The BS in Data Science degree does have a solid math background and will require advanced classes in mathematics, such as calculus II and linear algebra. In addition, various topics in calculus III, probability and statistics will be covered in data science core courses.

Students in the data science program want to become proficient and ethical producers, evaluators and consumers of data. They are typically interested in the following activities (covered in the data science core classes):

• Using programming languages (such as R and Python) in the context of data manipulation, data visualization and exploratory data analysis
• Using mathematical or statistical modeling to make predictions on world phenomena (such as housing prices based on input data)
• Understanding the relationship between key data points
• Classification of data (such as emails – spam or not spam; image recognition, etc.)
• Creating dynamic reports, interactive plots, web apps, slides and animations

Admission requirements

The BS in Data Science is offered by The College of Liberal Arts and Sciences.

All students admitted into the university are eligible to pursue this degree, with some exceptions:

• Previous degree offered by the SoMSS

• No concurrent degree students already in SoMSS (not currently an issue for ASU-Online)

Misconceptions

• This is not a pure statistics or business analytics degree.



• This is not just about observing data, but also utilizing it to make predictions through mathematical/statistical modeling.

  Reporting baseball statistics is not data science.

  Making predictions utilizing historical trends with large data sets (training data) is more in line with data science than it is reporting sports statistics.


• Plotting and visualizing data is an important part of data science. It helps us better understand the data, the relations between variables, make initial assumptions (which are later to be tested), as well as make first conclusions. This is a part of so called Exploratory Data Analysis (EDA), Data Analytics, etc. Our course DAT 301 Exploring Data in R and Python is devoted to EDA. However, this is just one aspect of data science, an initial one. The ultimate goal of data science is to analyze collected data for the purpose of making predictions in future. For that, we need mathematical and statistical models, often involving intensive programming. So, a data scientist should have a good background in mathematics and statistics, as well as good programming skills.