Curriculum Overview

Making sense of vast amounts of information is a critical skill that employers look for in today's data-driven environment. The most recent methods and tools for data analysis, machine learning, and data visualization are all covered in our program. You'll receive instruction from knowledgeable professors who are enthusiastic about giving you a top-notch education and putting you in a position to succeed in the quickly expanding profession of data science. Don't miss this chance to enhance your skills and advance your career with our one-of-a-kind data science program.

  • 2 BSC's graduate program can be completed in two years
  • 12 Students will complete 12 courses in an asynchronous, hybrid format
  • 2,100 Tuition is $2,100 per course

Currently, the MSDS program is 12 courses, which can be completed in two years. All students are required to take two courses in their first term. As a part of the curriculum, you will also attend an immersion experience on BSC’s campus.


All students will complete the following courses.

  • Introduction to the tools and techniques used in data science. Hands-on activities will include how to manipulate and represent data, and methods to analyze data. Prerequisite: undergraduate course in probability or statistics; prior programming experience preferred.

  • An introduction to different types of quantitative research methods and statistical techniques for analyzing data, with a focus on application to real-world data problems. The course begins with a focus on measurement, inferential statistics, and causal inference. Further topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, and logistic regression. Prerequisite: undergraduate course in statistics.

  • An examination of strategies for visualizing data. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, and drive decisions. Exercises throughout the course provide a hands-on experience using relevant, state-of-the-art programming libraries and software tools to apply concepts learned.

  • An exploration of ethical issues in computing, particularly regarding data analytics, and considers the consequences of increased artificial intelligence and data analysis integration into our world. Students will define systems for decision support and predictive analysis, and their effect on privacy and other ethical issues.

  • Provides a broad introduction to key concepts in machine learning. Emphasis will be on intuition and practical examples, though some experience with probability, statistics, and linear algebra will be important. Students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

  • Introduces students to analytical technologies and the fundamental methods, techniques, and software used to design and develop artificially intelligent systems. This course is focused on Artificial Intelligence (AI) as it relates to data science models and predictability.

  • Presents the breadth of data storage solutions. The content spans from traditional databases and business warehouse architectures to streaming analytics solutions and graph processing. Students will consider both small and large datasets as both are equally important and justify different trade-offs.

  • Presents methods and tools for working with large datasets. Consideration of the core concepts behind Big Data problems, applications, and systems and introduces Big Data frameworks, including Hadoop, Spark, and Amazon Web Services (AWS). Topics may also include GPU programming, feature hashing, and the map-reduce framework.

  • Application of skills learned throughout the data science program to specific industries (financial, business, healthcare, etc.). Students will work on several large projects throughout the course focusing on one industry at a time.

  • Explains how blockchain technologies work and how they can be applied to various industries to better understand their implications and innovative potential. The relationship between blockchain technology, AI, and Internet of Things will be critically analyzed.

  • Introduces linguistic phenomena and our attempts to analyze them with machine learning. The course will cover a wide range of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

  • Finalizes the data science experience with one large, term-long group project. Students will work in small groups to complete a data analysis project incorporating skills learned throughout the program.

Immersive Weekends

All courses are delivered in a hybrid format. Instruction includes both asynchronous, online work as well as one required in-person immersive weekend on the Birmingham-Southern College campus in Birmingham, Alabama.

Students have the option to stay in on campus housing during these in-person weekend experiences. Travel expenses for the on-campus experience are not included, however we have created a nominal cost for those students who want to have meals and housing provided during these immersive weekends.


Carla Rego

Carla Rego

Carla Rego is an Assistant Lecturer of Applied Computer Science at Birmingham-Southern College. Prior to her role at BSC, she held various positions at the University of Mississippi, most notably as a Systems Analyst and Software Developer in the University's Academic Computing Division and Graduate Instructor of Computer Science in the Department of Computer and Information Science. Dr. Rego's educational background includes a Ph.D. in Engineering Science from the University of Mississippi along with a Bachelor of Science in Computer Science and Education and a Master of Arts in Education Administration from Portucalense University in Portugal. She brings a wealth of expertise in machine learning, data mining, high-performance computing, and the ethical side of data science and AI to her role as an educator and researcher.

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