is SDN and security becoming more data driven?

first off for relevance I am @battlenerd on Twitter or iNetwork.

I am looking to get some input on a major choice I have been considering which is changing my major from networking and security to data science and data management.

So one of my main questions here is is networking and security innovation being driven by data science and will the python and automation skills offered in this program also be a value add to my networking and security skill set?

https://www.wgu.edu/online-degree-programs.html

Program guide;

https://www.wgu.edu/content/dam/western-governors/documents/programguides/2017-guides/it/BSDMDA.pdf

Main programs of interest;

Applied Probability and Statistics Applied Probability and Statistics is designed to help students develop competence in the fundamental concepts of basic statistics including: introductory algebra and graphing; descriptive statistics; regression and correlation; and probability. Statistical data and probability are often used in everyday life, science, business, information technology, and educational settings to make informed decisions about the validity of studies and the effect of data on decisions. This course discusses what constitutes sound research design and how to appropriately model phenomena using statistical data. Additionally, the content covers simple probability calculations, based on events that occur in the business and IT industries. No prerequisites are required for this course.

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Introduction to Programming in Python Introduction to Programming in Python provides the fundamentals of the Python language and its features to control program flow and to manipulate data sets. This course teaches how to develop Python scripts that extract and manipulate data from unstructured data sources. Python libraries including acquisition and configuration are also covered. Scripting and Programming Foundations and Web Development Foundations are prerequisites to this course.

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Scripting and Programming - Applications for undergraduates explores the various aspects of the C++ programming language by examining its syntax, the development environment, and tools and techniques to solve some real-world problems

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Introduction to Data Science This Introduction to Data Science course introduces the data analysis process and common statistical techniques necessary for the analysis of data. Students will ask questions that can be solved with a given data set, set up experiments, use statistics and data wrangling to test hypotheses, find ways to speed up their data analysis code, make their data set easier to access, and communicate their findings.

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Machine Learning This course presents the end-to-end process of investigating data through a machine learning lens. Topics covered include: techniques for extracting data, identifying useful features that best represent data, a survey of commonly-used machine learning algorithms, and methods for evaluating the performance of machine learning algorithms.

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Data Structures and Algorithms I Data Structures and Algorithms I covers the fundamentals of dynamic data structures, such as bags, lists, stacks, queues, trees, hash tables, and their associated algorithms. With Python software as the basis, the course discusses object-oriented design and abstract data types as a design paradigm. The course emphasizes problem solving and techniques for designing efficient, maintainable software applications. Students will implement simple applications using the techniques learned. This course has one prerequisite: Software 1.

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Thanks for reading and replying!