Data Science FAQs | Department of Mathematics | BYU-Idaho

# Data Science FAQs

Data science prepares students for Data Analysis and Technology based careers.

## About the Data Science Major

The data science bachelor's degree prepares students to immediately start a career in the field of data science. This program typically takes 8 semesters to complete and allows students to specialize in the field. Comparatively, an Associate Degree only takes 4 semesters and gives students a general knowledge of data science compared to a more skilled education they'll receive with a bachelor's, but allows students to transfer easily.

There is quite a bit of overlap between the Mathematical sciences degree with an emphasis in statistics (stats) and a data science (ds) degree. Here are the key comparisons: 
 
****Required Paths**** 
The stats degree requires a strong math core and you take 1-2 classes in programming. The data science degree requires strong programming core and no mathematics classes. In both cases, you can use your electives to build up experience. 
 
****Long-term Goals**** 
The data science degree is built to create employability. It helps you get a job with more ease than the stats degree. The stats degree will give you a better chance to get into graduate school in a statistics program. 
 
****Emphasis**** 
The statistics classes required in the data science degree are classes that directly relate to data science. The statistics classes that are required in the stats degree cover a broader spectrum of statistical methods.

If the programming component of CS 241 and Math/CS 335 speaks to you, you might want to consider a degree in software engineering or computer science. The data science major has opportunities for several electives that overlap with these majors (e.g. CS 246, CS 313). There is a huge need for software engineers that understand the data science process, working with the cloud, and the data flow pipeline, so a major in software engineering with a minor in data science could be a powerful combination. 
 
In addition, if you want to understand machine learning algorithms better, you should take CS 450, which introduces the way these work at a deeper level. Then, if you are interested in furthering your education of machine learning, a degree in computer science may be the best fit to prepare you for both graduate school as well as the workforce now.

Data science students will have many opportunities for employment after graduation. Demand for data scientists is high and is expected to grow by 19% over the next 10 years (US Bureau of Labor Statistics). 

Data science jobs are available at small, medium, and large companies and involve a mix of data wrangling, business decision making, and modeling. For the first part of your career, you could expect to do quite a bit of programming with data. Depending on your choices you can move into management, advanced modelling, or continue in data coding. Some entry level Data Science jobs include: 
 
 Big Data Analyst 
 
Database Administrator 
 
Data Architect 
 
Commercial Intelligence Manager 
 
Competitive Intelligence Analyst 
 
Business Intelligence Analyst 
 
Technology Intelligence Specialist

## Data Science Classes

No. Any programming class you have taken previously could potentially be substituted for CS-101. CS124 and CIT160 are already approved substitutions.

- First semester: CIT 111, MATH 221, and FDSCI 212. 
 - Also attend data science society on Wednesdays at 6:00 in STC 361.

- Second semester: MATH 325 and CS 101. 
 - You can start to see what you can do with your degree.

- Third semester: MATH 335 and CS 241 
 - This will solidify your understanding.

It is also important that you take classes in varied domains (e.g. business, sociology, agriculture, hospital management, finance) to figure out what industry you would like to support with your data science skills.

We recommend that freshmen take CS 101, CIT 111, and MATH 221A. 
 
Your second semester should include MATH 325. 
 
Once you take CS 241 in your third semester, those four classes should give you just enough insight to make an educated decision on your major. You should have enough information to pick between the stats, data science, and computer science degrees as your major.

There are a lot of opportunities in data science, some are more involved on the programming-side, others are more focused on the statistics side. Having a good foundation in each area can be very beneficial, but you can certainly specialize in different ways. Note that the Math classes required in the data science major are applied statistics classes. The degree does not require traditional math classes. 
 
You might consider a major in software engineering with a minor in data science. This will give you the basics of statistics and data visualization to go with your computer science skills.

Design thinking is a new way to solve problems creatively. Solutions are created through an organized method of observing and empathizing with the people who are impacted by the problem, generating multiple solutions, prototyping a solution, and testing it. 
 
Design thinking courses focus on teamwork and radical collaboration to work through this method of "enlightened" trial and error. Design thinking courses encourage creativity and prove that everyone is creative, whether they believe it or not.

See the Suggested Courses page.

Depending on your previously completed courses, we would recommend MATH 325 or MATH 335. 
 
If you have previous experience with programming, then MATH 335 would be a good choice. 
 
MATH 325 only requires experience with an introductory statistics class: MATH 221A, MATH 221B, or MATH 221C.

We have long term goals to get the data science degree online. However, this goal won't be reached for at least 5 years.

Some of the required data science classes that are not offered online include: 

- CS 241
- MATH 325
- MATH 425
- MATH 488
- MATH 335
- CS 335
- CS 450
- CIT 425
- CS 237
- CS 450
- MATH 113
- MATH 119
- MATH 214
- MATH 241
- MATH 281
- MATH 326
- MATH 341
- MATH 423
- MATH 424
- DCM 221
- DCM 350.

We have plans for CS 241, MATH 325 and MATH/CS 335 to move online in the next few years.

## Data Science Internships

Data scientists are in high demand, so there are many options for internships. We expect that you could finish 2-3 internships during your studies. You should have the expectation that most of your internships will be paid as well. 
 
Like any other challenge, students should prepare enough time to find an internship. Building a quality resume will be a key element in your search. Students are advised to work with the data science faculty leads to build connections. There are some resources to help students find internship opportunities like LinkedIn, Indeed, Glassdoor, and the BYU-Idaho Career Center.

You will not have a hard time finding an internship. Take care to find an internship in the field/industry you are interested in. You want to find an internship that lets you develop your data analyzing skills and that allows you to program in R and/or Python.

## Data Science and Graduate School

The data science degree is built to provide the optimal mix of skills to get students employment upon graduation. The data science degree would be highly regarded in any social science graduate program and could be a plus in business and medical graduate programs. In all the above cases, you would need to make sure to complete the necessary prerequisite classes for the graduate program in which you are interested. 
 
If you would like to go to graduate school in mathematics, statistics, and possibly economics you would need to use most of your elective courses to strengthen your mathematics background. 
 
If you would like to advance your schooling in data science, BYU-Idaho's data science degree would help make you competitive. Depending on the graduate program, you may still need to complete some prerequisite classes. You can find a list of data science graduate programs at discoverdatascience.org.

You can find a job with a BS in data science. You do not need to go to graduate school to obtain quality employment in the field of analytics.