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Data Science

Master of Science

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Overview

The online Master of Science in Data Science program at CSP Global teaches you how to solve complex challenges with data methodologies, preparing you to contribute to a variety of industries. Throughout the course of our affordable graduate program, you will be equipped with a comprehensive set of skills to work effectively with data, learning how to extract valuable insights and make data-driven decisions to drive predictable outcomes.

Program
Details

Program Length

30 Credits

Location

Online

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  • Career-Centered Program: The online data science master’s program prepares you to make data-driven decisions that drive results in a variety of industries.
  • Designed for Busy Professionals: Complete your master’s in data science fully online and in as few as 12 months while you continue to build your career.
  • Programming Skills: Develop strong programming skills and a solid understanding of data engineering concepts.
  • In-Demand Expertise: Gain essential skills and knowledge, including data engineering, machine learning, data visualization, storytelling, big data technologies, experimental design, and ethical data practices.
$ 600

Cost per credit – CSP Global’s technology programs are valuable and affordable.

Jumpstart Your Future

Admissions Requirements

Career Paths

Market Research Analyst $ 68,230

2022 Median Income

Financial Risk Specialist $ 102,120

2022 Median Income

Data Scientist $ 103,500

2022 Median Income

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Masters/Doctoral RFI

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TUITION & FINANCIAL AID

  • Most affordable private university in MN
  • $600 per credit hour
  • Flexible payment options
  • $2,000 partnership scholarships available
  • Transfer up to 15 credit hours

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ADMISSIONS REQUIREMENTS

  • Complete a free application
  • Submit official transcripts
  • Completed degree in computing, engineering, mathematics, statistics, operations research, information technology, or a related field. OR completed degree with 2 years of relevant work experience in data science.

Full Requirements

Courses

  • This course provides a comprehensive exploration of Enterprise Architecture (EA), focusing on strategic planning and technical research. It equips students with the skills to design, implement, and manage architectures that align an organization’s business strategy, processes, information systems, and technology infrastructure to achieve success.

  • The CSP Project Management, Systems Development & Risk course offers an integrated study of project management principles, systems development methodologies, and risk management practices within the context of complex global IT projects. This course is designed for students and professionals seeking a comprehensive understanding of how to effectively manage projects, develop robust IT systems, lead diverse multi-disciplined teams and proactively address project risks for successful project delivery.

  • The CSP Database Systems course offers a comprehensive study of the principles, design methodologies, and practical applications of database management systems to support the enterprise of the future (DBMS). This course is designed for students and professionals interested in understanding the core concepts of database systems and developing the skills to design, implement, and manage databases and datasets for various applications.

  • The CSP Artificial Intelligence and High-Performance Computing and Ethical Considerations course offers an integrated study of the principles, techniques, ethical considerations and applications of artificial intelligence (AI) in combination with high-performance computing (HPC). This course is designed for students and professionals interested in harnessing the power of advanced computing technologies to develop and deploy AI solutions that can process vast amounts of data and solve complex problems at scale.

  • The CSP Cloud Architecture and Infrastructure course provides a comprehensive study of cloud computing principles, design methodologies, and best practices for architecting scalable and reliable cloud-based, hybrid, and multi-cloud solutions assisting global organizations design and build the architectures and infrastructures of the future. This course is designed for students and professionals interested in understanding how to design, deploy, and manage cloud infrastructure to meet the demands of modern global enterprises utilizing advanced applications and services.

  • This course offers an in-depth study of the principles, algorithms, and applications of machine learning and AI technologies. By the end of the course, you will possess the knowledge and skills to design, implement, and deploy machine learning and AI solutions to address complex challenges across various industries.

  • This course provides an in-depth exploration of the essential tools, software, and programming languages used in the field of data science. Become equipped to efficiently handle data-driven projects and apply data science techniques in a variety of domains, such as business analytics, research, healthcare, and finance.

  • Engage in a comprehensive study of the principles, methodologies, and applications of NLP, a branch of artificial intelligence that focuses on the interaction between computers and human language. Learn the skills you need to design, implement, and evaluate NLP solutions in a wide range of applications, such as virtual assistance, sentiment analysis, machine translation, and information retrieval.

  • This course provides a comprehensive study of the principles, practices, and tools related to DataOps, a methodology that combines the principles of DevOps with data management to enable efficient, Agile business processes. Learn to implement DataOps practices to optimize data processes, improve data collaboration, and accelerate data delivery.

  • Explore the principles, algorithms, and applications of reinforcement learning, a subfield of artificial intelligence focused on training agents to make decisions in dynamic and uncertain environments. By the end of the course, you will possess a solid understanding of reinforcement learning principles, algorithms, and applications.

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