Science in Applied Data Science / Practical Data Engineering

Practical Data Engineering

Course Overview

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.

Units

3 Units

Course Number

ADS 507

Frequently Asked Questions

Do I need a master’s degree to become a computer scientist?

In many cases, yes — an advanced degree in computer science, data science, or a related field is required. Some positions even required a Ph.D.

Are certifications required to become a computer scientist?

Certifications are often highly recommended and may be required. Make sure you have the appropriate certifications, education, and experience before applying to a specific job.

How much does a computer scientist make?

The median annual salary for a computer scientist is $131,490, but the total compensation package will depend on many factors, including the company, the job itself, and how much education and experience is required.

Where can I find a job as a computer scientist?

The short answer — everywhere! Since technology is valuable in every industry, you’ll find computer scientist positions in banking and finance, the government, retail, sports, entertainment, health care, education, and more.

Do I need an undergraduate degree in science or engineering?

It is expected that most applicants will have an undergraduate degree in science, mathematics, engineering, information technology, computer science or a STEM field. However, the program is also open to those with a bachelor’s degree from other fields (such as business, for example); such applicants are asked to provide a written statement on how their skills and experience are suited to the program.

I’m new to data science, looking to change careers — will this degree help?

The USD MS-ADS program is extremely well-suited to professionals who hold advanced degrees in other disciplines and are looking to develop helpful expertise in data science.

Do I need an undergraduate degree in science or engineering?

It is expected that most applicants will have an undergraduate degree in science, mathematics, engineering, information technology, computer science or a STEM field. However, the program is also open to those with a bachelor’s degree from other fields (such as business, for example); such applicants are asked to provide a written statement on how their skills and experience are suited to the program.

How much does the online data science master’s degree cost in tuition?

The Master of Science in Applied Data Science online program is 36 total credits at $925 per credit, for a total tuition of $33,300. However, this includes two prerequisite classes that may be waived for some students based on past experience. (those who qualify will complete 30 credits; total tuition: $27,750). Thanks to available federal financial aid, most students find the program to be a valuable, affordable investment that quickly pays for itself.

I’m a skilled data analyst who would like to advance, will this degree help?

This program is uniquely tailored to meet the needs of data analysts who are looking to advance to a data science role at their current organization or elsewhere.

Does USD offer career development/advising support?

The University of San Diego is committed to steering you on a path toward educational and professional success. Right from the start, you’ll receive support and guidance from experienced University of San Diego advisors who are invested in helping you achieve your education goals!

Is a GRE or GMAT required for admission?

Candidates who have a GPA above 2.5 are not required to take a GRE or GMAT; however, a standardized test such as GRE or GMAT will be required for applicants with a GPA between 2.0 and 2.5.

What is data science?

Now driving innovation and change across nearly all industries, data science is focused on uncovering valuable insights hidden in the vast amounts of data that humans and their machines now produce every single day.

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